#4 by GitHub stars
NousResearch/hermes-agent
The agent that grows with you
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
View weekly changes
optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/antigravity-cli/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/blackbox/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/dynamic-workflow/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/grok/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/honcho/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/opencode/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/autonomous-ai-agents/openhands/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/blockchain/evm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/blockchain/hyperliquid/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/blockchain/solana/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/communication/one-three-one-rule/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/ai-presenter-video/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/anatomy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/archify/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/ascii-art/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/audiocraft-audio-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −569 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/auteur/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/baoyu-article-illustrator/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/baoyu-comic/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/brag/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/brag-slim/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/comfyui/SKILL.md
Week of Nov 2, 2026 · +0 / −1 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/concept-diagrams/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/creative-ideation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/design-md/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/creative/draw-your-font/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/dream-loop/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/excalidraw/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/heartmula/SKILL.md
Week of Nov 2, 2026 · +1 / −1 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/hyperframes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/impeccable/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/ip-as-logo/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/kanban-video-orchestrator/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/meme-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/mono-color/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/pixel-art/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/pretext/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/simple-english/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/sketch/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/social-media-content-calendar/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/system-atlas/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/tldraw-offline/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/creative/unreal-mcp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/data-science/jupyter-notebook/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/actual-setup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/docker-management/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/hermes-s6-container-supervision/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/inference-sh-cli/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/pinggy-tunnel/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/setup-wizard-generator/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/devops/watchers/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/dogfood/adversarial-ux-test/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/email/agentmail/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/3-statement-model/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/comps-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/dcf-model/SKILL.md
Week of Nov 2, 2026 · +9 / −9 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/excel-author/SKILL.md
Week of Nov 2, 2026 · +34 / −3 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/lbo-model/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/merger-model/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/polymarket/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/pptx-author/SKILL.md
Week of Nov 2, 2026 · +37 / −6 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/finance/stocks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/gaming/minecraft-modpack-server/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/gaming/pokemon-player/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/health/fitness-nutrition/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/health/neuroskill-bci/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mcp/fastmcp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mcp/mcp-oauth-remote-gateway/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mcp/mcporter/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/media/gif-search/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/media/songsee/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/mlops/accelerate/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/chroma/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/clip/SKILL.md
Week of Nov 2, 2026 · +0 / −257 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/evaluation/evaluating-llms-harness/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/evaluation/weights-and-biases/SKILL.md
Week of Nov 2, 2026 · +0 / −598 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/faiss/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/flash-attention/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/guidance/SKILL.md
Week of Nov 2, 2026 · +0 / −580 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/huggingface-tokenizers/SKILL.md
Week of Nov 2, 2026 · +0 / −520 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/inference/llama-cpp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/inference/outlines/SKILL.md
Week of Nov 2, 2026 · +0 / −662 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/inference/serving-llms-vllm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/instructor/SKILL.md
Week of Nov 2, 2026 · +0 / −744 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/lambda-labs/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/llava/SKILL.md
Week of Nov 2, 2026 · +0 / −308 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/modal/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/models/huggingface-hub/SKILL.md
Week of Nov 2, 2026 · +0 / −81 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/models/segment-anything-model/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/nemo-curator/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/obliteratus/SKILL.md
Week of Nov 2, 2026 · +1 / −1 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/peft/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/pinecone/SKILL.md
Week of Nov 2, 2026 · +0 / −381 lines
Before Instructions only → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/pytorch-fsdp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/pytorch-lightning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/qdrant/SKILL.md
Week of Nov 2, 2026 · +0 / −505 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/research/dspy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/saelens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/simpo/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/slime/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/stable-diffusion/SKILL.md
Week of Nov 2, 2026 · +0 / −523 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/tensorrt-llm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/torchtitan/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/training/axolotl/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/training/trl-fine-tuning/SKILL.md
Week of Nov 2, 2026 · +0 / −498 lines
Before Local scripts → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/training/unsloth/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/mlops/whisper/SKILL.md
Week of Nov 2, 2026 · +0 / −321 lines
Before Network or broad shell → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/payments/mpp-agent/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/payments/stripe-link-cli/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/payments/stripe-projects/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/airtable/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/productivity/box/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/productivity/canvas/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/decision-questionnaire/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/here-now/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/live-dashboard/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/memento-flashcards/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/notion/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/productivity/property-listings/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/shop/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/shopify/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/siyuan/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/productivity/telephony/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/bioinformatics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/blogwatcher/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/darwinian-evolver/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/domain-intel/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/drug-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/duckduckgo-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/gitnexus-explorer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/llm-wiki/SKILL.md
Week of Nov 2, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → e8639fd4312e
No earlier version was observed for comparison.
optional-skills/research/osint-investigation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/parallel-cli/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/pinecone-research/SKILL.md
Week of Nov 2, 2026 · +0 / −108 lines
Before Local scripts → After Not checked yet
0b8bf3bdab6e → e8639fd4312e
SKILL.md was removed.
This preview is limited. See the complete change on GitHub.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/qmd/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/research-paper-writing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/rss-feeds/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/scrapling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/research/searxng-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/security/1password/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Network or broad shell
0b8bf3bdab6e → e8639fd4312e
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → 0b8bf3bdab6e
No earlier version was observed for comparison.
optional-skills/security/godmode/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
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optional-skills/security/oss-forensics/SKILL.md
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optional-skills/security/sherlock/SKILL.md
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optional-skills/security/unbroker/SKILL.md
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optional-skills/security/web-pentest/SKILL.md
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optional-skills/smart-home/openhue/SKILL.md
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optional-skills/social-media/reddit-reading/SKILL.md
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optional-skills/software-development/ast-grep/SKILL.md
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optional-skills/software-development/code-wiki/SKILL.md
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optional-skills/software-development/grill-me/SKILL.md
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optional-skills/software-development/hermes-agent-skill-authoring/SKILL.md
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optional-skills/software-development/inspecting-hermes-desktop-dom/SKILL.md
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optional-skills/software-development/node-inspect-debugger/SKILL.md
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optional-skills/software-development/pr-lens/SKILL.md
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optional-skills/software-development/python-debugpy/SKILL.md
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optional-skills/software-development/rest-graphql-debug/SKILL.md
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optional-skills/software-development/subagent-driven-development/SKILL.md
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optional-skills/web-development/cloudflare-temporary-deploy/SKILL.md
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optional-skills/web-development/har-derived-api-client/SKILL.md
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optional-skills/web-development/page-agent/SKILL.md
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optional-skills/web-development/publish-site/SKILL.md
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optional-skills/web-development/scrollcraft/SKILL.md
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optional-skills/yuanbao/SKILL.md
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plugins/google_meet/SKILL.md
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skills/apple/apple-notes/SKILL.md
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skills/apple/apple-reminders/SKILL.md
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skills/apple/findmy/SKILL.md
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skills/apple/imessage/SKILL.md
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skills/autonomous-ai-agents/claude-code/SKILL.md
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skills/autonomous-ai-agents/codex/SKILL.md
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skills/autonomous-ai-agents/computer-use/SKILL.md
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skills/autonomous-ai-agents/hermes-agent/SKILL.md
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skills/autonomous-ai-agents/opencode/SKILL.md
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skills/creative/architecture-diagram/SKILL.md
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skills/creative/ascii-video/SKILL.md
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skills/creative/baoyu-infographic/SKILL.md
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skills/creative/claude-design/SKILL.md
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skills/creative/design-md/SKILL.md
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skills/creative/humanizer/SKILL.md
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skills/creative/manim-video/SKILL.md
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skills/creative/p5js/SKILL.md
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skills/creative/popular-web-designs/SKILL.md
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skills/creative/songwriting-and-ai-music/SKILL.md
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skills/devops/sdlc-review/SKILL.md
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skills/email/email-inbox-triage/SKILL.md
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skills/email/himalaya/SKILL.md
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skills/media/gif-search/SKILL.md
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skills/media/songsee/SKILL.md
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skills/media/youtube-content/SKILL.md
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skills/note-taking/obsidian/SKILL.md
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skills/productivity/airtable/SKILL.md
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- 1 ---
- 2 name: airtable
- 3 description: Airtable REST API via curl. Records CRUD, filters, upserts.
- 4 version: 1.1.0
- 5 author: community
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 prerequisites:
- 9 env_vars: [AIRTABLE_API_KEY]
- 10 commands: [curl]
- 11 metadata:
- 12 hermes:
- 13 tags: [Airtable, Productivity, Database, API]
- 14 homepage: https://airtable.com/developers/web/api/introduction
- 15 ---
- 16
- 17 # Airtable — Bases, Tables & Records
- 18
- 19 Work with Airtable's REST API directly via `curl` using the `terminal` tool. No MCP server, no OAuth flow, no Python SDK — just `curl` and a personal access token.
- 20
- 21 ## Prerequisites
- 22
- 23 1. Create a **Personal Access Token (PAT)** at https://airtable.com/create/tokens (tokens start with `pat...`).
- 24 2. Grant these scopes (minimum):
- 25 - `data.records:read` — read rows
- 26 - `data.records:write` — create / update / delete rows
- 27 - `schema.bases:read` — list bases and tables
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skills/productivity/box/SKILL.md
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- 1 ---
- 2 name: box
- 3 description: Box manages cloud files, sharing, search, and metadata.
- 4 version: 1.0.0
- 5 author: Chris Kim (iskysun96), Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 prerequisites:
- 9 commands: [box]
- 10 metadata:
- 11 hermes:
- 12 tags: [Box, Productivity, Cloud Storage, Collaboration, Metadata, Content Extraction, CLI, SDK]
- 13 related_skills: [google-workspace]
- 14 homepage: https://developer.box.com/
- 15 ---
- 16
- 17 # Box
- 18
- 19 Use Box as the cloud file system for file operations, collaboration, metadata, and document work. Run operations with Hermes' `terminal` tool and use the Box CLI; use the SDK guide when building an application.
- 20
- 21 ## When to Use
- 22
- 23 - Organizing, uploading, versioning, moving, sharing, or collaborating on Box files and folders
- 24 - Searching Box content or existing metadata
- 25 - Asking questions about Box files, extracting metadata, or generating text grounded in a file
- 26 - Processing a Box folder at scale without downloading every source file
- 27 - Building a Box-backed application, integration, or webhook handler
- 28
- 29 ## Start broad file-system conversations
- 30
- 31 When someone is exploring a cloud file system for Hermes, first give a short fit assessment: Box is useful when a team needs cloud file storage, sharing, search, metadata, and document work. Then ask whether they want to connect a Box account with OAuth or build a Box-backed application or integration with an SDK.
- 32
- 33 OAuth makes Hermes act as the Box account authorized in the browser. That account's Box permissions determine what Hermes can access. To give Hermes narrower access, authorize an account that is invited only to the required files, folders, or Hubs.
- 34
- 35 Do not run setup, show a command cookbook, propose account plans or folder taxonomies, or load every reference for a broad exploratory question. Wait for the user's answer, then load only the relevant path. When a request already names a concrete outcome, skip this discovery step and handle that outcome directly.
- 36
- 37 Start normal CLI work with the official Box CLI OAuth app. It covers ordinary content work and Box AI. Use a custom **User Authentication (OAuth 2.0)** Platform App only when the requested operation needs an additional OAuth scope, such as webhook management. This remains an OAuth flow; do not substitute a server-side or impersonation identity.
- 38
- 39 ## Perform chosen setup interactively
- 40
- 41 When a user selects an authentication path or asks Hermes to connect Box, perform the setup through `terminal`; do not turn the next response into instructions for the user to copy. Take the next safe action yourself, and pause only for an approval, browser sign-in, administrator action, or secret that Hermes cannot safely supply.
- 42
- 43 - If `box` is missing, ask for any terminal approval required to install `@box/cli` under the current Hermes home at `tools/box-cli`; then verify it with the shell-appropriate command in [CLI guide](references/cli-guide.md). Do not attempt a global npm install, use `sudo`, change npm's global prefix, or change `PATH`.
- 44 - Before OAuth, ask: **“Is Hermes running on the same computer as the browser you will use to authorize Box, or on a remote host such as a VPS, container, or cloud VM?”** Use normal `box login` only for the same-computer path. Use `box login --code` only for the remote/headless path. Do not infer runtime topology from the operating system alone; read [OAuth setup](references/oauth-setup.md) after the user answers.
- 45 - Before starting browser authorization, state that Hermes will act as the Box account signed in there. If the user wants narrower access, they can authorize an account that is invited only to the required files, folders, or Hubs. Do not make that account an administrator to unlock an exceptional operation.
- 46 - If a custom OAuth Platform App is necessary, use the CLI's interactive Platform App flow. Ask the user to enter its client secret only in the local CLI prompt; never request it in chat, write it to Hermes configuration, or commit it.
- 47 - If an install, browser authorization, environment switch, or permission change needs approval, request that approval and resume the setup after it is granted. Do not replace the action with a command list.
- 48
- 49 ## Start each task
- 50
- 51 1. Confirm the CLI and current actor. Probe with `command -v box` on POSIX shells or `Get-Command box -ErrorAction SilentlyContinue` in PowerShell. If `box` is on `PATH`, use it. If Hermes installed the CLI under its current home, use the shell-appropriate verified runner in [CLI guide](references/cli-guide.md) in place of every leading `box`. Then run `box users:get me --json --fields id,name,login` with that runner.
- 52 If this succeeds, record the actor and continue. Do not ask about authentication again. Treat `folders:items 0` only as a listing of the actor's root; it is not proof that a shared file, folder, or Hub is inaccessible. For a known file or folder, verify its ID directly; for a Hub, use the Hubs discovery path in [Box Hubs](references/hubs.md).
- 53 2. If authentication is absent, ask to connect a Box account with OAuth, then ask whether Hermes and the authorization browser run on the same computer or on separate hosts. Read [OAuth setup](references/oauth-setup.md).
- 54 3. Read the relevant reference before operating. Use documented commands first; only run subcommand help when the request needs an option not covered by the reference or the installed CLI rejects the documented form.
- 55
- 56 Examples labeled `bash` use POSIX continuation syntax. In PowerShell, run the Box command on one line or replace each trailing `\` with PowerShell's backtick continuation. Do not paste POSIX variable assignments into PowerShell.
- 57
- 58 ## Extend the CLI without pausing
- 59
- 60 When the Box CLI lacks a dedicated subcommand, use `box request` for the matching REST endpoint and continue the ordinary operation. Do not ask the user to choose merely because the implementation uses REST; it is the same Box task and preserves the configured CLI identity. Read [REST API fallback](references/rest-api.md) when the endpoint needs a request body or custom header.
- 61
- 62 Ask before a delete, a collaboration/shared-link or permission change, an identity change, a broad or costly batch mutation, or when the target or scope is ambiguous. Otherwise perform the requested operation and verify it.
- 63
- 64 ## Choose the right path
- 65
- 66 | Need | Read |
- 67 | --- | --- |
- 68 | CLI conventions, environments, JSON, or REST escape hatch | [CLI guide](references/cli-guide.md) |
- 69 | Files, folders, versions, links, or collaborations | [Content workflows](references/content-workflows.md) |
- 70 | Search, metadata, Box AI, or AI units | [Search and AI](references/search-and-ai.md) |
- 71 | Curated large-scale Q&A or a reusable knowledge base | [Box Hubs](references/hubs.md) |
- 72 | Many files or a resumable batch | [Bulk operations](references/bulk-operations.md) |
- 73 | Application code or a Box SDK | [SDK development](references/sdk-development.md) |
- 74 | Webhooks or Events API | [Webhooks and events](references/webhooks-and-events.md) |
- 75 | CLI unavailable or a missing CLI operation | [REST API fallback](references/rest-api.md) |
- 76 | Auth, permissions, rate limits, or API errors | [Troubleshooting](references/troubleshooting.md) |
- 77
- 78 ## Content handling policy
- 79
- 80 For semantic analysis of Box-hosted content, prefer Box AI: it preserves Box permissions, processes source files through Box's governed AI integration, keeps source-file bodies out of Hermes' coding-model context, and scales document work without downloading every file. Do not criticize or block another workflow; use it when the user explicitly chooses it.
- 81
- 82 Use existing Box metadata or metadata queries for deterministic lookups. Otherwise use Box AI:
- 83
- 84 - `ai:ask` for Q&A, summaries, and comparisons
- 85 - `ai:extract-structured` for known fields or metadata templates
- 86 - `ai:extract` for flexible key-value extraction
- 87 - `ai:text-gen` for writing grounded in one Box file
- 88
- 89 For Q&A over more than 25 files or a reusable curated knowledge base, prefer Box AI for Hubs. Discover an existing accessible Hub first; only create or populate one after the user approves the shared-resource change. If no Hub is available and the user does not want one created, narrow a one-off request with search or metadata. Do not use a Hub for metadata extraction or text generation. Read [Box Hubs](references/hubs.md).
- 90
- 91 When the user asks to extract metadata from a Box file, treat it as a request to persist the result unless they ask for a preview. Use structured extraction with inline fields when the desired schema is known and freeform extraction when the fields are exploratory. Reuse a compatible existing enterprise template when one represents every requested field. Otherwise store flat scalar results in the built-in `global.properties` metadata instance, or upload a JSON sidecar beside the source file when the result contains nested objects, tables, or values that must retain their types. Read every write back and compare it with the intended result. Never silently substitute a file description, attach a partial or unrelated template, truncate fields, or discard fields.
- 92
- 93 Do not create or change metadata templates. Box does not permit creation of global templates, and enterprise-template administration is outside Hermes' normal OAuth content workflow. If the user needs reusable typed enterprise metadata and no compatible template exists, explain that a Box Admin or authorized Co-Admin must create it separately, leave existing structured metadata unchanged, and report the persisted `global.properties` instance or JSON sidecar instead. Read [Search and AI](references/search-and-ai.md) for the complete extraction and writeback workflow.
- 94
- 95 Before the first Box AI request, state that Box AI must be enabled, consumes AI units, and remains limited to the current actor's permissions; do not wait for acknowledgement. An AI response returned to Hermes can still contain sensitive information. Confirm only when a material batch's file scope or expected AI-unit use is ambiguous, or when the user has not explicitly requested that scale. See [Search and AI](references/search-and-ai.md).
- 96
- 97 ## Operate safely
- 98
- 99 - Prefer IDs to paths and verify the current actor before diagnosing a missing file.
- 100 - Use `--json` and `--fields` to keep output small. For mutations, inventory first, confirm ambiguous or large scope, then read back the result.
- 101 - Run ordered CLI mutations serially so progress and recovery are unambiguous. Use documented bulk input support or bounded SDK concurrency for scalable work.
- 102 - Do not create a shared link merely to provide navigation. Shared links change access and require explicit confirmation.
- 103 - Do not put secrets in chat, command output, source control, or logs.
- 104
- 105 ## Report results
- 106
- 107 For every individually reported Box item, include its ID and a clickable navigation link:
- 108
- 109 - File: `https://app.box.com/file/<FILE_ID>`
- 110 - Folder: `https://app.box.com/folder/<FOLDER_ID>`
- 111 - Hub: `https://app.box.com/hubs/<HUB_ID>`
- 112
- 113 For large batches, link the source and destination folders plus exceptions instead of listing hundreds of items. A human may not be able to open content that is only visible to the connected Box account; state that clearly. Include the actor and verification performed in every write summary.
- 114
- 115 ## Verify
- 116
- 117 After any write, fetch the file or folder with the same actor or list its parent and confirm the returned ID and name. For a metadata write, retrieve the metadata instance and compare every returned field with the intended value; an HTTP success alone is not verification. Report missing, normalized, or rejected values. For a disposable setup check, create a smoke folder, verify it, then delete it only if the user authorized cleanup.
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skills/productivity/document-to-action-items/SKILL.md
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- 11 related_skills: [pdf, pdf, docx, notion]
+ 11 related_skills: [pdf, microsoft-office, notion]
- 16 Turn documents into cited facts and proposed actions. Extraction is not legal advice, and low-confidence OCR or ambiguous language must remain visible. The `pdf` / `pdf` / `docx` skills own extraction mechanics; this skill owns what happens to the extracted content.
+ 16 Turn documents into cited facts and proposed actions. Extraction is not legal advice, and low-confidence OCR or ambiguous language must remain visible. The `pdf` and `microsoft-office` skills own extraction mechanics; this skill owns what happens to the extracted content.
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skills/productivity/docx/SKILL.md
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- 1 ---
- 2 name: docx
- 3 description: Create, read, edit, template, and review Word .docx files.
- 4 version: 1.1.0
- 5 author: Nous Research
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [word, docx, documents, office, templates, revisions, comments]
- 11 category: productivity
- 12 related_skills: [pdf, xlsx, powerpoint]
- 13 ---
- 14
- 15 # Docx Skill
- 16
- 17 Create, read, edit, and template Microsoft Word `.docx` files with
- 18 python-docx via small CLIs. It handles text, styles, lists, tables,
- 19 images, headers/footers, `{{token}}` templating, tracked changes
- 20 (list/accept/reject), comments (list/add/delete), TOC and page-number
- 21 fields, and package health checks. It does not render documents itself
- 22 (PDF needs LibreOffice — see Converting to PDF) or edit legacy `.doc`.
- 23
- 24 ## When to Use
- 25
- 26 - The user asks to generate a Word document (report, letter, contract).
- 27 - You need the text, outline, styles, or embedded images of a `.docx`.
- 28 - You must change an existing `.docx`: replace text, edit table cells,
- 29 insert/delete paragraphs, apply styles, merge fragmented runs.
- 30 - You have a `.docx` template with `{{placeholders}}` to fill from data.
- 31 - The document has tracked changes to review, accept, or reject.
- 32 - You need to read reviewers' comments, or add/delete comments.
- 33 - A `.docx` won't open or behaves oddly and you need corruption triage.
- 34 - The document needs a table of contents or "Page X of Y" footers.
- 35 - Not for: `.doc` (legacy), `.odt`, or WYSIWYG layout work.
- 36
- 37 ## Prerequisites
- 38
- 39 - Python 3.10+ with `python-docx` installed:
- 40 `pip install python-docx` (import name is `docx`; lxml comes with it).
- 41 - Comments `add` uses the native API on python-docx >= 1.2 and an XML
- 42 fallback on older versions — both are automatic.
- 43 - For image blocks: the image files must exist locally (PNG/JPEG).
- 44
- 45 ## How to Run
- 46
- 47 All helpers live in `scripts/` next to this file. Run them with the
- 48 `terminal` tool; each supports `--help` and prints JSON to stdout.
- 49
- 50 ```bash
- 51 python scripts/docx_create.py spec.json out.docx
- 52 python scripts/docx_read.py out.docx --text
- 53 python scripts/docx_edit.py replace out.docx --find old --replace new
- 54 python scripts/docx_template.py tpl.docx values.json filled.docx
- 55 python scripts/docx_revisions.py list out.docx
- 56 python scripts/docx_comments.py list out.docx
- 57 python scripts/docx_validate.py out.docx
- 58 ```
- 59
- 60 ## Quick Reference
- 61
- 62 | Task | Command |
- 63 | --- | --- |
- 64 | Create from JSON spec | `docx_create.py spec.json out.docx` |
- 65 | Full text (body+tables+headers/footers) | `docx_read.py f.docx --text` |
- 66 | Heading outline + table shapes | `docx_read.py f.docx --structure` |
- 67 | Styles actually used | `docx_read.py f.docx --styles` |
- 68 | Extract embedded images | `docx_read.py f.docx --images outdir/` |
- 69 | Detect tracked changes/comments | `docx_read.py f.docx --revisions` |
- 70 | Find/replace (formatting kept) | `docx_edit.py replace f.docx --find A --replace B -o out.docx` |
- 71 | Set a table cell | `docx_edit.py set-cell f.docx --table 0 --row 1 --col 2 --text X` |
- 72 | Insert paragraph before index N | `docx_edit.py insert f.docx --index N --text X --style Normal` |
- 73 | Delete paragraph N | `docx_edit.py delete f.docx --index N` |
- 74 | Apply style to paragraph N | `docx_edit.py style f.docx --index N --style "Heading 1"` |
- 75 | Merge equal-format adjacent runs | `docx_edit.py normalize f.docx -o out.docx` |
- 76 | Insert TOC field before para N | `docx_edit.py toc f.docx --index N -o out.docx` |
- 77 | "Page X of Y" footer fields | `docx_edit.py page-numbers f.docx` |
- 78 | Fill `{{tokens}}` | `docx_template.py tpl.docx values.json out.docx --strict` |
- 79 | List revisions (id/author/date/text) | `docx_revisions.py list f.docx` |
- 80 | Accept / reject all revisions | `docx_revisions.py accept-all f.docx -o out.docx` (or `reject-all`) |
- 81 | Accept / reject one revision | `docx_revisions.py accept f.docx --id 3 -o out.docx` |
- 82 | List comments (+anchored text) | `docx_comments.py list f.docx` |
- 83 | Add comment anchored to text | `docx_comments.py add f.docx --target "phrase" --text "note" --author You` |
- 84 | Delete comment by id | `docx_comments.py delete f.docx --id 0` |
- 85 | Health-check the package | `docx_validate.py f.docx` (exit 1 on errors) |
- 86
- 87 ## Procedure
- 88
- 89 1. **Create.** Write a JSON spec with `write_file`, then run
- 90 `scripts/docx_create.py`. The spec supports: `page` (size + margins in
- 91 mm), `header`/`footer` strings, `footer_page_numbers` (adds a
- 92 "Page X of Y" field footer), `styles` (custom paragraph styles with
- 93 font, size, bold/italic, hex `color`), and `blocks` — `heading`
- 94 (level 1-9), `paragraph` (either `text` or a `runs` list where each run
- 95 may set `bold`/`italic`/`underline`), `bullet_list`, `numbered_list`,
- 96 `table` (`header` row rendered bold, `rows`, optional built-in table
- 97 `style` such as `Table Grid`), `image` (`path`, optional `width_mm`),
- 98 `toc` (Table of Contents field), and `page_break`. The full spec
- 99 format is documented at the top of `scripts/docx_create.py`.
- 100 2. **Read.** Use `scripts/docx_read.py` with exactly one mode flag.
- 101 `--text` returns body paragraphs, all table cell text, and
- 102 header/footer text as JSON. `--structure` returns the heading outline
- 103 plus paragraph/table/section counts. `--images DIR` copies every file
- 104 under `word/media/` out of the package.
- 105 3. **Edit.** Use `scripts/docx_edit.py`. `replace` walks body, tables
- 106 (nested included), headers and footers, and preserves run formatting;
- 107 add `--body-only` to skip headers/footers. Pass `-o out.docx` to keep
- 108 the original; omit it to edit in place. Paragraph indices for
- 109 `insert`/`delete`/`style`/`toc` refer to `--structure`/`--text` body
- 110 order. Run `normalize` first on documents that came out of heavy Word
- 111 editing — it merges adjacent runs with identical formatting so later
- 112 find-replace matches reliably.
- 113 4. **Review revisions.** `docx_revisions.py list` reports every `w:ins`
- 114 and `w:del` (id, author, date, affected text) anywhere in body,
- 115 tables, headers, or footers. `accept-all` / `reject-all` resolve them
- 116 in bulk; `accept`/`reject --id N` handles a single revision. Accept
- 117 keeps insertions and drops deleted text; reject does the reverse.
- 118 5. **Comments.** `docx_comments.py list` returns each comment's id,
- 119 author, date, body text, and the document text it is anchored to.
- 120 `add --target "some phrase"` anchors a new comment to the first
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skills/productivity/google-workspace/SKILL.md
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skills/productivity/maps/SKILL.md
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skills/productivity/meeting-action-items/SKILL.md
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skills/productivity/microsoft-office/SKILL.md
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skills/productivity/notion/SKILL.md
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- 1 ---
- 2 name: notion
- 3 description: "Notion API + ntn CLI: pages, databases, markdown, Workers."
- 4 version: 2.0.0
- 5 author: community
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 prerequisites:
- 9 env_vars: [NOTION_API_KEY]
- 10 metadata:
- 11 hermes:
- 12 tags: [Notion, Productivity, Notes, Database, API, CLI, Workers]
- 13 homepage: https://developers.notion.com
- 14 ---
- 15
- 16 # Notion
- 17
- 18 Talk to Notion two ways. Same integration token works for both — pick by what's available.
- 19
- 20 ◆ **`ntn` CLI** — Notion's official CLI. Shorter syntax, one-line file uploads, required for Workers. macOS + Linux only as of May 2026 (Windows support "coming soon"). **Default when installed.**
- 21 ◆ **HTTP + curl** — works everywhere including Windows. **Default fallback** when `ntn` isn't installed.
- 22
- 23 ## Setup
- 24
- 25 ### 1. Get an integration token (required for both paths)
- 26
- 27 1. Create an integration at https://notion.so/my-integrations
- 28 2. Copy the API key (starts with `ntn_` or `secret_`)
- 29 3. Store in `${HERMES_HOME:-~/.hermes}/.env`:
- 30 ```
- 31 [credential-like text hidden]
- 32 ```
- 33 4. **Share target pages/databases with the integration** in Notion: page menu `...` → `Connect to` → your integration name. Without this, the API returns 404 for that page even though it exists.
- 34
- 35 ### 2. Install `ntn` (preferred path on macOS / Linux)
- 36
- 37 ```bash
- 38 # Recommended
- 39 curl -fsSL https://ntn.dev | bash
- 40
- 41 # Or via npm (needs Node 22+, npm 10+)
- 42 npm install --global ntn
- 43
- 44 ntn --version # verify
- 45 ```
- 46
- 47 **Skip `ntn login` — use the integration token instead.** This works headlessly, no browser needed:
- 48 ```bash
- 49 [credential-like text hidden]
- 50 export NOTION_KEYRING=0 # don't try to use the OS keychain
- 51 ```
- 52
- 53 Add those exports to your shell profile (or to `${HERMES_HOME:-~/.hermes}/.env`) so every session inherits them.
- 54
- 55 ### 3. Choose path at runtime
- 56
- 57 ```bash
- 58 if command -v ntn >/dev/null 2>&1; then
- 59 # use ntn
- 60 else
- 61 # fall back to curl
- 62 fi
- 63 ```
- 64
- 65 Windows users: skip step 2 entirely until native `ntn` ships — Path B works fine. If you want CLI ergonomics now, install `ntn` inside WSL2.
- 66
- 67 ## API Basics
- 68
- 69 `Notion-Version: 2025-09-03` is required on all HTTP requests. `ntn` handles this for you. In this version, what users call "databases" are called **data sources** in the API.
- 70
- 71 ## Path A — `ntn` CLI (preferred, macOS / Linux)
- 72
- 73 ### Raw API calls (shorthand for curl)
- 74 ```bash
- 75 ntn api v1/users # GET
- 76 # POST with inline body
- 77 ntn api v1/pages parent[page_id]=abc123 \
- 78 properties[title][0][text][content]="Notes"
- 79 ntn api v1/pages/abc123 -X PATCH archived:=true # PATCH; := is non-string (bool/num/null)
- 80 ```
- 81
- 82 Syntax notes:
- 83 - `key=value` — string fields
- 84 - `key[nested]=value` — nested object fields
- 85 - `key:=value` — typed assignment (booleans, numbers, null, arrays)
- 86
- 87 ### Search
- 88 ```bash
- 89 ntn api v1/search query="page title"
- 90 ```
- 91
- 92 ### Read page metadata
- 93 ```bash
- 94 ntn api v1/pages/{page_id}
- 95 ```
- 96
- 97 ### Read page as Markdown (agent-friendly)
- 98 ```bash
- 99 ntn api v1/pages/{page_id}/markdown
- 100 ```
- 101
- 102 ### Read page content as blocks
- 103 ```bash
- 104 ntn api v1/blocks/{page_id}/children
- 105 ```
- 106
- 107 ### Create page from Markdown
- 108 ```bash
- 109 ntn api v1/pages \
- 110 parent[page_id]=xxx \
- 111 properties[title][0][text][content]="Notes from meeting" \
- 112 markdown="# Agenda
- 113
- 114 - Q3 roadmap
- 115 - Hiring"
- 116 ```
- 117
- 118 ### Patch a page with Markdown
- 119 ```bash
- 120 ntn api v1/pages/{page_id}/markdown -X PATCH \
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skills/productivity/pdf/SKILL.md
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- 12 related_skills: [docx, xlsx, powerpoint]
+ 12 related_skills: [microsoft-office]
- 34 - Python 3.10+ with `pypdf`, `reportlab`, `pdfplumber`:
- 35 `python -m pip install pypdf reportlab pdfplumber`
- 36 - Optional, for page rasterization (`pdf_page_image.py`, overlay rendering): `python -m pip install pypdfium2`, or poppler's `pdftoppm` on PATH. Scripts fall back pypdfium2 → pdftoppm and report `{"rendered": false, "missing": [...]}` (exit 0) when neither exists.
+ 34 - Python 3.10+ with `pypdf`, `reportlab`, `pdfplumber`.
+ 35 - Pick the interpreter with `terminal`. Use `python` when it already imports
+ 36 the libraries (the Hermes Docker image ships them). Otherwise, when `hermes`
+ 37 is on PATH, let Hermes build its shared, pinned documents environment under
+ 38 the Hermes home: it survives container restarts, never touches the Hermes
+ 39 install itself, and is safe to run from several agents at once. When it is
+ 40 not (the terminal runs in a Docker sandbox, which has no Hermes), the block
+ 41 builds a venv in the sandbox home with the same pinned versions. Use this
+ 42 exact block in every document skill so they all share one environment:
+ 43
+ 44 ```bash
+ 45 python -c "import pypdf, reportlab, pdfplumber" 2>/dev/null && PY=python || if command -v hermes >/dev/null 2>&1; then
+ 46 PY="$(hermes pm environment documents --extra documents --extra documents-pdf)"
+ 47 else
+ 48 PY="$HOME/.cache/hermes-docs-venv/bin/python"
+ 49 "$PY" -c "import docx, openpyxl, pptx, reportlab, pypdf, pdfplumber, pypdfium2" 2>/dev/null || (
+ 50 # Safe to run from several agents at once: each caller builds a private venv,
+ 51 # a symlink publishes the first finished one atomically, the others delete theirs;
+ 52 # a published build is replaced and deleted only while it still fails to import.
+ 53 mkdir -p "$HOME/.cache" && cd "$HOME/.cache" && stage="$(mktemp -d hermes-docs-venv.XXXXXX)" || exit 1
+ 54 old="$(readlink hermes-docs-venv)" # the published build, if any (another agent may have just published it)
+ 55 python3 -m venv "$stage" &&
+ 56 "$stage/bin/python" -m pip install -q --disable-pip-version-check python-docx==1.2.0 openpyxl==3.1.5 python-pptx==1.0.2 reportlab==5.1.0 pypdf==6.21.2 pdfplumber==0.11.10 pypdfium2==5.14.0 &&
+ 57 "$stage/bin/python" -c "import docx, openpyxl, pptx, reportlab, pypdf, pdfplumber, pypdfium2" &&
+ 58 if [ -z "$old" ]; then python3 -c 'import os, sys; os.symlink(*sys.argv[1:])' "$stage" hermes-docs-venv
+ 59 elif [ "$(readlink hermes-docs-venv)" = "$old" ] && ! "$old/bin/python" -c "import docx, openpyxl, pptx, reportlab, pypdf, pdfplumber, pypdfium2" 2>/dev/null; then
+ 60 ln -s "$stage" "$stage.lnk" && python3 -c 'import os, sys; os.replace(*sys.argv[1:])' "$stage.lnk" hermes-docs-venv &&
+ 61 case "$old" in hermes-docs-venv.??????) rm -rf "$old" ;; esac
+ 62 fi 2>/dev/null
+ 63 [ "$(readlink hermes-docs-venv)" = "$stage" ] || rm -rf "$stage" "$stage.lnk"
+ 64 )
+ 65 fi
+ 66 export PY
+ 67 "$PY" scripts/pdf_create.py --help
+ 68 ```
+ 69
+ 70 Run every helper below with `"$PY"`. In PowerShell (Hermes runs on that
+ 71 host, so it is on PATH):
+ 72 `$env:PY = hermes pm environment documents --extra documents --extra documents-pdf`.
+ 73 Never `pip install` into the interpreter that runs Hermes or into the
+ 74 `hermes pm environment` one; Hermes owns both.
+ 75 - Page rasterization (`pdf_page_image.py`, overlay rendering) uses `pypdfium2`, which the documents environment already has (if your `python` lacks it, switch `PY` to that environment with the command above), or poppler's `pdftoppm` on PATH. Scripts fall back pypdfium2 → pdftoppm and report `{"rendered": false, "missing": [...]}` (exit 0) when neither exists.
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skills/productivity/powerpoint/SKILL.md
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- 1 ---
- 2 name: powerpoint
- 3 description: Create, read, edit .pptx decks with python-pptx.
- 4 version: 1.1.0
- 5 author: Nous Research
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [pptx, powerpoint, presentations, slides, office, python-pptx]
- 11 category: productivity
- 12 related_skills: [docx, xlsx, pdf]
- 13 ---
- 14
- 15 # Powerpoint Skill
- 16
- 17 Create, inspect, and edit PowerPoint (.pptx) presentations using the
- 18 python-pptx library. Five helper scripts cover deck creation from a JSON
- 19 spec, structured read-back, in-place edits, template-driven brand decks,
- 20 and slide rendering — all offline, no PowerPoint installation required.
- 21
- 22 ## When to Use
- 23
- 24 - The user asks to build a slide deck, report presentation, or pitch deck.
- 25 - You need to extract text, notes, tables, chart data, or images from a
- 26 .pptx someone shared.
- 27 - You need to update an existing deck: replace text, refresh or patch
- 28 chart data, swap a logo, duplicate/remove/reorder slides, set
- 29 backgrounds, footers, hyperlinks, or speaker notes.
- 30 - You must produce an on-brand deck from a company .pptx template.
- 31 - Do NOT use this for .ppt (legacy binary) files — convert them first with
- 32 `soffice --convert-to pptx old.ppt` if LibreOffice is available.
- 33
- 34 ## Prerequisites
- 35
- 36 - Python 3.10+ with `python-pptx` installed
- 37 (`pip install python-pptx`).
- 38 - Optional: LibreOffice (`soffice`) plus poppler (`pdftoppm` or
- 39 `pdftocairo`) for rendering slides to PNGs and for PDF export.
- 40 `pptx_render.py` detects both with `shutil.which` and degrades
- 41 gracefully (reports `{"rendered": false, "missing": [...]}`, exit 0)
- 42 when absent — all create/read/edit operations work without them.
- 43 - Check availability via `terminal`:
- 44 `python -c "import pptx; print(pptx.__version__)"` and `which soffice pdftoppm`.
- 45
- 46 ## How to Run
- 47
- 48 All scripts live in `scripts/`, take `--help`, print JSON to stdout, and
- 49 exit non-zero on failure. Run them with `terminal`:
- 50
- 51 ```bash
- 52 python scripts/pptx_create.py deck.json out.pptx
- 53 python scripts/pptx_read.py deck.pptx --outline # full JSON outline
- 54 python scripts/pptx_read.py deck.pptx --notes # speaker notes
- 55 python scripts/pptx_read.py deck.pptx --images ./img # export pictures
- 56 python scripts/pptx_edit.py deck.pptx --replace-text "Old Corp" "New Corp"
- 57 python scripts/pptx_edit.py deck.pptx --chart-data update.json
- 58 python scripts/pptx_edit.py deck.pptx --duplicate-slide 2
- 59 python scripts/pptx_edit.py deck.pptx --remove-slide 3 --move-slide 2 0
- 60 python scripts/pptx_from_template.py brand.pptx out.pptx --values vals.json
- 61 python scripts/pptx_render.py deck.pptx --outdir ./render # slide PNGs
- 62 ```
- 63
- 64 Author JSON specs with `write_file`; inspect script output and generated
- 65 JSON with `read_file`.
- 66
- 67 ## Quick Reference
- 68
- 69 | Task | Command |
- 70 |---|---|
- 71 | New deck from spec | `pptx_create.py spec.json out.pptx` |
- 72 | 16:9 vs 4:3 | `"slide_size": "16:9"` or `"4:3"` in the spec |
- 73 | Outline as JSON | `pptx_read.py deck.pptx --outline` |
- 74 | Export images | `pptx_read.py deck.pptx --images DIR` |
- 75 | Replace text | `pptx_edit.py deck.pptx --replace-text OLD NEW` |
- 76 | Replace chart data | `pptx_edit.py deck.pptx --chart-data spec.json` |
- 77 | Patch one series | same flag, spec with `"ops"` (see below) |
- 78 | Swap picture | `pptx_edit.py deck.pptx --swap-image N NAME new.png` |
- 79 | Duplicate slide | `pptx_edit.py deck.pptx --duplicate-slide N` |
- 80 | Remove slide | `pptx_edit.py deck.pptx --remove-slide N` |
- 81 | Reorder slide | `pptx_edit.py deck.pptx --move-slide FROM TO` |
- 82 | Slide background | `pptx_edit.py deck.pptx --set-background N RRGGBB` |
- 83 | Hyperlink runs | `pptx_edit.py deck.pptx --hyperlink N TEXT URL` |
- 84 | Slide number on | `pptx_edit.py deck.pptx --enable-slide-number N` |
- 85 | Footer text | `pptx_edit.py deck.pptx --set-footer N TEXT` |
- 86 | Set notes | `pptx_edit.py deck.pptx --set-notes N TEXT` |
- 87 | Append notes | `pptx_edit.py deck.pptx --append-notes N TEXT` |
- 88 | Fill template | `pptx_from_template.py tpl.pptx out.pptx --values v.json` |
- 89 | Render slide PNGs | `pptx_render.py deck.pptx --outdir DIR` |
- 90
- 91 ## Procedure
- 92
- 93 ### 1. Create a deck
- 94
- 95 Write a JSON spec (see `pptx_create.py --help` for the full format), then
- 96 run `pptx_create.py`. Per slide you can set: `layout` (title,
- 97 title_content, section, two_content, title_only, blank), `title`,
- 98 `subtitle`, `bullets` (strings, or dicts with `level` 0-4, `size` pt,
- 99 `bold`, `italic`, `font`, `color` hex, `link` URL for a hyperlink),
- 100 `background` (solid hex), `footer` (text; enables the layout's footer
- 101 placeholder), `slide_number` (true; enables the layout's slide-number
- 102 placeholder), `images` (path + left/top/width/height in inches), `tables`
- 103 (`rows` as list-of-lists), `shapes` (rectangle, rounded_rectangle, oval,
- 104 diamond, right_arrow, chevron, with `fill` hex + optional `text`),
- 105 `charts` (bar, bar_h, line, pie with `categories` + `series`), and
- 106 `notes` (speaker notes).
- 107
- 108 ### 2. Read a deck
- 109
- 110 `pptx_read.py deck.pptx --outline` returns slide size, layout inventory,
- 111 and per slide: layout name, all shape texts, table cells, image inventory
- 112 (filename/ext/bytes), chart categories/series/values, and speaker notes.
- 113 Use `--images DIR` to dump embedded pictures to files, then
- 114 `vision_analyze` on any exported image if you need to see its content.
- 115
- 116 ### 3. Edit a deck
- 117
- 118 `pptx_edit.py` combines operations in one pass; use `--output` to keep the
- 119 original. Text replacement scans slide shapes, table cells, and notes.
- 120 Image swap retargets the picture's relationship id so position and size
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skills/productivity/product-price-monitor/SKILL.md
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skills/productivity/xlsx/SKILL.md
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- 1 ---
- 2 name: xlsx
- 3 description: Create, read, edit Excel .xlsx workbooks and CSVs.
- 4 version: 1.1.0
- 5 author: Nous Research
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [excel, spreadsheet, xlsx, csv, openpyxl, productivity]
- 11 category: productivity
- 12 related_skills: [docx, pdf, powerpoint]
- 13 ---
- 14
- 15 # Xlsx Skill
- 16
- 17 Work with Excel .xlsx workbooks using Python and openpyxl: build styled
- 18 multi-sheet workbooks with formulas and charts, inspect or dump existing
- 19 files, edit cells and structure, and convert to/from CSV. All helper
- 20 scripts are argparse CLIs that print JSON and use explicit UTF-8 I/O.
- 21
- 22 ## When to Use
- 23
- 24 - Creating .xlsx reports: multiple sheets, number formats, styling,
- 25 merged cells, freeze panes, autofilter, conditional formatting,
- 26 charts, data-validation dropdowns, native Excel tables, defined
- 27 names, hyperlinks, cell notes, sheet protection.
- 28 - Reading a workbook: sheet inventory, dumping data as JSON or CSV,
- 29 listing formulas vs cached values, notes, defined names, tables.
- 30 - Editing existing files: set cells, append rows, insert/delete
- 31 rows/columns (reference-aware via `xlsx_restructure.py`),
- 32 copy/rename sheets, tables, names, notes, protection.
- 33 - Recalculating formulas headlessly via LibreOffice
- 34 (`xlsx_recalc.py`).
- 35 - CSV interop with type inference and non-UTF-8 encodings.
- 36 - Not for the legacy .xls binary format (use LibreOffice to convert
- 37 first: `soffice --headless --convert-to xlsx old.xls`).
- 38
- 39 ## Prerequisites
- 40
- 41 - Python 3.10+ with `openpyxl` (`pip install openpyxl`). No other
- 42 third-party packages are needed; everything else is stdlib.
- 43 - Optional: LibreOffice (`soffice`) for headless recalculation or
- 44 format conversion.
- 45
- 46 ## How to Run
- 47
- 48 Run the helper scripts with the `terminal` tool from this skill's
- 49 `scripts/` directory (every script supports `--help`):
- 50
- 51 ```bash
- 52 python scripts/xlsx_create.py spec.json report.xlsx # build from JSON spec
- 53 python scripts/xlsx_read.py report.xlsx --sheets # inventory
- 54 python scripts/xlsx_read.py report.xlsx --json --sheet Data
- 55 python scripts/xlsx_read.py report.xlsx --formulas
- 56 python scripts/xlsx_edit.py report.xlsx --sheet Data --set B2=42 --recalc
- 57 python scripts/xlsx_restructure.py report.xlsx --sheet Data --insert-rows 3:2
- 58 python scripts/xlsx_recalc.py report.xlsx
- 59 python scripts/csv_to_xlsx.py data.csv out.xlsx --encoding utf-8
- 60 python scripts/xlsx_to_csv.py report.xlsx out.csv --sheet Data
- 61 ```
- 62
- 63 Author the JSON spec with `write_file`, inspect script JSON output with
- 64 `read_file` or directly from stdout.
- 65
- 66 ## Quick Reference
- 67
- 68 | Task | Command |
- 69 |---|---|
- 70 | Create workbook from spec | `xlsx_create.py spec.json out.xlsx` |
- 71 | Sheet names + dimensions | `xlsx_read.py f.xlsx --sheets` |
- 72 | Dump sheet as JSON | `xlsx_read.py f.xlsx --json --sheet S` |
- 73 | Dump sheet as CSV | `xlsx_read.py f.xlsx --csv --out d.csv` |
- 74 | List formulas + cached values | `xlsx_read.py f.xlsx --formulas` |
- 75 | Set a cell / formula | `xlsx_edit.py f.xlsx --set "A1==SUM(B:B)"` |
- 76 | Append a row | `xlsx_edit.py f.xlsx --append '[1,"x",true]'` |
- 77 | Insert 2 rows, refs NOT shifted | `xlsx_edit.py f.xlsx --insert-rows 3:2` |
- 78 | Insert 2 rows, refs shifted | `xlsx_restructure.py f.xlsx --insert-rows 3:2` |
- 79 | Delete a column, refs shifted | `xlsx_restructure.py f.xlsx --delete-cols B` |
- 80 | Create a native table | `xlsx_edit.py f.xlsx --add-table Sales:A1:C9` |
- 81 | Append inside a table | `--table-append 'Sales=["West",5]'` |
- 82 | List tables | `xlsx_edit.py f.xlsx --list-tables` |
- 83 | Defined names | `--define-name "Rates='Data'!$B$2:$B$9"` / `--delete-name Rates` / `xlsx_read.py f.xlsx --names` |
- 84 | Hyperlink | `--hyperlink "A1=https://example.com|Docs"` |
- 85 | Cell note | `--note "B2=Check this|Reviewer"`; read via `xlsx_read.py f.xlsx --notes` |
- 86 | Protect sheet (see Pitfalls) | `--protect your-password --unlock B2:B9` |
- 87 | Recalculate via LibreOffice | `xlsx_recalc.py f.xlsx` |
- 88 | Copy / rename sheet | `--copy-sheet Src:New --rename-sheet Old:New` |
- 89 | Force recalc on open | `xlsx_edit.py f.xlsx --recalc` |
- 90 | CSV -> styled xlsx | `csv_to_xlsx.py in.csv out.xlsx` |
- 91 | xlsx -> CSV | `xlsx_to_csv.py f.xlsx out.csv --encoding utf-8` |
- 92
- 93 ## Procedure
- 94
- 95 1. **Create**: write a JSON spec (schema documented in
- 96 `xlsx_create.py --help` and its docstring). Each sheet supports
- 97 `rows` (scalars or styled cell objects), sparse `cells` overrides,
- 98 `column_widths`, `row_heights`, `merges`, `freeze_panes`,
- 99 `autofilter`, `conditional_formats` (cell_is rules and color
- 100 scales), `charts` (bar/line/pie from cell ranges),
- 101 `validations` (list dropdowns), `tables` (native Excel tables with
- 102 a style name), and `protection`. Workbook-level `defined_names`
- 103 maps names to refs. Cell objects also take `hyperlink` and `note`.
- 104 Typed values: JSON numbers/bools
- 105 pass through; dates use `{"value": "2026-01-31", "type": "date"}`.
- 106 Number formats are Excel format strings: currency `"$#,##0.00"`,
- 107 percent `"0.0%"`, date `"yyyy-mm-dd"`.
- 108 2. **Formulas**: set with `"formula": "SUM(B2:B9)"` in the spec or
- 109 `--set "C1==SUM(A:A)"` in the editor. When writing formulas, add
- 110 `"full_calc_on_load": true` (spec) or `--recalc` (editor); this sets
- 111 the workbook's `fullCalcOnLoad` flag so Excel/LibreOffice recompute
- 112 everything on open. openpyxl itself NEVER evaluates formulas.
- 113 3. **Read**: `--sheets` for inventory (names, dimensions, merged
- 114 ranges, chart count, tables, protection, defined names),
- 115 `--json`/`--csv` for data, `--formulas` to
- 116 pair each formula string with its cached result, `--notes` for
- 117 cell comments, `--names` for defined names. Cached results
- 118 exist only if the file was last saved by a real spreadsheet app;
- 119 files fresh from openpyxl return `null` there. To materialize
- 120 results headlessly run `xlsx_recalc.py file.xlsx` (uses
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skills/research/arxiv/SKILL.md
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skills/research/competitor-news-monitor/SKILL.md
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skills/research/llm-wiki/SKILL.md
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- 1 ---
- 2 name: llm-wiki
- 3 description: "Karpathy's LLM Wiki: build/query interlinked markdown KB."
- 4 version: 2.1.0
- 5 author: Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [wiki, knowledge-base, research, notes, markdown, rag-alternative]
- 11 category: research
- 12 related_skills: [obsidian, arxiv]
- 13 ---
- 14
- 15 # Karpathy's LLM Wiki
- 16
- 17 Build and maintain a persistent, compounding knowledge base as interlinked markdown files.
- 18 Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f).
- 19
- 20 Unlike traditional RAG (which rediscovers knowledge from scratch per query), the wiki
- 21 compiles knowledge once and keeps it current. Cross-references are already there.
- 22 Contradictions have already been flagged. Synthesis reflects everything ingested.
- 23
- 24 **Division of labor:** The human curates sources and directs analysis. The agent
- 25 summarizes, cross-references, files, and maintains consistency.
- 26
- 27 ## When This Skill Activates
- 28
- 29 Use this skill when the user:
- 30 - Asks to create, build, or start a wiki or knowledge base
- 31 - Asks to ingest, add, or process a source into their wiki
- 32 - Asks a question and an existing wiki is present at the configured path
- 33 - Asks to lint, audit, or health-check their wiki
- 34 - References their wiki, knowledge base, or "notes" in a research context
- 35
- 36 ## Wiki Location
- 37
- 38 **Location:** Set via `WIKI_PATH` environment variable (e.g. in `${HERMES_HOME:-~/.hermes}/.env`).
- 39
- 40 If unset, defaults to `~/wiki`.
- 41
- 42 ```bash
- 43 WIKI="${WIKI_PATH:-$HOME/wiki}"
- 44 ```
- 45
- 46 The wiki is just a directory of markdown files — open it in Obsidian, VS Code, or
- 47 any editor. No database, no special tooling required.
- 48
- 49 ## Architecture: Three Layers
- 50
- 51 ```
- 52 wiki/
- 53 ├── SCHEMA.md # Conventions, structure rules, domain config
- 54 ├── index.md # Sectioned content catalog with one-line summaries
- 55 ├── log.md # Chronological action log (append-only, rotated yearly)
- 56 ├── raw/ # Layer 1: Immutable source material
- 57 │ ├── articles/ # Web articles, clippings
- 58 │ ├── papers/ # PDFs, arxiv papers
- 59 │ ├── transcripts/ # Meeting notes, interviews
- 60 │ └── assets/ # Images, diagrams referenced by sources
- 61 ├── entities/ # Layer 2: Entity pages (people, orgs, products, models)
- 62 ├── concepts/ # Layer 2: Concept/topic pages
- 63 ├── comparisons/ # Layer 2: Side-by-side analyses
- 64 └── queries/ # Layer 2: Filed query results worth keeping
- 65 ```
- 66
- 67 **Layer 1 — Raw Sources:** Immutable. The agent reads but never modifies these.
- 68 **Layer 2 — The Wiki:** Agent-owned markdown files. Created, updated, and
- 69 cross-referenced by the agent.
- 70 **Layer 3 — The Schema:** `SCHEMA.md` defines structure, conventions, and tag taxonomy.
- 71
- 72 ## Resuming an Existing Wiki (CRITICAL — do this every session)
- 73
- 74 When the user has an existing wiki, **always orient yourself before doing anything**:
- 75
- 76 ① **Read `SCHEMA.md`** — understand the domain, conventions, and tag taxonomy.
- 77 ② **Read `index.md`** — learn what pages exist and their summaries.
- 78 ③ **Scan recent `log.md`** — read the last 20-30 entries to understand recent activity.
- 79
- 80 ```bash
- 81 WIKI="${WIKI_PATH:-$HOME/wiki}"
- 82 # Orientation reads at session start
- 83 read_file "$WIKI/SCHEMA.md"
- 84 read_file "$WIKI/index.md"
- 85 read_file "$WIKI/log.md" offset=<last 30 lines>
- 86 ```
- 87
- 88 Only after orientation should you ingest, query, or lint. This prevents:
- 89 - Creating duplicate pages for entities that already exist
- 90 - Missing cross-references to existing content
- 91 - Contradicting the schema's conventions
- 92 - Repeating work already logged
- 93
- 94 For large wikis (100+ pages), also run a quick `search_files` for the topic
- 95 at hand before creating anything new.
- 96
- 97 ## Initializing a New Wiki
- 98
- 99 When the user asks to create or start a wiki:
- 100
- 101 1. Determine the wiki path (from `$WIKI_PATH` env var, or ask the user; default `~/wiki`)
- 102 2. Create the directory structure above
- 103 3. Ask the user what domain the wiki covers — be specific
- 104 4. Write `SCHEMA.md` customized to the domain (see template below)
- 105 5. Write initial `index.md` with sectioned header
- 106 6. Write initial `log.md` with creation entry
- 107 7. Confirm the wiki is ready and suggest first sources to ingest
- 108
- 109 ### SCHEMA.md Template
- 110
- 111 Adapt to the user's domain. The schema constrains agent behavior and ensures consistency:
- 112
- 113 ```markdown
- 114 # Wiki Schema
- 115
- 116 ## Domain
- 117 [What this wiki covers — e.g., "AI/ML research", "personal health", "startup intelligence"]
- 118
- 119 ## Conventions
- 120 - File names: lowercase, hyphens, no spaces (e.g., `transformer-architecture.md`)
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skills/software-development/hermes-agent-skill-authoring/SKILL.md
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- 1 ---
- 2 name: hermes-agent-skill-authoring
- 3 description: "Author in-repo SKILL.md files: frontmatter and structure."
- 4 version: 2.0.0
- 5 author: Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [skills, authoring, hermes-agent, conventions, skill-md]
- 11 related_skills: [requesting-code-review]
- 12 ---
- 13
- 14 # Authoring Hermes-Agent Skills (in-repo)
- 15
- 16 ## Overview
- 17
- 18 There are two places a SKILL.md can live:
- 19
- 20 1. **User-local:** `~/.hermes/skills/<maybe-category>/<name>/SKILL.md` — personal, not shared. Created via `skill_manage(action='create')`.
- 21 2. **In-repo (this skill is about this case):** `skills/<category>/<name>/SKILL.md` or `optional-skills/<category>/<name>/SKILL.md` inside the hermes-agent repo — committed, shipped with the package. Use `write_file` + `git add`. `skill_manage(action='create')` does NOT target this tree.
- 22
- 23 In-repo skills must meet the repo's **hardline authoring standards** (see AGENTS.md, "Skill authoring standards (HARDLINE)" — that section is the source of truth; this skill is the operational walkthrough). Reviewers reject PRs that violate them, so meeting them up front is cheaper than a salvage pass later.
- 24
- 25 ## When to Use
- 26
- 27 - User asks you to add a skill "in this branch / repo / commit"
- 28 - You're committing a reusable workflow that should ship with hermes-agent
- 29 - You're editing an existing skill under `skills/` or `optional-skills/` (use `patch` for small edits, `write_file` for rewrites; `skill_manage` still works for patch on in-repo skills, but not for `create`)
- 30 - Don't use for: personal skills in `~/.hermes/skills/` (just use `skill_manage`)
- 31
- 32 ## Decide the Tier First: Bundled vs Optional
- 33
- 34 - **Bundled (`skills/<category>/`)** — daily-driver behavior, broadly useful across many user types, low footprint. Hard bar: you can say "a user will load this in 5+ sessions per month" with a straight face.
- 35 - **Optional (`optional-skills/<category>/`)** — niche, vertical-specific (blockchain, gaming, finance, one app), recurring-job/task skills, or anything heavy. Installed via `hermes skills install official/<category>/<skill>`.
- 36
- 37 **When in doubt, optional.** Promoting later is easy; demoting is churn. "Would be useful to anyone who ever needs this" is an optional-tier argument, not a bundled one.
- 38
- 39 Pick the category by what the tool IS, not what it feels like (an AI-agent CLI goes in `autonomous-ai-agents/` even if it "feels productivity"). Confirm existing categories with `search_files(pattern='*', target='files', path='skills')` and don't invent new top-level categories casually.
- 40
- 41 **No router / index / hub skills.** A skill whose core content is a routing table pointing at sibling skills adds an indirection hop and duplicates the siblings' own `When to Use` triggers. If the skill would be empty without "load skill X instead" pointers, don't write it — the catalog and each sibling's triggers already do that job.
- 42
- 43 ## Required Frontmatter
- 44
- 45 Validator source of truth: `tools/skill_manager_tool.py::_validate_frontmatter`. Validator hard requirements:
- 46
- 47 - Starts with `---` as the first bytes (no leading blank line).
- 48 - Closes with `\n---\n` before the body.
- 49 - Parses as a YAML mapping.
- 50 - `name` field present.
- 51 - `description` field present (validator ceiling 1024 chars — but see the repo hardline below, which is much stricter).
- 52 - Non-empty body after the closing `---`.
- 53
- 54 Repo-standard shape (all fields expected, even where the validator doesn't enforce them):
- 55
- 56 ```yaml
- 57 ---
- 58 name: my-skill-name # lowercase, hyphens, ≤64 chars (MAX_NAME_LENGTH)
- 59 description: Concise capability statement, under sixty chars.
- 60 version: 0.1.0 # semver; new skills start at 0.1.0
- 61 author: Real Name (github-handle), Hermes Agent
- 62 license: MIT
- 63 platforms: [linux, macos, windows] # audit, don't guess — see Platform Gating
- 64 metadata:
- 65 hermes:
- 66 tags: [Short, Descriptive, Tags]
- 67 related_skills: [other-in-repo-skill]
- 68 ---
- 69 ```
- 70
- 71 ### `description` rules (HARDLINE — the validator's 1024 is NOT the standard)
- 72
- 73 - **≤ 60 characters.** One sentence. Ends with a period.
- 74 - State the capability, not the implementation, and don't repeat the skill name or its category name (the index prints the category above the skill).
- 75 - No marketing words ("powerful", "comprehensive", "seamless", "advanced").
- 76 - 60 is the width the system prompt skill index shows; `skill_manage` rejects a new or changed description over it, and the index clips a longer legacy one at a word boundary.
- 77 - If the description contains a `:`, wrap it in double quotes or YAML parses it as a mapping and the docs generator crashes. Quotes don't count toward the 60.
- 78
- 79 Good: `Track named companies for material news with cited digests.`
- 80 Bad: `Use when a user asks to monitor named competitors or companies for product launches, pricing changes, funding, ...` (240 chars — rejected in review)
- 81
- 82 ### `author` rules
- 83
- 84 - Credit the **human first**, then "Hermes Agent" as secondary collaborator: `Ben Barclay (benbarclay), Hermes Agent`.
- 85 - Never `author: Hermes Agent` alone for contributed skills — credit the human, not the tool, even (especially) when an agent drafted the text.
- 86 - Maintainer-authored skills: `Teknium (teknium1), Hermes Agent`.
- 87
- 88 ### `related_skills` rules
- 89
- 90 - Every entry must resolve to an existing **in-repo** skill in the same tree state as your PR. Do not reference skills that were only planned, live in another PR, or exist only in `~/.hermes/skills/`.
- 91 - Verify each entry: `search_files(pattern='<name>', target='files', path='skills')` (and `optional-skills/`).
- 92
- 93 ## Platform Gating: audit, don't trust
- 94
- 95 `platforms:` gates loading by host OS. Set it from what the skill's prose and scripts actually invoke:
- 96
- 97 | Skill uses only… | `platforms:` |
- 98 |---|---|
- 99 | Hermes tools + stdlib Python + cross-platform CLIs | `[linux, macos, windows]` |
- 100 | bash pipelines, `grep`/`awk`/`sed` chains, heredocs | `[linux, macos]` |
- 101 | `osascript`, `defaults`, `pmset` | `[macos]` |
- 102 | `apt`/`systemctl`/`/proc` | `[linux]` |
- 103
- 104 <!-- no-tmp: ok — names the anti-pattern skill authors must avoid -->
- 105 POSIX-only signals to search for in `scripts/`: `fcntl`, `termios`, `pty`, `os.fork`, `os.killpg`, `signal.SIGKILL`, `os.kill(pid, 0)` liveness checks, hardcoded `/tmp` `/proc` `/etc`. Default posture: fix cross-platform first (`tempfile.gettempdir()`, `pathlib.Path`, `psutil.pid_exists`); gate narrower only when the dependency is genuinely platform-bound, and say why in `## Pitfalls`.
- 106
- 107 ## Size Limits
- 108
- 109 - Full SKILL.md: ≤ 100,000 chars enforced (`MAX_SKILL_CONTENT_CHARS`), but target **~100 lines for a simple skill, ~200 for a complex one**. Peer skills sit at 8-14k chars.
- 110 - Bulky or branch-specific material goes in `references/*.md`, `templates/`, or `scripts/` — pointed to from SKILL.md, not inlined.
- 111 - Don't expect the model to inline-write parsers or non-trivial logic every call — ship a helper script in `scripts/` and reference it by path.
- 112
- 113 ## Body Structure (modern section order)
- 114
- 115 ```
- 116 # <Skill> Skill
- 117 2-3 sentence intro: what it does, what it doesn't do, dependency stance.
- 118
- 119 ## When to Use — bulleted triggers (+ "Don't use for:" counter-triggers)
- 120 ## Prerequisites — exact env vars, installs, API key sourcing
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skills/software-development/inspecting-hermes-desktop-dom/SKILL.md
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- 1 ---
- 2 name: inspecting-hermes-desktop-dom
- 3 description: "Read the live Hermes desktop DOM/CSS over CDP."
- 4 version: 1.0.0
- 5 author: Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [desktop, electron, cdp, dom, ui-verification, self-inspection]
- 11 related_skills: [node-inspect-debugger, systematic-debugging, dogfood]
- 12 ---
- 13
- 14 # Inspecting the live Hermes desktop DOM
- 15
- 16 ## Overview
- 17
- 18 When you are developing `apps/desktop` and the user is running that same app
- 19 (`hgui` / `npm run dev`), you can read the **live rendered DOM** of the window
- 20 they are looking at — computed styles, geometry, which CSS rule actually won,
- 21 console output — instead of inferring it from `.tsx` and being wrong.
- 22
- 23 Dev-server runs open a Chrome DevTools Protocol port on `127.0.0.1:9222`
- 24 automatically. The renderer is a Chromium page, so everything DevTools can read,
- 25 a script can read.
- 26
- 27 **This does not replace looking at it.** CDP answers *factual* questions ("what
- 28 is the computed padding", "did this element render", "which selector matches").
- 29 It cannot tell you whether the result looks good. Colour balance, spacing feel,
- 30 and "is this ugly" still need the user's eyes or a screenshot. Answer facts with
- 31 CDP; hand aesthetics to the user.
- 32
- 33 ## When to Use
- 34
- 35 - Verifying a UI change actually took effect in the running app
- 36 - "Why is this element still X?" — find the winning rule before editing anything
- 37 - Locating a stable selector for a component you're about to change
- 38 - Checking a design token's computed value on a real node
- 39 - Reading renderer console errors the user mentions but can't copy out
- 40
- 41 **Don't use for:** perf profiling or heap work (`node-inspect-debugger`,
- 42 `debugging-hermes-desktop`), or anything where the real question is "does this
- 43 look right".
- 44
- 45 ## The port
- 46
- 47 Open on `127.0.0.1:9222` for any dev-server run. Closed in exactly two cases
- 48 (`apps/desktop/electron/dev-cdp.ts`):
- 49
- 50 - **packaged builds** — always, and no environment value overrides it;
- 51 - **no `HERMES_DESKTOP_DEV_SERVER`** — an unpackaged `electron .` against
- 52 `dist/` is how the packaged app gets smoke tested, so it behaves like one.
- 53
- 54 `HERMES_DESKTOP_CDP_PORT` moves the port (`=9333`) or disables it (`=off`).
- 55
- 56 Check before doing anything else:
- 57
- 58 ```bash
- 59 curl -s --max-time 3 http://127.0.0.1:${HERMES_DESKTOP_CDP_PORT:-9222}/json/version
- 60 ```
- 61
- 62 Empty → no port. Do not guess another port silently.
- 63
- 64 **Never relaunch the user's app to get a port.** That destroys their session and
- 65 their state. Launch your own isolated instance instead (below).
- 66
- 67 ## Reading the DOM
- 68
- 69 `apps/desktop/scripts/eval.mjs` is the one-liner:
- 70
- 71 ```bash
- 72 cd apps/desktop
- 73 node scripts/eval.mjs "document.querySelectorAll('[data-slot]').length"
- 74 ```
- 75
- 76 For multi-step work use the shared client — it has target discovery and
- 77 promise-aware eval:
- 78
- 79 ```js
- 80 import { CDP, SELECTORS } from './scripts/perf/lib/cdp.mjs'
- 81
- 82 const cdp = await CDP.connect({ port: 9222, match: '5174' })
- 83 const out = await cdp.eval(`JSON.stringify({
- 84 radius: getComputedStyle(document.documentElement).getPropertyValue('--radius-scalar').trim(),
- 85 composer: !!document.querySelector('[data-slot="composer-rich-input"]')
- 86 })`)
- 87 cdp.close()
- 88 ```
- 89
- 90 `SELECTORS` in `scripts/perf/lib/cdp.mjs` holds the stable `data-slot` hooks
- 91 (composer, thread viewport, assistant message, turn pair, profile rail). Prefer
- 92 them over inventing a `querySelector` — they are updated as a unit when
- 93 components move.
- 94
- 95 ## The question this is best at: which rule won?
- 96
- 97 Editing every call site because a style "isn't applying" is the classic waste.
- 98 Read the real node first:
- 99
- 100 ```js
- 101 const el = document.querySelector('[data-slot="aui_assistant-message-root"] a')
- 102 JSON.stringify({
- 103 ownClasses: el.className,
- 104 weight: getComputedStyle(el).fontWeight,
- 105 parents: (() => {
- 106 const out = []
- 107 let n = el
- 108 while ((n = n.parentElement) && out.length < 6) out.push(n.className)
- 109 return out
- 110 })()
- 111 })
- 112 ```
- 113
- 114 If the node carries no class of its own, the value is **inherited** — sweeping
- 115 call sites will not fix it, and you need the ancestor rule. A plugin stylesheet
- 116 (e.g. `@tailwindcss/typography`'s `prose a { font-weight: 500 }`) routinely beats
- 117 a utility class; override on the shared class, not at each usage.
- 118
- 119 ## Your own isolated instance
- 120
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skills/software-development/node-inspect-debugger/SKILL.md
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- 1 ---
- 2 name: node-inspect-debugger
- 3 description: "Debug Node.js via --inspect + Chrome DevTools Protocol CLI."
- 4 version: 1.0.0
- 5 author: Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos, windows]
- 8 metadata:
- 9 hermes:
- 10 tags: [debugging, nodejs, node-inspect, cdp, breakpoints, ui-tui]
- 11 related_skills: [systematic-debugging, python-debugpy]
- 12 ---
- 13
- 14 # Node.js Inspect Debugger
- 15
- 16 ## Overview
- 17
- 18 When `console.log` isn't enough, drive Node's built-in V8 inspector programmatically from the terminal. You get real breakpoints, step in/over/out, call-stack walking, local/closure scope dumps, and arbitrary expression evaluation in the paused frame.
- 19
- 20 Two tools, pick one:
- 21
- 22 - **`node inspect`** — built-in, zero install, CLI REPL. Best for quick poking.
- 23 - **`ndb` / CDP via `chrome-remote-interface`** — scriptable from Node/Python; best when you want to automate many breakpoints, collect state across runs, or debug non-interactively from an agent loop.
- 24
- 25 **Prefer `node inspect` first.** It's always available and the REPL is fast.
- 26
- 27 ## When to Use
- 28
- 29 - A Node test fails and you need to see intermediate state
- 30 - ui-tui crashes or behaves wrong and you want to inspect React/Ink state pre-render
- 31 - tui_gateway child processes (`_SlashWorker`, PTY bridge workers) misbehave
- 32 - You need to inspect a value in a closure that `console.log` can't reach without patching
- 33 - Perf: attach to a running process to capture a CPU profile or heap snapshot
- 34
- 35 **Don't use for:** things `console.log` solves in under a minute. Breakpoint-driven debugging is heavier; use it when the payoff is real.
- 36
- 37 ## Quick Reference: `node inspect` REPL
- 38
- 39 Launch paused on first line:
- 40
- 41 ```bash
- 42 node inspect path/to/script.js
- 43 # or with tsx
- 44 node --inspect-brk $(which tsx) path/to/script.ts
- 45 ```
- 46
- 47 The `debug>` prompt accepts:
- 48
- 49 | Command | Action |
- 50 |---|---|
- 51 | `c` or `cont` | continue |
- 52 | `n` or `next` | step over |
- 53 | `s` or `step` | step into |
- 54 | `o` or `out` | step out |
- 55 | `pause` | pause running code |
- 56 | `sb('file.js', 42)` | set breakpoint at file.js line 42 |
- 57 | `sb(42)` | set breakpoint at line 42 of current file |
- 58 | `sb('functionName')` | break when function is called |
- 59 | `cb('file.js', 42)` | clear breakpoint |
- 60 | `breakpoints` | list all breakpoints |
- 61 | `bt` | backtrace (call stack) |
- 62 | `list(5)` | show 5 lines of source around current position |
- 63 | `watch('expr')` | evaluate expr on every pause |
- 64 | `watchers` | show watched expressions |
- 65 | `repl` | drop into REPL in current scope (Ctrl+C to exit REPL) |
- 66 | `exec expr` | evaluate expression once |
- 67 | `restart` | restart script |
- 68 | `kill` | kill the script |
- 69 | `.exit` | quit debugger |
- 70
- 71 **In the `repl` sub-mode:** type any JS expression, including access to locals/closure variables. `Ctrl+C` exits back to `debug>`.
- 72
- 73 ## Attaching to a Running Process
- 74
- 75 When the process is already running (e.g. a long-lived dev server or the TUI gateway):
- 76
- 77 ```bash
- 78 # 1. Send SIGUSR1 to enable the inspector on an existing process
- 79 kill -SIGUSR1 <pid>
- 80 # Node prints: Debugger listening on ws://127.0.0.1:9229/<uuid>
- 81
- 82 # 2. Attach the debugger CLI
- 83 node inspect -p <pid>
- 84 # or by URL
- 85 node inspect ws://127.0.0.1:9229/<uuid>
- 86 ```
- 87
- 88 To start a process with the inspector from the beginning:
- 89
- 90 ```bash
- 91 node --inspect script.js # listen on 127.0.0.1:9229, keep running
- 92 node --inspect-brk script.js # listen AND pause on first line
- 93 node --inspect=0.0.0.0:9230 script.js # custom host:port
- 94 ```
- 95
- 96 For TypeScript via tsx:
- 97
- 98 ```bash
- 99 node --inspect-brk --import tsx script.ts
- 100 # or older tsx
- 101 node --inspect-brk -r tsx/cjs script.ts
- 102 ```
- 103
- 104 ## Programmatic CDP (scripting from terminal)
- 105
- 106 When you want to automate — set many breakpoints, capture scope state, script a repro — use `chrome-remote-interface`:
- 107
- 108 ```bash
- 109 npm i -g chrome-remote-interface # or project-local
- 110 # Start your target:
- 111 node --inspect-brk=9229 target.js &
- 112 ```
- 113
- 114 Driver script (save as `~/.hermes/cache/scratch/cdp-debug.js`):
- 115
- 116 ```javascript
- 117 const CDP = require('chrome-remote-interface');
- 118
- 119 (async () => {
- 120 const client = await CDP({ port: 9229 });
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skills/software-development/python-debugpy/SKILL.md
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- 1 ---
- 2 name: python-debugpy
- 3 description: "Debug Python: pdb REPL + debugpy remote (DAP)."
- 4 version: 1.0.0
- 5 author: Hermes Agent
- 6 license: MIT
- 7 platforms: [linux, macos]
- 8 metadata:
- 9 hermes:
- 10 tags: [debugging, python, pdb, debugpy, breakpoints, dap, post-mortem]
- 11 related_skills: [systematic-debugging, node-inspect-debugger]
- 12 ---
- 13
- 14 # Python Debugger (pdb + debugpy)
- 15
- 16 ## Overview
- 17
- 18 Three tools, picked by situation:
- 19
- 20 | Tool | When |
- 21 |---|---|
- 22 | **`breakpoint()` + pdb** | Local, interactive, simplest. Add `breakpoint()` in the source, run normally, get a REPL at that line. |
- 23 | **`python -m pdb`** | Launch an existing script under pdb with no source edits. Useful for quick poking. |
- 24 | **`debugpy`** | Remote / headless / "attach to already-running process." Talks DAP, scriptable from terminal, works for long-lived processes (gateway, daemon, PTY children). |
- 25
- 26 **Start with `breakpoint()`.** It's the cheapest thing that works.
- 27
- 28 ## When to Use
- 29
- 30 - A test fails and the traceback doesn't reveal why a value is wrong
- 31 - You need to step through a function and watch a collection mutate
- 32 - A long-running process (hermes gateway, tui_gateway) misbehaves and you can't restart it
- 33 - Post-mortem: an exception fired in prod-ish code and you want to inspect locals at the crash site
- 34 - A subprocess / child (Python `_SlashWorker`, PTY bridge worker) is the actual bug site
- 35
- 36 **Don't use for:** things `print()` / `logging.debug` solve in under a minute, or things `pytest -vv --tb=long --showlocals` already reveals.
- 37
- 38 ## pdb Quick Reference
- 39
- 40 Inside any pdb prompt (`(Pdb)`):
- 41
- 42 | Command | Action |
- 43 |---|---|
- 44 | `h` / `h cmd` | help |
- 45 | `n` | next line (step over) |
- 46 | `s` | step into |
- 47 | `r` | return from current function |
- 48 | `c` | continue |
- 49 | `unt N` | continue until line N |
- 50 | `j N` | jump to line N (same function only) |
- 51 | `l` / `ll` | list source around current line / full function |
- 52 | `w` | where (stack trace) |
- 53 | `u` / `d` | move up / down in the stack |
- 54 | `a` | print args of the current function |
- 55 | `p expr` / `pp expr` | print / pretty-print expression |
- 56 | `display expr` | auto-print expr on every stop |
- 57 | `b file:line` | set breakpoint |
- 58 | `b func` | break on function entry |
- 59 | `b file:line, cond` | conditional breakpoint |
- 60 | `cl N` | clear breakpoint N |
- 61 | `tbreak file:line` | one-shot breakpoint |
- 62 | `!stmt` | execute arbitrary Python (assignments included) |
- 63 | `interact` | drop into full Python REPL in current scope (Ctrl+D to exit) |
- 64 | `q` | quit |
- 65
- 66 The `interact` command is the most powerful — you can import anything, inspect complex objects, even call methods that mutate state. Locals are read-only by default; use `!x = 42` from the `(Pdb)` prompt to mutate.
- 67
- 68 ## Recipe 1: Local breakpoint
- 69
- 70 Easiest. Edit the file:
- 71
- 72 ```python
- 73 def compute(x, y):
- 74 result = some_helper(x)
- 75 breakpoint() # <-- drops into pdb here
- 76 return result + y
- 77 ```
- 78
- 79 Run the code normally. You land at the `breakpoint()` line with full access to locals.
- 80
- 81 **Don't forget to remove `breakpoint()` before committing.** Use `git diff` or a pre-commit grep:
- 82 ```bash
- 83 rg -n 'breakpoint\(\)' --type py
- 84 ```
- 85
- 86 ## Recipe 2: Launch a script under pdb (no source edits)
- 87
- 88 ```bash
- 89 python -m pdb path/to/script.py arg1 arg2
- 90 # Lands at first line of script
- 91 (Pdb) b path/to/script.py:42
- 92 (Pdb) c
- 93 ```
- 94
- 95 ## Recipe 3: Debug a pytest test
- 96
- 97 Use `terminal` and the canonical runner for noninteractive diagnostics:
- 98
- 99 ```bash
- 100 # Show locals in tracebacks without pdb:
- 101 scripts/run_tests.sh tests/path/to/test_file.py --showlocals --tb=long
- 102 ```
- 103
- 104 `scripts/run_tests.sh` captures each file in a separate subprocess, so `--pdb`
- 105 or `--trace` cannot provide an interactive prompt there. For an interactive
- 106 debugger only, use the independent development/test interpreter prepared in
- 107 Recipe 5 (never a production generation):
- 108
- 109 ```bash
- 110 .venv/bin/python -m pytest tests/foo_test.py::test_bar --pdb
- 111 ```
- 112
- 113 This bypasses the hermetic-env guarantees — fine for debugging, but re-run under the wrapper to confirm before pushing.
- 114
- 115 ## Recipe 4: Post-mortem on any exception
- 116
- 117 ```python
- 118 import pdb, sys
- 119 try:
- 120 run_the_thing()
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skills/software-development/requesting-code-review/SKILL.md
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skills/software-development/simplify-code/SKILL.md
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skills/software-development/spike/SKILL.md
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skills/software-development/systematic-debugging/SKILL.md
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skills/software-development/test-driven-development/SKILL.md
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skills/web/blocked-page-recovery/SKILL.md
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Verified commit e8639fd4312e5102d6f10c76b56406cb01c19092
git clone --filter=blob:none --no-checkout https://github.com/NousResearch/hermes-agent.git .hotskills/nousresearch-hermes-agent && git -C .hotskills/nousresearch-hermes-agent checkout --detach e8639fd4312e5102d6f10c76b56406cb01c19092
# Copy each selected skill directory from .hotskills/nousresearch-hermes-agent into .agents/skills/<skill-name>/Heat
7.2- Star growth, last 7 daysAhead of 82% of tracked repositories
- Star growth, last 24 hoursAhead of 83% of tracked repositories
- Rank movement, last 7 daysAhead of 74% of tracked repositories
- Commits, last 30 daysAhead of 100% of tracked repositories
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekAhead of 0% of tracked repositories
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
Individual skills in this repository
- agent-merge-conflict-arbiter
optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter - antigravity-cli
optional-skills/autonomous-ai-agents/antigravity-cli - blackbox
optional-skills/autonomous-ai-agents/blackbox - dynamic-workflow
optional-skills/autonomous-ai-agents/dynamic-workflow - grok
optional-skills/autonomous-ai-agents/grok - honcho
optional-skills/autonomous-ai-agents/honcho - opencode
optional-skills/autonomous-ai-agents/opencode - openhands
optional-skills/autonomous-ai-agents/openhands - evm
optional-skills/blockchain/evm - hyperliquid
optional-skills/blockchain/hyperliquid - solana
optional-skills/blockchain/solana - one-three-one-rule
optional-skills/communication/one-three-one-rule - ai-presenter-video
optional-skills/creative/ai-presenter-video - anatomy
optional-skills/creative/anatomy - archify
optional-skills/creative/archify - ascii-art
optional-skills/creative/ascii-art - auteur
optional-skills/creative/auteur - baoyu-article-illustrator
optional-skills/creative/baoyu-article-illustrator - baoyu-comic
optional-skills/creative/baoyu-comic - brag
optional-skills/creative/brag - brag-slim
optional-skills/creative/brag-slim - comfyui
optional-skills/creative/comfyui - concept-diagrams
optional-skills/creative/concept-diagrams - creative-ideation
optional-skills/creative/creative-ideation - design-md
optional-skills/creative/design-md - draw-your-font
optional-skills/creative/draw-your-font - dream-loop
optional-skills/creative/dream-loop - excalidraw
optional-skills/creative/excalidraw - heartmula
optional-skills/creative/heartmula - hyperframes
optional-skills/creative/hyperframes - impeccable
optional-skills/creative/impeccable - ip-as-logo
optional-skills/creative/ip-as-logo - kanban-video-orchestrator
optional-skills/creative/kanban-video-orchestrator - meme-generation
optional-skills/creative/meme-generation - mono-color
optional-skills/creative/mono-color - pixel-art
optional-skills/creative/pixel-art - pretext
optional-skills/creative/pretext - simple-english
optional-skills/creative/simple-english - sketch
optional-skills/creative/sketch - social-media-content-calendar
optional-skills/creative/social-media-content-calendar - system-atlas
optional-skills/creative/system-atlas - tldraw-offline
optional-skills/creative/tldraw-offline - unreal-mcp
optional-skills/creative/unreal-mcp - jupyter-notebook
optional-skills/data-science/jupyter-notebook - actual-setup
optional-skills/devops/actual-setup - docker-management
optional-skills/devops/docker-management - hermes-s6-container-supervision
optional-skills/devops/hermes-s6-container-supervision - inference-sh-cli
optional-skills/devops/inference-sh-cli - pinggy-tunnel
optional-skills/devops/pinggy-tunnel - setup-wizard-generator
optional-skills/devops/setup-wizard-generator - watchers
optional-skills/devops/watchers - adversarial-ux-test
optional-skills/dogfood/adversarial-ux-test - agentmail
optional-skills/email/agentmail - 3-statement-model
optional-skills/finance/3-statement-model - comps-analysis
optional-skills/finance/comps-analysis - dcf-model
optional-skills/finance/dcf-model - excel-author
optional-skills/finance/excel-author - lbo-model
optional-skills/finance/lbo-model - merger-model
optional-skills/finance/merger-model - polymarket
optional-skills/finance/polymarket - pptx-author
optional-skills/finance/pptx-author - stocks
optional-skills/finance/stocks - minecraft-modpack-server
optional-skills/gaming/minecraft-modpack-server - pokemon-player
optional-skills/gaming/pokemon-player - fitness-nutrition
optional-skills/health/fitness-nutrition - neuroskill-bci
optional-skills/health/neuroskill-bci - fastmcp
optional-skills/mcp/fastmcp - mcp-oauth-remote-gateway
optional-skills/mcp/mcp-oauth-remote-gateway - mcporter
optional-skills/mcp/mcporter - gif-search
optional-skills/media/gif-search - songsee
optional-skills/media/songsee - accelerate
optional-skills/mlops/accelerate - chroma
optional-skills/mlops/chroma - evaluating-llms-harness
optional-skills/mlops/evaluation/evaluating-llms-harness - faiss
optional-skills/mlops/faiss - flash-attention
optional-skills/mlops/flash-attention - llama-cpp
optional-skills/mlops/inference/llama-cpp - serving-llms-vllm
optional-skills/mlops/inference/serving-llms-vllm - lambda-labs
optional-skills/mlops/lambda-labs - modal
optional-skills/mlops/modal - segment-anything-model
optional-skills/mlops/models/segment-anything-model - nemo-curator
optional-skills/mlops/nemo-curator - obliteratus
optional-skills/mlops/obliteratus - peft
optional-skills/mlops/peft - pytorch-fsdp
optional-skills/mlops/pytorch-fsdp - pytorch-lightning
optional-skills/mlops/pytorch-lightning - dspy
optional-skills/mlops/research/dspy - saelens
optional-skills/mlops/saelens - simpo
optional-skills/mlops/simpo - slime
optional-skills/mlops/slime - tensorrt-llm
optional-skills/mlops/tensorrt-llm - torchtitan
optional-skills/mlops/torchtitan - axolotl
optional-skills/mlops/training/axolotl - unsloth
optional-skills/mlops/training/unsloth - mpp-agent
optional-skills/payments/mpp-agent - stripe-link-cli
optional-skills/payments/stripe-link-cli - stripe-projects
optional-skills/payments/stripe-projects - airtable
optional-skills/productivity/airtable - box
optional-skills/productivity/box - canvas
optional-skills/productivity/canvas - decision-questionnaire
optional-skills/productivity/decision-questionnaire - here-now
optional-skills/productivity/here-now - live-dashboard
optional-skills/productivity/live-dashboard - memento-flashcards
optional-skills/productivity/memento-flashcards - notion
optional-skills/productivity/notion - property-listings
optional-skills/productivity/property-listings - shop
optional-skills/productivity/shop - shopify
optional-skills/productivity/shopify - siyuan
optional-skills/productivity/siyuan - telephony
optional-skills/productivity/telephony - bioinformatics
optional-skills/research/bioinformatics - blogwatcher
optional-skills/research/blogwatcher - darwinian-evolver
optional-skills/research/darwinian-evolver - domain-intel
optional-skills/research/domain-intel - drug-discovery
optional-skills/research/drug-discovery - duckduckgo-search
optional-skills/research/duckduckgo-search - gitnexus-explorer
optional-skills/research/gitnexus-explorer - llm-wiki
optional-skills/research/llm-wiki - osint-investigation
optional-skills/research/osint-investigation - parallel-cli
optional-skills/research/parallel-cli - qmd
optional-skills/research/qmd - research-paper-writing
optional-skills/research/research-paper-writing - rss-feeds
optional-skills/research/rss-feeds - scrapling
optional-skills/research/scrapling - searxng-search
optional-skills/research/searxng-search - 1password
optional-skills/security/1password - godmode
optional-skills/security/godmode - oss-forensics
optional-skills/security/oss-forensics - sherlock
optional-skills/security/sherlock - unbroker
optional-skills/security/unbroker - web-pentest
optional-skills/security/web-pentest - openhue
optional-skills/smart-home/openhue - reddit-reading
optional-skills/social-media/reddit-reading - ast-grep
optional-skills/software-development/ast-grep - code-wiki
optional-skills/software-development/code-wiki - grill-me
optional-skills/software-development/grill-me - hermes-agent-skill-authoring
optional-skills/software-development/hermes-agent-skill-authoring - inspecting-hermes-desktop-dom
optional-skills/software-development/inspecting-hermes-desktop-dom - node-inspect-debugger
optional-skills/software-development/node-inspect-debugger - pr-lens
optional-skills/software-development/pr-lens - python-debugpy
optional-skills/software-development/python-debugpy - rest-graphql-debug
optional-skills/software-development/rest-graphql-debug - subagent-driven-development
optional-skills/software-development/subagent-driven-development - cloudflare-temporary-deploy
optional-skills/web-development/cloudflare-temporary-deploy - har-derived-api-client
optional-skills/web-development/har-derived-api-client - page-agent
optional-skills/web-development/page-agent - publish-site
optional-skills/web-development/publish-site - scrollcraft
optional-skills/web-development/scrollcraft - yuanbao
optional-skills/yuanbao - google_meet
plugins/google_meet - apple-notes
skills/apple/apple-notes - apple-reminders
skills/apple/apple-reminders - findmy
skills/apple/findmy - imessage
skills/apple/imessage - claude-code
skills/autonomous-ai-agents/claude-code - codex
skills/autonomous-ai-agents/codex - computer-use
skills/autonomous-ai-agents/computer-use - hermes-agent
skills/autonomous-ai-agents/hermes-agent - architecture-diagram
skills/creative/architecture-diagram - ascii-video
skills/creative/ascii-video - baoyu-infographic
skills/creative/baoyu-infographic - claude-design
skills/creative/claude-design - humanizer
skills/creative/humanizer - manim-video
skills/creative/manim-video - p5js
skills/creative/p5js - popular-web-designs
skills/creative/popular-web-designs - songwriting-and-ai-music
skills/creative/songwriting-and-ai-music - sdlc-review
skills/devops/sdlc-review - email-inbox-triage
skills/email/email-inbox-triage - himalaya
skills/email/himalaya - youtube-content
skills/media/youtube-content - obsidian
skills/note-taking/obsidian - document-to-action-items
skills/productivity/document-to-action-items - google-workspace
skills/productivity/google-workspace - maps
skills/productivity/maps - meeting-action-items
skills/productivity/meeting-action-items - microsoft-office
skills/productivity/microsoft-office - pdf
skills/productivity/pdf - product-price-monitor
skills/productivity/product-price-monitor - teams-meeting-pipeline
skills/productivity/teams-meeting-pipeline - weekly-review-planning
skills/productivity/weekly-review-planning - arxiv
skills/research/arxiv - competitor-news-monitor
skills/research/competitor-news-monitor - grounded-citations
skills/research/grounded-citations - xurl
skills/social-media/xurl - codebase-inspection
skills/software-development/codebase-inspection - dogfood
skills/software-development/dogfood - github
skills/software-development/github - requesting-code-review
skills/software-development/requesting-code-review - simplify-code
skills/software-development/simplify-code - spike
skills/software-development/spike - systematic-debugging
skills/software-development/systematic-debugging - test-driven-development
skills/software-development/test-driven-development - blocked-page-recovery
skills/web/blocked-page-recovery
How it works
agent-merge-conflict-arbiter
When your agent uses it
Neutral arbiter for merge conflicts between two agents.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,779 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Files
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# Agent Merge-Conflict Arbiter
Resolve a git merge conflict between two AGENTS' branches as an impartial third
party. Agents resolving conflicts against a peer's work reliably either
overwrite the peer or abandon their own change — they lack the peer's context
and are biased toward their own side. This skill is the fix: a neutral
reconciler that receives both diffs plus both sides' stated intents and
produces a merged result, like a merge-queue arbiter.
## When to Use
- Two agent branches/worktrees collide during a parallel campaign (kanban
engineering pipeline, parallel-PR wave, multi-worktree refactor).
- `git merge` or `git rebase` halts on conflicts between two agents' work andView the complete source on GitHubantigravity-cli
When your agent uses it
Operate the Antigravity CLI (agy): plugins, auth, sandbox.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
2,510 tokensWhen the skill is activated
Reference documents
557 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Mental model
- Prerequisites
- How to Run
- Delegation patterns
- Core paths
- Quick Reference
- Settings and permissions
- Authentication behavior
- Plugins
- Pitfalls
- Verification
- Support files
Files
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# Antigravity CLI (`agy`)
Operator guide for the Antigravity CLI, invoked as `agy`. Run all `agy`
commands through the Hermes `terminal` tool; inspect its config and logs with
`read_file`. This skill is reference + procedure — it does not wrap a network
API, so there is nothing to authenticate from Hermes itself.
## When to Use
- Installing, updating, or smoke-testing the `agy` binary
- Driving non-interactive `agy --print` / `agy -p` one-shots
- Debugging Antigravity auth, sandbox, permissions, or plugin state
- Reading Antigravity settings, keybindings, conversations, or logs
View the complete source on GitHubblackbox
When your agent uses it
Delegate coding tasks to the Blackbox AI multi-model CLI.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,361 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- One-Shot Tasks
- Background Mode (Long Tasks)
- Checkpoints & Resume
- Session Commands
- PR Reviews
- Parallel Work
- Multi-Model Mode
- Key Flags
- Vision Support
- Token Limits
- Rules
Files
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# Blackbox CLI
Delegate coding tasks to [Blackbox AI](https://www.blackbox.ai/) via the Hermes terminal. Blackbox is a multi-model coding agent CLI that dispatches tasks to multiple LLMs (Claude, Codex, Gemini, Blackbox Pro) and uses a judge to select the best implementation.
The CLI (npm `@blackbox_ai/blackbox-cli`, binary `blackbox`) is a TypeScript coding agent (forked from Gemini CLI) and supports interactive sessions, non-interactive one-shots, checkpointing, MCP, and vision model switching.
## Prerequisites
- Node.js 20+ installed
- Blackbox CLI installed: `npm install -g @blackbox_ai/blackbox-cli` (binary: `blackbox`)
- API key from [app.blackbox.ai/dashboard](https://app.blackbox.ai/dashboard)
- Configured: run `blackbox configure` and enter your API key
- Use `pty=true` in terminal calls — Blackbox CLI is an interactive terminal app
View the complete source on GitHubdynamic-workflow
When your agent uses it
Plan-in-code fan-outs, adversarial verification, waves.
What it loads into context
Frontmatter
285 tokensAlways available to the agent
SKILL.md
2,702 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Files
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# Dynamic Workflow Skill
Runs large fan-out work as a workflow: the plan, the loop and every intermediate
result live in a script and on disk, so the parent's context holds only verified
results. Covers one-shot fan-outs, adversarial convergence (attempts + refuters),
and multi-wave campaigns that integrate dozens of worker branches. It does not
make `delegate_task` durable across restarts; that is the kanban swarm's job.
## When to Use
Reach for it when the unit of work is clear (a file, an endpoint, a record) and
there are more units than one context can hold. Skip it for under ~10 units or
for serial chains. For a refactor or fix campaign on hermes-agent itself, load
`hermes-agent` (the dev workflow) alongside; this skill owns the fan-out shape.View the complete source on GitHubgrok
When your agent uses it
Delegate coding to xAI Grok Build CLI (features, PRs).
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
3,703 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Prerequisites
- Two Orchestration Modes
- Headless Deep Dive
- Read-Only Audit → Markdown Note Pattern
- PR Review Patterns
- Parallel Issue Fixing with Worktrees
- Useful Subcommands & TUI Commands
- Config (`~/.grok/config.toml`)
- Pitfalls & Gotchas
- Rules for Hermes Agents
Files
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# Grok Build CLI — Hermes Orchestration Guide
Delegate coding tasks to [Grok Build](https://docs.x.ai/build/overview) (xAI's
autonomous coding agent CLI, the `grok` command) via the Hermes terminal. Grok
can read files, write code, run shell commands, spawn subagents, and manage git
workflows. It runs three ways: an interactive TUI, **headless** (`-p`), and as
an **ACP agent** over JSON-RPC.
This is the third sibling to `codex` and `claude-code`. The orchestration
pattern is nearly identical — **prefer headless `-p` for one-shots**, use a PTY
for interactive sessions.
## When to use
View the complete source on GitHubhoncho
When your agent uses it
Configure and troubleshoot Honcho memory for Hermes.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
4,673 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Setup
- Architecture
- Three Orthogonal Knobs
- Multi-Profile Setup
- Tools
- Agent Usage Patterns
- Config Reference
- Troubleshooting
- CLI Commands
Scripts executed separately
../../../hermes
Files
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# Honcho Memory for Hermes
Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Hermes profile its own peer identity while sharing a unified view of the user.
## When to Use
- Setting up Honcho (cloud or self-hosted)
- Troubleshooting memory not working / peers not syncing
- Creating multi-profile setups where each agent has its own Honcho peer
- Tuning observation, recall, dialectic depth, or write frequency settings
- Understanding what the 5 Honcho tools do and when to use them
- Configuring context budgets and session summary injection
## SetupView the complete source on GitHubopencode
When your agent uses it
Delegate coding to OpenCode CLI (features, PR review).
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,852 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Binary Resolution (Important)
- One-Shot Tasks
- Interactive Sessions (Background)
- Common Flags
- Procedure
- PR Review Workflow
- Parallel Work Pattern
- Session & Cost Management
- Pitfalls
- Verification
- Rules
Files
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# OpenCode CLI
Use [OpenCode](https://opencode.ai) as an autonomous coding worker orchestrated by Hermes terminal/process tools. OpenCode is a provider-agnostic, open-source AI coding agent with a TUI and CLI.
## When to Use
- User explicitly asks to use OpenCode
- You want an external coding agent to implement/refactor/review code
- You need long-running coding sessions with progress checks
- You want parallel task execution in isolated workdirs/worktrees
## Prerequisites
- OpenCode installed: `npm i -g opencode-ai@latest` or `brew install anomalyco/tap/opencode`View the complete source on GitHubopenhands
When your agent uses it
Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
2,318 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Real Flag List
- JSON Event Schema
- Pitfalls
- Verification
- Related
Files
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# OpenHands CLI
Delegate coding tasks to the [OpenHands CLI](https://github.com/All-Hands-AI/OpenHands) via the `terminal` tool. OpenHands is model-agnostic: any LiteLLM-supported provider (OpenAI, Anthropic, OpenRouter, DeepSeek, Ollama, vLLM, etc.).
This skill is the headless-mode wrapper for batch / one-shot delegation. The interactive textual UI is not used from Hermes.
## When to Use
- User wants a coding task delegated to OpenHands specifically.
- User wants a coding agent that can run on a non-Anthropic / non-OpenAI provider (DeepSeek, Qwen, Ollama, vLLM, Nous, etc.) — sibling skills `claude-code` and `codex` are tied to one vendor.
- Multi-step file edits + shell commands inside a workspace.
For Claude-native, prefer `claude-code`. For OpenAI-native, prefer `codex`. For Hermes-native subagents, use `delegate_task`.
View the complete source on GitHubevm
When your agent uses it
Read-only EVM client: wallets, tokens, gas across 8 chains.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
2,288 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure
- Supported Chains
- Pitfalls
- Verification
Scripts executed separately
scripts/evm_client.py
Files
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# EVM Blockchain Skill
Query EVM-compatible blockchain data across 8 chains with USD pricing.
14 commands: wallet portfolio, token info, transactions, activity, gas tracker,
network stats, price lookup, multi-chain scan, whale detection, ENS resolution,
allowance checker, contract inspector, and transaction decoder.
Supports 8 chains: Ethereum, BNB Chain (BSC), Base, Arbitrum One, Polygon,
Optimism, Avalanche (C-Chain), zkSync Era.
No API key needed. Zero external dependencies — Python standard library only
(urllib, json, argparse, threading).
> **Supersedes the standalone `base` skill.** Base-specific tokens (AERO, DEGEN,View the complete source on GitHubhyperliquid
When your agent uses it
Hyperliquid market data, account history, trade review.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,875 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/hyperliquid_client.py
Files
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# Hyperliquid Skill
Query Hyperliquid market and account data through the public `/info` endpoint.
Read-only — no API key, no signing, no order placement.
12 commands: `dexs`, `markets`, `spots`, `candles`, `funding`, `l2`, `state`,
`spot-balances`, `fills`, `orders`, `review`, `export`. Stdlib only
(`urllib`, `json`, `argparse`).
---
## When to Use
- User asks for Hyperliquid perp or spot market data, candles, funding, or L2 bookView the complete source on GitHubsolana
When your agent uses it
Query Solana wallets, tokens, txs, and NFTs in USD.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,790 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/solana_client.py
Files
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# Solana Blockchain Skill
Query Solana on-chain data enriched with USD pricing via CoinGecko.
8 commands: wallet portfolio, token info, transactions, activity, NFTs,
whale detection, network stats, and price lookup.
No API key needed. Uses only Python standard library (urllib, json, argparse).
---
## When to Use
- User asks for a Solana wallet balance, token holdings, or portfolio value
- User wants to inspect a specific transaction by signatureView the complete source on GitHubone-three-one-rule
When your agent uses it
1-3-1 decision briefs: problem, three options, one pick.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
985 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Verification
- Example
Files
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# 1-3-1 Communication Rule
Structured decision-making format for when a task has multiple viable approaches and the user needs a clear recommendation. Produces a concise problem framing, three options with trade-offs, and an actionable plan for the recommended path.
## When to Use
- The user explicitly asks for a "1-3-1" response.
- The user says "give me options" or "what are my choices" for a technical decision.
- A task has multiple viable approaches with meaningful trade-offs (architecture, tooling, migration strategy).
- The user needs a proposal they can forward to a team or stakeholder.
Do NOT use for simple questions with one obvious answer, debugging sessions, or tasks where the user has already decided on an approach.
## ProcedureView the complete source on GitHubai-presenter-video
When your agent uses it
Make a verified AI presenter video from script + image.
What it loads into context
Frontmatter
153 tokensAlways available to the agent
SKILL.md
2,219 tokensWhen the skill is activated
Reference documents
2,354 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Hermes adaptations (read first)
- Workflow
- Operating rules (non-negotiable)
- Defaults for minimal input
- Reference routing
- Pitfalls
- Verification
Scripts executed separately
scripts/finalize_delivery.shscripts/init_job.pyscripts/preflight.py
Files
LICENSEasset1,063 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesSKILL.mdcore9,304 bytesassets/job.template.jsonasset1,870 bytesreferences/editing.mddoc3,608 bytesreferences/generation.mddoc4,614 bytesreferences/qa-recovery.mddoc3,821 bytesscripts/finalize_delivery.shruns6,960 bytesscripts/init_job.pyruns5,443 bytesscripts/preflight.pyruns7,177 bytes
Read SKILL.md
# AI Presenter Video
Turn a topic (or finished script) plus ONE authorized adult presenter image
into a complete, publish-ready presenter-led video: locked narration, avatar
generation with lip-sync QA, captions, deterministic editing, loudness-normalized
master/share encodes, and machine + visual acceptance reports.
Use this skill for new presenter videos AND for continuing, revising,
captioning, lip-sync-repairing, or re-exporting an existing presenter-video
job. The workflow is provider-neutral: pick generation capabilities from what
is actually available in the session (FAL video/image models via
`image_generate` and the video-gen plugin, TTS via `text_to_speech`, ASR via
the whisper/STT tooling, ffmpeg for everything deterministic).
View the complete source on GitHubanatomy
When your agent uses it
Explain an idea as an interactive isometric machine figure.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
8,444 tokensWhen the skill is activated
Reference documents
81,097 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Running it in Hermes
- The examples
- The one idea that matters
- Workflow
- Setup
- Craft rules (short form)
- WebGL inside the drawing
- 3D: figures that turn (beta)
- References
Scripts executed separately
examples/arcade-cabinet/build.mjsexamples/arcade-cabinet/live.jsexamples/arcade-cabinet/parts.mjsexamples/arcade-cabinet/screen.mjsexamples/desk-computer/build.mjsexamples/dial-indicator/build.mjsexamples/raptor-engine/build.mjsexamples/raptor-engine/live.jsexamples/ripple-tank/build.mjsexamples/ripple-tank/live.jsexamples/ripple-tank/model.mjsexamples/spot-plate/art.tsxexamples/spot-plate/drops.tsexamples/spot-plate/fan.tsexamples/spot-plate/figure.tsxexamples/spot-plate/geometry.tsexamples/spot-plate/live.tsxexamples/spot-plate/rig.tsexamples/test-rig/art.tsxexamples/test-rig/figure.tsxexamples/test-rig/geometry.tsexamples/test-rig/live.tsxexamples/test-rig/pose.tsexamples/test-rig/rig.tsexamples/test-rig/view.tsexamples/turning-dial/build.mjsexamples/turning-dial/live.jsexamples/turning-dial/stress.mjskit/audit.mjskit/canvas-painter.mjskit/gl-shared.mjskit/gl.mjskit/iso-kit.mjskit/iso-kit.tskit/lathe.mjskit/react/draw.tsxkit/react/live.tsxkit/tube.mjskit/turn-audit.mjskit/turn-build.mjskit/turn-fixed.mjskit/turn.mjsscripts/capture.mjsscripts/drive.mjsscripts/inline-kit.mjsscripts/line-ends.mjsscripts/lines.mjsscripts/turn-bench.mjsscripts/turn-check.mjsscripts/turn-fidelity.mjs
Files
LICENSE.txtdoc1,061 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesSKILL.mdcore33,780 bytesexamples/arcade-cabinet/arcade-cabinet.cssasset3,221 bytesexamples/arcade-cabinet/build.mjsruns51,493 bytesexamples/arcade-cabinet/live.jsruns21,274 bytesexamples/arcade-cabinet/parts.mjsruns4,296 bytesexamples/arcade-cabinet/screen.mjsruns6,701 bytesexamples/desk-computer/build.mjsruns22,981 bytesexamples/dial-indicator/build.mjsruns14,973 bytesexamples/raptor-engine/build.mjsruns70,705 bytesexamples/raptor-engine/live.jsruns30,752 bytesexamples/raptor-engine/raptor.cssasset6,792 bytesexamples/ripple-tank/build.mjsruns32,062 bytesexamples/ripple-tank/live.jsruns21,720 bytesexamples/ripple-tank/model.mjsruns5,004 bytesexamples/ripple-tank/ripple.cssasset1,675 bytesexamples/spot-plate/art.tsxruns4,491 bytesexamples/spot-plate/drops.tsruns19,355 bytesexamples/spot-plate/fan.tsruns2,715 bytesexamples/spot-plate/figure.tsxruns1,078 bytesexamples/spot-plate/geometry.tsruns10,962 bytesexamples/spot-plate/live.tsxruns11,718 bytesexamples/spot-plate/rig.tsruns8,160 bytesexamples/spot-plate/spot-plate.cssasset1,518 bytesexamples/test-rig/art.tsxruns7,372 bytesexamples/test-rig/figure.tsxruns850 bytesexamples/test-rig/geometry.tsruns13,085 bytesexamples/test-rig/live.tsxruns17,463 bytesexamples/test-rig/pose.tsruns3,894 bytesexamples/test-rig/rig.tsruns5,604 bytesexamples/test-rig/stretch.cssasset1,210 bytesexamples/test-rig/view.tsruns3,115 bytesexamples/turning-dial/build.mjsruns32,098 bytesexamples/turning-dial/dial.cssasset509 bytesexamples/turning-dial/live.jsruns12,460 bytesexamples/turning-dial/stress.mjsruns19,414 byteskit/audit.mjsruns89,650 byteskit/canvas-painter.mjsruns27,535 byteskit/gl-shared.mjsruns8,863 byteskit/gl.mjsruns8,763 byteskit/iso-kit.mjsruns35,175 byteskit/iso-kit.tsruns38,631 byteskit/iso.cssasset7,698 byteskit/lathe.mjsruns48,028 byteskit/react/draw.tsxruns6,425 byteskit/react/live.tsxruns4,066 byteskit/tube.mjsruns14,230 byteskit/turn-audit.mjsruns57,157 byteskit/turn-build.mjsruns64,933 byteskit/turn-fixed.mjsruns147,055 byteskit/turn.mjsruns139,815 bytesreferences/3d.mddoc44,644 bytesreferences/craft.mddoc50,299 bytesreferences/kit.mddoc67,808 bytesreferences/motion.mddoc22,611 bytesreferences/react.mddoc3,614 bytesreferences/verify.mddoc43,574 bytesreferences/walkthrough.mddoc5,276 bytesreferences/webgl.mddoc61,100 bytesscripts/capture.mjsruns12,276 bytesscripts/drive.mjsruns21,050 bytesscripts/inline-kit.mjsruns648 bytesscripts/line-ends.mjsruns13,175 bytesscripts/lines.mjsruns1,700 bytesscripts/turn-bench.mjsruns3,863 bytesscripts/turn-check.mjsruns37,531 bytesscripts/turn-fidelity.mjsruns19,605 bytes
Read SKILL.md
# Anatomy
This skill builds figures that work like a small instrument on a bench: a heavy base on feet, rails with tick rulers, a carriage on bushings, knurled knobs, coil springs, a dial gauge whose needle moves, and screws drawn as tiny dots. The reader understands the idea because they recognise the machine. Every number the figure shows comes from the same model that moves its parts.
Output is a standalone HTML page (SVG plus optional WebGL layers), a lone `.svg`, or React/Next.js components. The kit uses only Node built-ins, so there is nothing to `npm install`.
## Running it in Hermes
**Skill directory.** Hermes expands `${HERMES_SKILL_DIR}` to this skill's installed directory. Shell variables don't carry over between terminal calls, so repeat the assignment in every call that uses it:
```bash
SKILL_DIR="${HERMES_SKILL_DIR}"
```
View the complete source on GitHubarchify
When your agent uses it
Validated interactive HTML diagrams, upstream-maintained.
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
600 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
Files
SKILL.mdcore2,449 bytesSKILL.mdcore2,449 bytes
Read SKILL.md
# Archify (upstream-maintained)
> **Catalog stub.** This entry is maintained upstream at
> [tt-a1i/archify](https://github.com/tt-a1i/archify): the project ships a
> self-contained skill directory (`archify/`) with the Node CLI, schemas,
> renderers, examples and references. `hermes skills install
> official/creative/archify` pulls the current tree live from that repo
> (quarantined and scanned like any hub install) — this directory holds only
> the catalog metadata, so the vendored copy can never go stale.
Archify turns a small typed JSON spec into a self-contained, explorable HTML
diagram: `architecture`, `workflow`, `sequence`, `dataflow` and `lifecycle`
(state machine) types, dark/light themes, pan/zoom, search, relationship
tracing, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. It acceptsView the complete source on GitHubascii-art
When your agent uses it
ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
3,050 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Tool 1: Text Banners (pyfiglet — local)
- Tool 2: Text Banners (asciified API — remote, no install)
- Tool 3: Cowsay (Message Art)
- Tool 4: Boxes (Decorative Borders)
- Tool 5: TOIlet (Colored Text Art)
- Tool 6: Image to ASCII Art
- Tool 7: Search Pre-Made ASCII Art
- Tool 8: Fun ASCII Utilities (via curl)
- Tool 9: LLM-Generated Custom Art (Fallback)
- Decision Flow
Files
SKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytesSKILL.mdcore10,614 bytes
Read SKILL.md
# ASCII Art Skill
Multiple tools for different ASCII art needs. All tools are local CLI programs or free REST APIs — no API keys required.
## Tool 1: Text Banners (pyfiglet — local)
Render text as large ASCII art banners. 571 built-in fonts.
### Setup
```bash
pip install pyfiglet --break-system-packages -q
```
View the complete source on GitHubauteur
When your agent uses it
Design and build cinematic, award-level web pages.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
5,535 tokensWhen the skill is activated
Reference documents
54,540 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/chromadiff.mjsscripts/moodboard.mjsscripts/motionqa.mjsscripts/refscout.mjsscripts/shoot.mjsscripts/slopscan.mjsscripts/source.mjsscripts/systemscan.mjstemplates/scroll-flight-engine.js
Files
LICENSEasset1,372 bytesSKILL.mdcore22,780 bytesSKILL.mdcore22,780 bytesSKILL.mdcore22,780 bytesSKILL.mdcore22,780 bytesreferences/ambient-backgrounds.mddoc8,119 bytesreferences/assets.mddoc26,930 bytesreferences/build.mddoc5,491 bytesreferences/direct.mddoc13,059 bytesreferences/motion.mddoc16,848 bytesreferences/recon.mddoc12,621 bytesreferences/scroll-cinema.mddoc61,131 bytesreferences/scroll-flight.mddoc10,855 bytesreferences/system.mddoc10,692 bytesreferences/taste.mddoc13,916 bytesreferences/verify.mddoc15,088 bytesscripts/chromadiff.mjsruns7,761 bytesscripts/moodboard.mjsruns11,913 bytesscripts/motionqa.mjsruns8,444 bytesscripts/refscout.mjsruns22,003 bytesscripts/shoot.mjsruns8,799 bytesscripts/slopscan.mjsruns22,174 bytesscripts/source.mjsruns21,546 bytesscripts/systemscan.mjsruns22,785 bytestemplates/CINEMA-QA.mddoc2,263 bytestemplates/COMMIT-SHEET.mddoc3,155 bytestemplates/DESIGN.mddoc3,990 bytestemplates/STORYBOARD.mddoc4,578 bytestemplates/SYSTEM-SHEET.mddoc4,986 bytestemplates/scroll-flight-engine.jsruns35,442 bytes
Read SKILL.md
# Auteur Skill
> Ported from [agiwhitelist/auteur](https://github.com/agiwhitelist/auteur) (MIT), snapshot
> commit [`9bca227d`](https://github.com/agiwhitelist/auteur/commit/9bca227df9877e60dc45d49783c8cbd885eccd9b)
> — see `LICENSE`. Scripts, templates and references are the upstream files (CRLF→LF), with
> Hermes adaptation notes and `references/` path fixes as the only edits.
Auteur designs and builds web experiences the way a film director makes a film: script first, then assets, then the shoot, then the cut. It has three registers — **build** (an excellent conventional site), **direct** (a cinematic scroll-directed site) and **system** (a multi-screen product as one design system) — on one shared core of taste. Nothing ships until the page passes an executable anti-slop gate and the skill has looked at its own output.
## When to Use
- A landing page, marketing site, hero section, portfolio or product page has to be **built or redesigned** — and looking generic is not acceptable.
- The brief asks for **scroll animation, storytelling, or a site that feels like a film**.
- A product spans **several screens that must feel like one thing** — app, dashboard, admin, onboarding, docs.View the complete source on GitHubbaoyu-article-illustrator
When your agent uses it
Article illustrations: type × style × palette consistency.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
2,449 tokensWhen the skill is activated
Reference documents
23,561 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Three Dimensions
- Types
- Styles
- Output Structure
- Core Principles
- Workflow
- Modification
- References
- Pitfalls
Files
PORT_NOTES.mddoc3,418 bytesSKILL.mdcore10,086 bytesSKILL.mdcore10,086 bytesSKILL.mdcore10,086 bytesSKILL.mdcore10,086 bytesprompts/system.mddoc1,050 bytesreferences/palettes/macaron.mddoc1,339 bytesreferences/palettes/mono-ink.mddoc1,780 bytesreferences/palettes/neon.mddoc1,025 bytesreferences/palettes/warm.mddoc1,036 bytesreferences/prompt-construction.mddoc14,981 bytesreferences/style-presets.mddoc4,830 bytesreferences/styles.mddoc9,385 bytesreferences/styles/blueprint.mddoc1,844 bytesreferences/styles/chalkboard.mddoc2,249 bytesreferences/styles/editorial.mddoc1,722 bytesreferences/styles/elegant.mddoc1,733 bytesreferences/styles/fantasy-animation.mddoc1,969 bytesreferences/styles/flat-doodle.mddoc1,869 bytesreferences/styles/flat.mddoc1,688 bytesreferences/styles/ink-notes.mddoc4,289 bytesreferences/styles/intuition-machine.mddoc1,850 bytesreferences/styles/minimal.mddoc1,607 bytesreferences/styles/nature.mddoc1,703 bytesreferences/styles/notion.mddoc1,636 bytesreferences/styles/pixel-art.mddoc1,907 bytesreferences/styles/playful.mddoc1,707 bytesreferences/styles/retro.mddoc1,682 bytesreferences/styles/scientific.mddoc1,741 bytesreferences/styles/screen-print.mddoc2,680 bytesreferences/styles/sketch-notes.mddoc1,824 bytesreferences/styles/sketch.mddoc1,691 bytesreferences/styles/vector-illustration.mddoc1,940 bytesreferences/styles/vintage.mddoc1,819 bytesreferences/styles/warm.mddoc1,760 bytesreferences/styles/watercolor.mddoc1,815 bytesreferences/usage.mddoc1,834 bytesreferences/workflow.mddoc13,752 bytes
Read SKILL.md
# Article Illustrator
Adapted from [baoyu-article-illustrator](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.
Analyze articles, identify illustration positions, generate images with **Type × Style × Palette** consistency.
## When to Use
Trigger this skill when the user asks to illustrate an article, add images to an article, generate illustrations for content, or uses phrases like "为文章配图", "illustrate article", or "add images". The user provides an article (file path or pasted content) and optionally specifies type, style, palette, or density.
## Three Dimensions
| Dimension | Controls | Examples |
|-----------|----------|----------|View the complete source on GitHubbaoyu-comic
When your agent uses it
Knowledge comics (知识漫画): educational, biography, tutorial.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
3,553 tokensWhen the skill is activated
Reference documents
24,107 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Reference Images
- Options
- File Structure
- Language Handling
- Workflow
- References
- Page Modification
- Pitfalls
Files
PORT_NOTES.mddoc5,725 bytesSKILL.mdcore14,542 bytesSKILL.mdcore14,542 bytesSKILL.mdcore14,542 bytesreferences/analysis-framework.mddoc5,481 bytesreferences/art-styles/chalk.mddoc2,790 bytesreferences/art-styles/ink-brush.mddoc2,569 bytesreferences/art-styles/ligne-claire.mddoc2,384 bytesreferences/art-styles/manga.mddoc2,608 bytesreferences/art-styles/minimalist.mddoc3,402 bytesreferences/art-styles/realistic.mddoc2,775 bytesreferences/auto-selection.mddoc3,581 bytesreferences/base-prompt.mddoc3,140 bytesreferences/character-template.mddoc4,876 bytesreferences/layouts/cinematic.mddoc450 bytesreferences/layouts/dense.mddoc415 bytesreferences/layouts/four-panel.mddoc1,388 bytesreferences/layouts/mixed.mddoc429 bytesreferences/layouts/splash.mddoc471 bytesreferences/layouts/standard.mddoc440 bytesreferences/layouts/webtoon.mddoc778 bytesreferences/ohmsha-guide.mddoc2,978 bytesreferences/partial-workflows.mddoc3,051 bytesreferences/presets/concept-story.mddoc5,644 bytesreferences/presets/four-panel.mddoc4,660 bytesreferences/presets/ohmsha.mddoc4,484 bytesreferences/presets/shoujo.mddoc3,273 bytesreferences/presets/wuxia.mddoc2,972 bytesreferences/storyboard-template.mddoc3,982 bytesreferences/tones/action.mddoc2,269 bytesreferences/tones/dramatic.mddoc1,913 bytesreferences/tones/energetic.mddoc2,173 bytesreferences/tones/neutral.mddoc1,356 bytesreferences/tones/romantic.mddoc2,143 bytesreferences/tones/vintage.mddoc2,162 bytesreferences/tones/warm.mddoc1,862 bytesreferences/workflow.mddoc16,168 bytes
Read SKILL.md
# Knowledge Comic Creator
Adapted from [baoyu-comic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.
Create original knowledge comics with flexible art style × tone combinations.
## When to Use
Trigger this skill when the user asks to create a knowledge/educational comic, biography comic, tutorial comic, or uses terms like "知识漫画", "教育漫画", or "Logicomix-style". The user provides content (text, file path, URL, or topic) and optionally specifies art style, tone, layout, aspect ratio, or language.
## Reference Images
Hermes' `image_generate` tool is **prompt-only** — it accepts a text prompt and an aspect ratio, and returns an image URL. It does **NOT** accept reference images. When the user supplies a reference image, use it to **extract traits in text** that get embedded in every page prompt:
View the complete source on GitHubbrag
When your agent uses it
Project launch video via Hyperframes, upstream-maintained.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
761 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
Files
SKILL.mdcore2,861 bytes
Read SKILL.md
# Brag (upstream-maintained)
> **Catalog stub.** This entry is maintained upstream at
> [latent-spaces/brag](https://github.com/latent-spaces/brag): the project
> ships the skill directory (`skills/brag/`) with its step references, tone
> presets, bundled music and SFX library, and cue-analysis script. `hermes
> skills install official/creative/brag` pulls the current tree live from that
> repo (quarantined and scanned like any hub install) — this directory holds
> only the catalog metadata, so the vendored copy can never go stale.
`/brag` reads the project in the current directory, commits to an angle and a
tone (`default`, `polished`, `yc-parody`, `chaotic`, `deadpan`, `cinematic`,
`app-store`, or freeform direction), storyboards a 15–25 second video, hands a
composition brief to Hyperframes, and renders `brag-output/brag.mp4` with aView the complete source on GitHubbrag-slim
When your agent uses it
Launch video from a project or URL, upstream-maintained.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
518 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
Files
SKILL.mdcore1,987 bytes
Read SKILL.md
# Brag Slim (upstream-maintained)
> **Catalog stub.** This entry is maintained upstream at
> [latent-spaces/brag](https://github.com/latent-spaces/brag): the project
> ships `/brag-slim` as a single `SKILL.md` under `skills/brag-slim/`. `hermes
> skills install official/creative/brag-slim` pulls the current file live from
> that repo (quarantined and scanned like any hub install) — this directory
> holds only the catalog metadata, so the vendored copy can never go stale.
`/brag-slim` is the lean `/brag`, written for Claude Opus 5.5: no Hyperframes,
no bundled assets. The model builds the whole launch video itself (story,
visuals, music, sound effects, mix and render) with the tools already on the
machine. It takes the current project directory or a website URL, reuses the
source's real UI, fonts and assets instead of redrawing them, and writesView the complete source on GitHubcomfyui
When your agent uses it
Generate images, video, and audio via diffusion workflows.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
6,272 tokensWhen the skill is activated
Reference documents
11,664 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What's in this skill
- When to Use
- Architecture: Two Layers
- Quick Start
- Core Workflow
- Decision Tree
- Setup & Onboarding
- Image Upload (img2img / Inpainting)
- Cloud Specifics
- Queue & System Management
- Pitfalls
- Verification Checklist
Scripts executed separately
scripts/_common.pyscripts/auto_fix_deps.pyscripts/check_deps.pyscripts/comfyui_setup.shscripts/extract_schema.pyscripts/fetch_logs.pyscripts/hardware_check.pyscripts/health_check.pyscripts/run_batch.pyscripts/run_workflow.pyscripts/ws_monitor.pytests/conftest.pytests/test_check_deps.pytests/test_cloud_integration.pytests/test_common.pytests/test_extract_schema.pytests/test_run_workflow.py
Files
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Read SKILL.md
# ComfyUI
Generate images, video, audio, and 3D content through ComfyUI using the
official `comfy-cli` for setup/lifecycle and direct REST/WebSocket API
for workflow execution.
## What's in this skill
**Reference docs (`references/`):**
- `official-cli.md` — every `comfy ...` command, with flags
- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas
- `workflow-format.md` — API-format JSON, common node types, param mapping
- `template-integrity.md` — converting `comfyui-workflow-templates` fromView the complete source on GitHubconcept-diagrams
When your agent uses it
Generate flat, minimal educational SVG visuals as HTML.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
4,506 tokensWhen the skill is activated
Reference documents
50,249 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scope
- Workflow
- Design System
- SVG Boilerplate
- Diagram Types
- Validation Checklist
- Output & Preview
- Examples Reference
- Quick Reference: What to Use When
Files
SKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesSKILL.mdcore16,463 bytesexamples/apartment-floor-plan-conversion.mddoc9,231 bytesexamples/automated-password-reset-flow.mddoc15,268 bytesexamples/autonomous-llm-research-agent-flow.mddoc12,630 bytesexamples/banana-journey-tree-to-smoothie.mddoc6,122 bytesexamples/commercial-aircraft-structure.mddoc7,513 bytesexamples/cpu-ooo-microarchitecture.mddoc9,576 bytesexamples/electricity-grid-flow.mddoc6,864 bytesexamples/feature-film-production-pipeline.mddoc8,652 bytesexamples/hospital-emergency-department-flow.mddoc7,903 bytesexamples/ml-benchmark-grouped-bar-chart.mddoc4,790 bytesexamples/place-order-uml-sequence.mddoc20,108 bytesexamples/smart-city-infrastructure.mddoc7,021 bytesexamples/smartphone-layer-anatomy.mddoc5,965 bytesexamples/sn2-reaction-mechanism.mddoc8,221 bytesexamples/wind-turbine-structure.mddoc15,012 bytesreferences/dashboard-patterns.mddoc1,799 bytesreferences/infrastructure-patterns.mddoc5,335 bytesreferences/physical-shape-cookbook.mddoc2,083 bytestemplates/template.htmlasset6,796 bytes
Read SKILL.md
# Concept Diagrams
Generate production-quality SVG diagrams with a unified flat, minimal design system. Output is a single self-contained HTML file that renders identically in any modern browser, with automatic light/dark mode.
## Scope
**Best suited for:**
- Physics setups, chemistry mechanisms, math curves, biology
- Physical objects (aircraft, turbines, smartphones, mechanical watches, cells)
- Anatomy, cross-sections, exploded layer views
- Floor plans, architectural conversions
- Narrative journeys (lifecycle of X, process of Y)
- Hub-spoke system integrations (smart city, IoT networks, electricity grids)
- Educational / textbook-style visuals in any domainView the complete source on GitHubcreative-ideation
When your agent uses it
Generate ideas via named methods from creative practice.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,596 tokensWhen the skill is activated
Reference documents
28,807 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Operating rules
- Routing — 4-step procedure
- Output format
- File map
- Attribution
Files
SKILL.mdcore10,434 bytesSKILL.mdcore10,434 bytesreferences/anti-slop.mddoc7,223 bytesreferences/exercises.mddoc6,069 bytesreferences/full-prompt-library.mddoc8,119 bytesreferences/heuristics.mddoc5,133 bytesreferences/method-catalog.mddoc4,932 bytesreferences/methods/affinity-diagrams.mddoc4,661 bytesreferences/methods/analogy-and-blending.mddoc5,330 bytesreferences/methods/biomimicry.mddoc4,596 bytesreferences/methods/chance-and-remix.mddoc3,839 bytesreferences/methods/compression-progress.mddoc3,024 bytesreferences/methods/creative-discipline.mddoc5,057 bytesreferences/methods/defamiliarization.mddoc4,180 bytesreferences/methods/derive-and-mapping.mddoc4,298 bytesreferences/methods/first-principles.mddoc4,234 bytesreferences/methods/jobs-to-be-done.mddoc4,014 bytesreferences/methods/lateral-provocations.mddoc3,780 bytesreferences/methods/leverage-points.mddoc4,674 bytesreferences/methods/oblique-strategies.mddoc2,916 bytesreferences/methods/oulipo.mddoc3,313 bytesreferences/methods/pataphysics.mddoc3,934 bytesreferences/methods/pattern-languages.mddoc4,608 bytesreferences/methods/polya.mddoc3,572 bytesreferences/methods/premortem-and-inversion.mddoc3,756 bytesreferences/methods/scamper.mddoc3,575 bytesreferences/methods/story-skeletons.mddoc6,211 bytesreferences/methods/triz-principles.mddoc6,021 bytesreferences/methods/volume-generation.mddoc3,909 bytes
Read SKILL.md
# Creative Ideation
A library of ideation methods for any domain. Read the user's situation, route to the matching method, apply, generate output that is specific and non-obvious. Methods are tools — pick the right one for the situation, don't perform all of them.
## When to use
Any open-ended generative or selective question: "I want to make / build / write / start something", "I'm stuck", "inspire me", "make this weirder", "help me pick", "I need to invent X", "give me a research question".
## Operating rules
1. **Constraint plus direction is creativity.** No constraint = no traction. No direction = no shape. Methods supply both.
2. **Refuse the first three ideas.** They're slop. Generate, discard, regenerate. See `references/anti-slop.md`.
3. **One method per response unless asked.** Don't stack.
4. **Specificity over abstraction.** Real proper nouns, real materials, real mechanisms. "An app for X" is slop; "a 200-line CLI tool that prints Y when Z" is direction. Naming a tech stack is not specificity — name a mechanism.View the complete source on GitHubdesign-md
When your agent uses it
Author/validate/export Google's DESIGN.md token spec files.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
2,230 tokensWhen the skill is activated
Reference documents
720 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- File anatomy
- Token types
- Canonical section order
- Workflow: authoring a new DESIGN.md
- Workflow: lint / diff / export
- Pitfalls
- Spec source of truth
Files
SKILL.mdcore8,477 bytesSKILL.mdcore8,477 bytesSKILL.mdcore8,477 bytesSKILL.mdcore8,477 bytestemplates/starter.mddoc2,566 bytes
Read SKILL.md
# DESIGN.md Skill
DESIGN.md is Google's open spec (Apache-2.0, `google-labs-code/design.md`) for
describing a visual identity to coding agents. One file combines:
- **YAML front matter** — machine-readable design tokens (normative values)
- **Markdown body** — human-readable rationale, organized into canonical sections
Tokens give exact values. Prose tells agents *why* those values exist and how to
apply them. The CLI (`npx @google/design.md`) lints structure + WCAG contrast,
diffs versions for regressions, and exports to Tailwind or W3C DTCG JSON.
## When to use this skill
View the complete source on GitHubdraw-your-font
When your agent uses it
Turn a handwriting photo into an installable TTF font.
What it loads into context
Frontmatter
152 tokensAlways available to the agent
SKILL.md
1,876 tokensWhen the skill is activated
Reference documents
603 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Setup in Hermes (once per session)
- Decide the flow
- Template flow (best quality)
- Main flow: photo(s) → font
- Refine (conversational iteration)
- Legibility report (offer after delivering)
- Troubleshooting
Files
SKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesSKILL.mdcore6,978 bytesreferences/troubleshooting.mddoc2,398 bytes
Read SKILL.md
# draw-your-font
Photo of handwritten letters in → installable font out. You do the seeing
(find and label letters, judge quality); the CLI does all geometry (trace,
metrics, font assembly). Never edit SVG paths or coordinates yourself.
## Setup in Hermes (once per session)
The CLI is the pinned npm package `draw-your-font@0.1.0` — run it via npx (Node ≥ 18 required, no global install needed):
```bash
npx -y draw-your-font@0.1.0 --help
```
View the complete source on GitHubdream-loop
When your agent uses it
Build stunning 3D scenes via a concept-art fidelity loop.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
3,317 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure
- Follow-up loops
- Pitfalls
- Verification
Files
LICENSE.txtdoc1,070 bytesSKILL.mdcore13,931 bytes
Read SKILL.md
# Dream Loop Skill
An autonomous process for building extremely impressive visuals, especially 3D
scenes (games, apps, usually browser three.js/WebGL): generate photorealistic
concept art of the ideal result, build it, screenshot the live build, have a
judge score screenshot vs concept against a gated ladder, and iterate until
convergence. Goal: the most visually stunning result at an acceptable frame
rate for the target platform (e.g. 60 fps browser, 120 fps modern mobile).
This skill does NOT cover general web-app functionality, 2D UI design, or
non-visual quality — only the visual-fidelity loop.
## When to Use
View the complete source on GitHubexcalidraw
When your agent uses it
Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,275 tokensWhen the skill is activated
Reference documents
6,068 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Workflow
- Element Format Reference
Scripts executed separately
scripts/upload.py
Files
SKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesreferences/colors.mddoc1,777 bytesreferences/dark-mode.mddoc2,282 bytesreferences/examples.mddoc14,226 bytesscripts/upload.pyruns3,817 bytes
Read SKILL.md
# Excalidraw Diagram Skill
Create diagrams by writing standard Excalidraw element JSON and saving as `.excalidraw` files. These files can be drag-and-dropped onto [excalidraw.com](https://excalidraw.com) for viewing and editing. No accounts, no API keys, no rendering libraries -- just JSON.
## When to use
Generate `.excalidraw` files for architecture diagrams, flowcharts, sequence diagrams, concept maps, and more. Files can be opened at excalidraw.com or uploaded for shareable links.
## Workflow
1. **Load this skill** (you already did)
2. **Write the elements JSON** -- an array of Excalidraw element objects
3. **Save the file** using `write_file` to create a `.excalidraw` file
4. **Optionally upload** for a shareable link using `scripts/upload.py` via `terminal`View the complete source on GitHubheartmula
When your agent uses it
HeartMuLa: Suno-like song generation from lyrics + tags.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,768 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Hardware Requirements
- Installation Steps
- GPU / CUDA
- Usage
- Pitfalls
- Links
Files
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Read SKILL.md
# HeartMuLa - Open-Source Music Generation
## Overview
HeartMuLa is a family of open-source music foundation models (Apache-2.0) that generates music conditioned on lyrics and tags, with multilingual support. Generates full songs from lyrics + tags. Comparable to Suno for open-source. Includes:
- **HeartMuLa** - Music language model (3B/7B) for generation from lyrics + tags
- **HeartCodec** - 12.5Hz music codec for high-fidelity audio reconstruction
- **HeartTranscriptor** - Whisper-based lyrics transcription
- **HeartCLAP** - Audio-text alignment model
## When to Use
- User wants to generate music/songs from text descriptions
- User wants an open-source Suno alternative
- User wants local/offline music generation
- User asks about HeartMuLa, heartlib, or AI music generationView the complete source on GitHubhyperframes
When your agent uses it
Render MP4/WebM videos from HTML compositions.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
3,817 tokensWhen the skill is activated
Reference documents
12,206 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Quick Reference
- Setup (one-time)
- Procedure
- Cleanup
- Pitfalls
- Verification
- References
Scripts executed separately
scripts/setup.sh
Files
SKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesSKILL.mdcore15,650 bytesreferences/cli.mddoc8,788 bytesreferences/composition.mddoc7,747 bytesreferences/features.mddoc14,280 bytesreferences/gsap.mddoc5,705 bytesreferences/troubleshooting.mddoc6,709 bytesreferences/website-to-video.mddoc5,258 bytesscripts/setup.shruns5,174 bytes
Read SKILL.md
# HyperFrames
HTML is the source of truth for video. A composition is an HTML file with `data-*` attributes for timing, a GSAP timeline for animation, and CSS for appearance. The HyperFrames engine captures the page frame-by-frame and encodes to MP4/WebM with FFmpeg.
**Complement to `manim-video`:** Use `manim-video` for mathematical/geometric explainers (equations, 3B1B-style). Use `hyperframes` for motion-graphics, talking-head with captions, product tours, social overlays, shader transitions, and anything driven by real video/audio media.
## When to Use
- User asks for a rendered video from text, a script, or a website
- Animated title cards, lower thirds, or typographic intros
- Captioned narration video (TTS + captions synced to waveform)
- Audio-reactive visuals (beat sync, spectrum bars, pulsing glow)
- Scene-to-scene transitions (crossfade, wipe, shader warp, flash-through-white)
- Social overlays (Instagram/TikTok/YouTube style)View the complete source on GitHubimpeccable
When your agent uses it
Frontend design guidance, upstream-maintained (impeccable).
What it loads into context
Frontmatter
130 tokensAlways available to the agent
SKILL.md
429 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,549 bytes
Read SKILL.md
# Impeccable (upstream-maintained)
> **Catalog stub.** This entry is maintained upstream at
> [pbakaus/impeccable](https://github.com/pbakaus/impeccable): the project
> ships and verifies a Hermes-native skill bundle under `.hermes/skills/`.
> `hermes skills install impeccable` pulls the current bundle live from that
> repo (quarantined and scanned like any hub install) — this directory holds
> only the catalog metadata, so the vendored copy can never go stale.
Impeccable is a design language for AI coding agents: one skill exposing 23
sub-commands (`/impeccable init`, `craft`, `shape`, `critique`, `audit`,
`polish`, `bolder`, `quieter`, `distill`, `harden`, `onboard`, `animate`,
`colorize`, `typeset`, `layout`, `delight`, `overdrive`, `clarify`, `adapt`,
`optimize`, `extract`, `document`, `live`), explicit anti-pattern guidanceView the complete source on GitHubip-as-logo
When your agent uses it
Design minimal cute IP mascot marks readable at 32px.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
3,829 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Procedure
- Complexity budget
- Shape language and composition
- Simplicity and visual treatment
- Color and canvas
- Prompt skeleton
- Delivery behavior
- Pitfalls
- Verification
Files
LICENSEasset1,366 bytesSKILL.mdcore18,880 bytes
Read SKILL.md
# IP as Logo Skill
Create the simplest possible cute IP character: a compact, lovable symbol that remains recognizable at `32 × 32`, not a detailed character illustration.
> **Hermes adaptation notes** (the rest of this document is the upstream
> workflow, kept intact — snapshot of
> [s1dashu/ip-as-logo-skill](https://github.com/s1dashu/ip-as-logo-skill)
> commit [`b1bf517c`](https://github.com/s1dashu/ip-as-logo-skill/commit/b1bf517c54a407452cfaca98a54668cd052f8e63),
> Aug 20 2026, MIT — see `LICENSE`):
>
> - **Image generation path**: use the built-in `image_generate` tool with
> `aspect_ratio="square"`. The active backend is user-configured — do not
> pick or switch models. Modern instruction-following backends (GPT Image,
> Seedream, FLUX, Grok Imagine) take the full prompt skeleton with theView the complete source on GitHubkanban-video-orchestrator
When your agent uses it
Plan and run multi-agent video production pipelines.
What it loads into context
Frontmatter
232 tokensAlways available to the agent
SKILL.md
2,309 tokensWhen the skill is activated
Reference documents
15,710 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When NOT to use this skill
- Workflow
- Reference: worked examples
- Critical rules
- File map
Scripts executed separately
../../../hermesassets/setup.sh.tmplscripts/bootstrap_pipeline.pyscripts/monitor.py
Files
../../../hermesruns262 bytesSKILL.mdcore9,777 bytesSKILL.mdcore9,777 bytesSKILL.mdcore9,777 bytesassets/brief.md.tmplasset2,109 bytesassets/setup.sh.tmplruns8,431 bytesassets/soul.md.tmplasset631 bytesreferences/examples.mddoc10,343 bytesreferences/intake.mddoc7,951 bytesreferences/kanban-setup.mddoc11,416 bytesreferences/monitoring.mddoc7,463 bytesreferences/role-archetypes.mddoc12,729 bytesreferences/tool-matrix.mddoc13,717 bytesscripts/bootstrap_pipeline.pyruns18,601 bytesscripts/monitor.pyruns6,830 bytes
Read SKILL.md
# Kanban Video Orchestrator
Wrap any video request — from a 15-second product teaser to a 5-minute narrative
short to a music video to an ASCII loop — in a Hermes Kanban pipeline that
decomposes the work to specialized agent profiles.
This skill does **not** render anything itself. It is a meta-pipeline that:
1. **Scopes** the request through targeted discovery
2. **Designs** an appropriate team (which roles, which tools per role) based on the style
3. **Generates** a setup script that creates Hermes profiles, project workspace, and the initial kanban task
4. **Hands off** to the director profile, which decomposes via the kanban
5. **Monitors** execution, helps intervene when tasks stall or fail
View the complete source on GitHubmeme-generation
When your agent uses it
Create meme PNGs from templates with Pillow text overlay.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,402 tokensWhen the skill is activated
Reference documents
301 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Available Templates
- Procedure
- Examples
- Listing Templates
- Pitfalls
- Verification
Scripts executed separately
scripts/.gitignorescripts/generate_meme.py
Files
EXAMPLES.mddoc1,171 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesscripts/.gitignoreruns8 bytesscripts/generate_meme.pyruns16,645 bytesscripts/templates.jsonasset4,154 bytes
Read SKILL.md
# Meme Generation
Generate actual meme images from a topic. Picks a template, writes captions, and renders a real .png file with text overlay.
## When to Use
- User asks you to make or generate a meme
- User wants a meme about a specific topic, situation, or frustration
- User says "meme this" or similar
## Available Templates
The script supports **any of the ~100 popular imgflip templates** by name or ID, plus 10 curated templates with hand-tuned text positioning.
View the complete source on GitHubmono-color
When your agent uses it
Generate one- or two-ink editorial print poster images.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
2,650 tokensWhen the skill is activated
Reference documents
4,544 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure
- Pitfalls
- Verification
- Notice
Files
LICENSE.txtdoc1,064 bytesSKILL.mdcore11,854 bytesSKILL.mdcore11,854 bytesSKILL.mdcore11,854 bytesSKILL.mdcore11,854 bytesSKILL.mdcore11,854 bytesdesign-system/carriers.jsonasset1,619 bytesdesign-system/colors.jsonasset4,341 bytesdesign-system/compositions.jsonasset2,378 bytesdesign-system/imperfections.jsonasset1,566 bytesdesign-system/rhythm.jsonasset2,800 bytesdesign-system/typography.jsonasset4,240 bytesreferences/composition.mddoc6,288 bytesreferences/quality-gate.mddoc4,418 bytesreferences/visual-language.mddoc11,054 bytes
Read SKILL.md
# Mono-Color Editorial Print Skill
Turn any user theme, sentence, or reference photo into an original printed editorial artifact with one stable visual language: adaptive neutral substrate + one or two inks + mechanically reproduced image + typographic tension + concise human voice.
This skill designs and generates the image; it does not imitate any one reference, copy a source composition, wording, logo, or artwork, and it never uses more than two printing inks.
## When to Use
The user asks for a monochrome editorial poster, duotone print, risograph/zine poster, halftone photo treatment, one-ink or two-ink cover, or names the mono-color style. Chinese trigger vocabulary includes 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、网点照片、复古或当代编辑排版. Do not trigger merely because a request mentions a color.
## Prerequisites
- The Hermes `image_generate` tool (search/describe it via the deferred-tool catalog if not loaded). If image generation is unavailable, deliver prompt-only and say so.
- The `design-system/` catalogs bundled with this skill (see Quick Reference).View the complete source on GitHubpixel-art
When your agent uses it
Pixel art w/ era palettes (NES, Game Boy, PICO-8).
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
2,094 tokensWhen the skill is activated
Reference documents
1,154 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Workflow
- Preset Catalog
- Scene Catalog (for video)
- Invocation Patterns
- Pipeline Rationale
- Dependencies
- Pitfalls
- Verification
- Attribution
Scripts executed separately
scripts/__init__.pyscripts/palettes.pyscripts/pixel_art.pyscripts/pixel_art_video.py
Files
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Read SKILL.md
# Pixel Art
Convert any image into retro pixel art, then optionally animate it into a short
MP4 or GIF with era-appropriate effects (rain, fireflies, snow, embers).
Two scripts ship with this skill:
- `scripts/pixel_art.py` — photo → pixel-art PNG (Floyd-Steinberg dithering)
- `scripts/pixel_art_video.py` — pixel-art PNG → animated MP4 (+ optional GIF)
Each is importable or runnable directly. Presets snap to hardware palettes
when you want era-accurate colors (NES, Game Boy, PICO-8, etc.), or use
adaptive N-color quantization for arcade/SNES-style looks.
View the complete source on GitHubpretext
When your agent uses it
Build creative browser demos with DOM-free text layout.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
3,471 tokensWhen the skill is activated
Reference documents
2,931 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Creative Standard
- Stack
- The Two Use Cases
- Demo Recipe Patterns
- Workflow
- Performance Notes
- Common Pitfalls
- Verification Checklist
- Reference: Community Demos
Files
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# Pretext Creative Demos
## Overview
[`@chenglou/pretext`](https://github.com/chenglou/pretext) is a 15KB zero-dependency TypeScript library by Cheng Lou (React core, ReasonML, Midjourney) for **DOM-free multiline text measurement and layout**. It does one thing: given `(text, font, width)`, return the line breaks, per-line widths, per-grapheme positions, and total height — all via canvas measurement, no reflow.
That sounds like plumbing. It is not. Because it is fast and geometric, it is a **creative primitive**: you can reflow paragraphs around a moving sprite at 60fps, build games whose level geometry is made of real words, drive ASCII logos through prose, shatter text into particles with exact per-grapheme starting positions, or pack shrink-wrapped multiline UI without any `getBoundingClientRect` thrash.
This skill exists so Hermes can make **cool demos** with it — the kind people post to X. See `pretext.cool` and `chenglou.me/pretext` for the community demo corpus.
## When to Use
Use when the user asks for:
- A "pretext demo" / "cool pretext thing" / "text-as-X"View the complete source on GitHubsimple-english
When your agent uses it
Rewrite text to ASD-STE100 Simplified Technical English.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
5,148 tokensWhen the skill is activated
Reference documents
1,675 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- How to use it in Hermes
- Your Task
- Two Modes
- Step 1: Classify the Text
- THE RULE CATALOG
- VOCABULARY DISCIPLINE
- Untouchables
- Beyond Documentation
- Self-Check Before You Deliver
- Full Example
- Limits
- References
Files
SKILL.mdcore20,542 bytesreferences/checklist.mddoc2,971 bytesreferences/use-cases.mddoc4,053 bytes
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# Simple English: Write Like an Aerospace Manual
Write technical text with the rules of ASD-STE100 Simplified Technical English. STE is the controlled language that aerospace and defense manufacturers use for maintenance documentation. The rules exist so that a tired reader who is not a native English speaker cannot misread an instruction. They remove the usual signs of AI-generated text as a side effect: long sentences, synonym rotation, hedges, filler, and decorative clauses.
Write for that tired reader. Each sentence must survive one read.
## How to use it in Hermes
The text usually arrives one of three ways:
1. **Inline.** The user pastes the text into the message. Rewrite it in place and reply with the result.
2. **File.** The user points at a file (README, runbook, docs page). Use `read_file` to load it, then `patch` for targeted section rewrites or `write_file` for a full rewrite. Never touch code blocks, identifiers, or quoted errors (see Untouchables).
3. **Check mode.** The user asks you to audit text for STE compliance instead of rewriting it. Report each violation as rule number + offending text + compliant rewrite, using `references/checklist.md`.
View the complete source on GitHubsketch
When your agent uses it
Throwaway HTML mockups: 2-3 design variants to compare.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
2,440 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When NOT to use this
- If the user has the full GSD system installed
- Core method
- Theming (when the project has a visual identity)
- Interactivity bar
- Frontier mode (picking what to sketch next)
- Output
- Attribution
Files
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# Sketch
Use this skill when the user wants to **see a design direction before committing** to one — exploring a UI/UX idea as disposable HTML mockups. The point is to generate 2-3 interactive variants so the user can compare visual directions side-by-side, not to produce shippable code.
Load this when the user says things like "sketch this screen", "show me what X could look like", "compare layout A vs B", "give me 2-3 takes on this UI", "let me see some variants", "mockup this before I build".
## When NOT to use this
- User wants a production component — use `claude-design` or build it properly
- User wants a polished one-off HTML artifact (landing page, deck) — `claude-design`
- User wants a diagram — `excalidraw`, `architecture-diagram`
- The design is already locked — just build it
## If the user has the full GSD system installedView the complete source on GitHubsocial-media-content-calendar
When your agent uses it
Plan multi-platform social campaigns: briefs to posting.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
814 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Pitfalls
- Verification
Files
SKILL.mdcore3,970 bytesSKILL.mdcore3,970 bytes
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# Social Media Content Calendar
Plan a concrete calendar across selected social platforms. This skill owns campaign structure, post briefs, channel adaptation, approvals, and publishing verification; platform skills such as `xurl` own API commands. For platforms without a connector, the verified handoff ends at approved drafts for the user's scheduler — say so rather than claiming publication.
## When to Use
- "Build next month's social calendar."
- "Turn this launch into posts for X, LinkedIn, Instagram, and TikTok."
- "Draft and schedule a campaign."
- "Repurpose these articles/videos into social content."
Don't use for: single one-off posts (use the platform skill directly).
## ProcedureView the complete source on GitHubsystem-atlas
When your agent uses it
Build explorable isometric architecture atlases as HTML.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
2,091 tokensWhen the skill is activated
Reference documents
2,501 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Publishing
- Pitfalls
- Verification
Scripts executed separately
assets/build.mjsassets/data.example.mjs
Files
LICENSE.txtdoc1,069 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesassets/build.mjsruns5,680 bytesassets/data.example.mjsruns5,894 bytesassets/template.htmlasset38,247 bytesreferences/design-language.mddoc4,823 bytesreferences/process-and-lessons.mddoc5,297 bytes
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# System Atlas Skill
An atlas is one data file (`data.mjs`) that renders two views: an **interactive isometric map** (a single self-contained `atlas.html` — hover to read, click to pin, go inside for steps, moving data packets you can inspect, chapters that reveal the system a few structures at a time), and a **generated text twin** (`SYSTEM.md`) with the decisions table, every structure, the flows, and the open questions by ID. The data file is the only thing anyone edits; both views rebuild from it. It sits beside a hand-written glossary (`CONTEXT.md`) and ADRs.
**Does:** interactive architecture maps with progressive disclosure, question tracking across feedback rounds, a generated text twin, and a repeatable update loop.
**Doesn't:** static one-off diagrams (use the architecture-diagram or excalidraw skill), finished systems that only need a README, or a single diagram for a PR.
## When to Use
Use whenever someone wants to discuss, design, review, or explain an architecture visually — "make an atlas", "map the system", "make the architecture explorable", "visualize the codebase/agent/pipeline so we can talk about it", "a diagram I can click around", "walk me through how it fits together" — or when an architecture discussion is producing a pile of open questions that need tracking across feedback rounds. Also use it to update an existing atlas after decisions change. Best when the system is new enough that vocabulary, decisions, and questions are still moving and there will be more than one feedback round.
## Prerequisites
- Node.js (any recent version; the build uses only `node:fs`, `node:path`, `node:url` — no npm install needed).View the complete source on GitHubtldraw-offline
When your agent uses it
Drive and script tldraw offline canvases with an agent.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
4,009 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Interactive UI (clickable buttons that drive state)
- Procedure
- Shape props (validated against tldraw SDK v5 schema)
- Pitfalls
- Verification
Scripts executed separately
scripts/counter.jsscripts/main.jsscripts/validate_shapes.mjs
Files
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Read SKILL.md
# tldraw offline Skill
Work with the tldraw offline desktop app (offline.tldraw.com): read the open
canvas, make edits, and write **document scripts** — JavaScript embedded in a
`.tldraw` file that runs on load and gives the file durable behavior. The app
runs a **local HTTP API** (default `localhost:7236`) that a coding agent drives
with plain `curl` from its terminal — this is exactly how the app's own homepage
demo (Codex editing a canvas live) works. The agent does NOT use computer-use /
GUI clicking, and does NOT hand-edit the `.tldraw` file directly. Keep tldraw
offline open while you work.
## When to Use
- The user has tldraw offline open and asks you to build or modify a canvasView the complete source on GitHubunreal-mcp
When your agent uses it
Automate Unreal Engine editor scenes, actors, and renders.
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
3,254 tokensWhen the skill is activated
Reference documents
18,369 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- The Tool Surface: Discovery, Not a Fixed List
- Operating Loop
- From Plain English to a Scene
- Reference Files
- Pitfalls (top of mind — full list in references/pitfalls.md)
- Verification Checklist
Files
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# Unreal Engine MCP Skill
Companion skill for the `unreal-engine` entry in the Hermes MCP catalog. The
MCP server (Epic's official, experimental "Unreal MCP" plugin, internal id
`ModelContextProtocol`) runs INSIDE the Unreal Editor process and exposes
editor functionality as typed tools. This skill teaches how to drive it well:
discovering the live tool surface, sequencing calls safely, translating
plain-English asks into scenes that actually look good, and verifying work
visually. The user should never need to touch the editor beyond launching it.
## When to Use
Use when the user wants anything done in Unreal Engine: build or dress a
level, spawn/move/delete actors, set up lighting and atmosphere, create orView the complete source on GitHubjupyter-notebook
When your agent uses it
Iterative Python via live Jupyter kernel (hamelnb).
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
1,535 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This vs Other Tools
- Prerequisites
- Setup
- Core Workflow
- Practical Tips from Experience
- Timeout Defaults
Files
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# Jupyter Notebook (hamelnb live kernel)
Gives you a **stateful Python REPL** via a live Jupyter kernel. Variables persist
across executions. Use this instead of `execute_code` when you need to build up
state incrementally, explore APIs, inspect DataFrames, or iterate on complex code.
## When to Use This vs Other Tools
| Tool | Use When |
|------|----------|
| **This skill** | Iterative exploration, state across steps, data science, ML, "let me try this and check" |
| `execute_code` | One-shot scripts needing hermes tool access (web_search, file ops). Stateless. |
| `terminal` | Shell commands, builds, installs, git, process management |
View the complete source on GitHubactual-setup
When your agent uses it
Set up Actual Computer (actual.inc) inference in Hermes.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,810 tokensWhen the skill is activated
Reference documents
1,038 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Pitfalls
- Verification
Scripts executed separately
../../../hermes
Files
../../../hermesruns262 bytesSKILL.mdcore6,767 bytesSKILL.mdcore6,767 bytesSKILL.mdcore6,767 bytesSKILL.mdcore6,767 bytesreferences/opencode.mddoc3,720 bytes
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# Actual Computer Setup Skill
Sets up [actual.inc](https://actual.inc) (Actual Computer) as a Hermes inference
provider. Actual turns the user's own hardware into a private inference cluster
and exposes an OpenAI-compatible API two ways: a hosted end-to-end-encrypted
relay at `https://api.actual.inc` (authenticated with an `ac_` key), and a local
on-device daemon at `http://127.0.0.1:8080` (no auth on loopback). This skill
does not install the Actual daemon for the user — device authorization requires
a human in a browser.
## When to Use
- User wants to add actual.inc as an inference provider (cloud relay or local).
- User has an `ac_` key and wants Hermes routed through their Actual cluster.View the complete source on GitHubdocker-management
When your agent uses it
Manage Docker containers, images, volumes, and Compose.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,434 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure
- Pitfalls
- Verification
- Dockerfile Optimization Tips
Files
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# Docker Management
Manage Docker containers, images, volumes, networks, and Compose stacks using standard Docker CLI commands. No additional dependencies beyond Docker itself.
## When to Use
- Run, stop, restart, remove, or inspect containers
- Build, pull, push, tag, or clean up Docker images
- Work with Docker Compose (multi-service stacks)
- Manage volumes or networks
- Debug a crashing container or analyze logs
- Check Docker disk usage or free up space
- Review or optimize a Dockerfile
View the complete source on GitHubhermes-s6-container-supervision
When your agent uses it
Modify or debug s6 services in the Hermes Docker image.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,132 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Architecture at a glance
- Key files
- Why Architecture B (CMD as main program, not s6-supervised)
- Quick recipes
- Common pitfalls
- Related skills
Scripts executed separately
../../../scripts/run_tests.sh
Files
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# Hermes s6-overlay Container Supervision
## When to use this skill
Load this skill when you're working on:
- Adding or removing a static service in the Hermes Docker image (something that should be supervised at every container start, like the dashboard)
- Diagnosing why a per-profile gateway isn't starting, restarting, or surviving `docker restart`
- Understanding why the container's CMD is `/opt/hermes/docker/main-wrapper.sh` and how leading-dash args reach the user's program
- Modifying `cont-init.d` boot scripts (UID remap, volume seeding, profile reconciliation)
- Changing the rendered run-script for per-profile gateways (Phase 4)
If you're just running the Hermes Agent and want to use Docker, see `website/docs/user-guide/docker.md` instead.
## Architecture at a glanceView the complete source on GitHubinference-sh-cli
When your agent uses it
Run 150+ AI apps (image, video, LLM) via inference.sh CLI.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,250 tokensWhen the skill is activated
Reference documents
2,893 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Workflow
- Common Commands
- Pitfalls
- Reference Docs
Files
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# inference.sh CLI
Run 150+ AI apps in the cloud with a simple CLI. No GPU required.
All commands use the **terminal tool** to run `infsh` commands.
## When to Use
- User asks to generate images (FLUX, Reve, Seedream, Grok, Gemini image)
- User asks to generate video (Veo, Wan, Seedance, OmniHuman)
- User asks about inference.sh or infsh
- User wants to run AI apps without managing individual provider APIs
- User asks for AI-powered search (Tavily, Exa)
- User needs avatar/lipsync generationView the complete source on GitHubpinggy-tunnel
When your agent uses it
Zero-install localhost tunnels over SSH via Pinggy.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,099 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Procedure — Start a Tunnel and Get the URL
- Access Control via Username Keywords
- Web Debugger (optional)
- Pitfalls
- Recipes
- Verification
Files
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# Pinggy Tunnel Skill
Expose a local service (dev server, webhook receiver, MCP endpoint, demo) to the public internet using a Pinggy SSH reverse tunnel. No daemon to install — the user's stock SSH client connects to `a.pinggy.io:443` and Pinggy hands back a public HTTP/HTTPS URL.
Free tier: 60-minute tunnels, random subdomain, no signup. Pro tier ($3/mo) is an opt-in with a token.
## When to Use
- User asks to "expose this locally", "share my dev server", "make this URL public", "tunnel port N", "get a public URL for a webhook"
- Need to receive a webhook callback during a local task (Stripe, GitHub, Discord, AgentMail)
- Sharing a one-off HTTP demo (MCP server, Ollama/vLLM endpoint, dashboard) with a remote party
- The host has SSH but no `cloudflared` / `ngrok` binary, and installing one would be overkill
If the host already has `cloudflared` configured, prefer the `cloudflared-quick-tunnel` skill — Cloudflare quick tunnels don't expire after 60 minutes.View the complete source on GitHubsetup-wizard-generator
When your agent uses it
Generate a bash wizard guiding a human through manual setup.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,236 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Procedure
- Pitfalls
- Verification
Scripts executed separately
templates/template.sh
Files
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# Setup Wizard Generator
Generates an interactive bash **wizard**: a script that walks a human, step
by step, through a manual procedure that is tedious to do by hand and tedious
to re-explain every time. It opens each URL, says exactly what to click and
copy, captures the values, writes them where they belong (`.env`, GitHub
secrets), confirms at every stage, and shows how many stages are left.
Ported from mattpocock/skills' MIT-licensed `wizard` skill.
## When to Use
- Provisioning infrastructure or third-party services (Stripe, Supabase,
DNS, OAuth apps) where only a human can click through the dashboardView the complete source on GitHubwatchers
When your agent uses it
Poll RSS, JSON APIs, and GitHub with watermark dedup.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,144 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Mental model
- Ready-made scripts
- Usage
- Wiring into cron
- State files
- Writing your own
- Common Pitfalls
Scripts executed separately
scripts/_watermark.pyscripts/watch_github.pyscripts/watch_http_json.pyscripts/watch_rss.py
Files
SKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesSKILL.mdcore4,500 bytesscripts/_watermark.pyruns5,241 bytesscripts/watch_github.pyruns6,203 bytesscripts/watch_http_json.pyruns4,589 bytesscripts/watch_rss.pyruns4,114 bytes
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# Watchers
Poll external sources on an interval and react only to new items. Three ready-made scripts plus a shared watermark helper; wire them into a cron job (or run them ad-hoc from the terminal).
## When to Use
- User wants to watch an RSS/Atom feed and be notified of new entries
- User wants to watch a GitHub repo's issues / pulls / releases / commits
- User wants to poll an arbitrary JSON endpoint and get notified on new items
- User asks for "a watcher for X" or "notify me when X changes"
## Mental model
A watcher is just a script that:View the complete source on GitHubadversarial-ux-test
When your agent uses it
Roleplay a hostile user to find and triage UX pain points.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,136 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Works
- How to Use
- Step 1: Define the Persona
- Step 2: Become the Asshole (Browse as the Persona)
- Step 3: The Rant (Write Feedback in Character)
- Step 4: The Pragmatism Filter (Critical — Do Not Skip)
- Step 5: Create Tickets
- Step 6: Report
- Tips
- Example Personas by Industry
- Rules
Files
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# Adversarial UX Test
Roleplay the worst-case user for your product — the person who hates technology, doesn't want your software, and will find every reason to complain. Then filter their feedback through a pragmatism layer to separate real UX problems from "I hate computers" noise.
Think of it as an automated "mom test" — but angry.
## Why This Works
Most QA finds bugs. This finds **friction**. A technically correct app can still be unusable for real humans. The adversarial persona catches:
- Confusing terminology that makes sense to developers but not users
- Too many steps to accomplish basic tasks
- Missing onboarding or "aha moments"
- Accessibility issues (font size, contrast, click targets)
- Cold-start problems (empty states, no demo content)View the complete source on GitHubagentmail
When your agent uses it
Use when an agent needs AgentMail CLI email inboxes.
What it loads into context
Frontmatter
180 tokensAlways available to the agent
SKILL.md
784 tokensWhen the skill is activated
Reference documents
1,717 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Files
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# AgentMail Skill
AgentMail gives an agent its own email inbox for sending mail, receiving
replies, completing email OTP flows, and running inbound email loops. Use it for
agent-owned inboxes, not a user's existing IMAP/SMTP mailbox.
Use the `agentmail` CLI first. Use MCP only when the harness expects MCP tools;
use REST only when the CLI is missing a required operation.
## When to Use
- The agent needs an email address it owns.
- The task involves email OTP flows, replies, threads, labels, or attachments.
- The agent needs webhook or WebSocket delivery for inbound mail.View the complete source on GitHub3-statement-model
When your agent uses it
Build integrated IS/BS/CF financial workbooks in Excel.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
4,800 tokensWhen the skill is activated
Reference documents
4,291 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- ⚠<U+FE0F> CRITICAL PRINCIPLES — Read Before Populating Any Template
- Formatting — Professional Blue/Grey Palette (Default unless template/user specifies otherwise)
- Model Structure
- Margin Analysis
- Credit Metrics
- Scenario Analysis (Base / Upside / Downside)
- SEC Filings Data Extraction
- Completing Model Templates
- Model Validation and Audit
- Data sources — MCP first, web fallback
- Attribution
Files
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## Environment
This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
# 3-Statement Financial Model Template Completion
Complete and populate integrated financial model templates with proper linkages between Income Statement, Balance Sheet, and Cash Flow Statement.
## ⚠<U+FE0F> CRITICAL PRINCIPLES — Read Before Populating Any Template
**Formulas over hardcodes (non-negotiable):**
- Every projection cell, roll-forward, linkage, and subtotal MUST be an Excel formula — never a pre-computed valueView the complete source on GitHubcomps-analysis
When your agent uses it
Build comparable-company valuation workbooks in Excel.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
7,303 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- ⚠<U+FE0F> CRITICAL: Data Source Priority (READ FIRST)
- Overview
- Core Philosophy
- ⚠<U+FE0F> CRITICAL: Formulas Over Hardcodes + Step-by-Step Verification
- Section 1: Document Structure & Setup
- Section 2: Operating Statistics & Financial Metrics
- Section 3: Valuation Multiples & Investment Metrics
- Section 4: Notes & Methodology Documentation
- Section 5: Choosing the Right Metrics (Decision Framework)
- Section 6: Best Practices & Quality Checks
- Section 6: Advanced Features
- Section 7: Workflow & Practical Tips
- Section 8: Example Template Layout
- Section 9: Industry-Specific Additions (Optional)
- Section 10: Red Flags & Warning Signs
- Section 11: Formulas Reference Guide
- Key Principles Summary
- Output Checklist
- Continuous Improvement
- Data sources — MCP first, web fallback
- Attribution
Files
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## Environment
This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
# Comparable Company Analysis
## ⚠<U+FE0F> CRITICAL: Data Source Priority (READ FIRST)
**ALWAYS follow this data source hierarchy:**
1. **FIRST: Check for MCP data sources** - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information
2. **DO NOT use web search** if the above MCP data sources are availableView the complete source on GitHubdcf-model
When your agent uses it
Build discounted cash flow valuation workbooks in Excel.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
12,812 tokensWhen the skill is activated
Reference documents
329 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- Overview
- Tools
- Critical Constraints - Read These First
- DCF Process Workflow
- Excel File Creation
- Quality Rubric
- Input Requirements
- Excel Model Structure
- Case Selector Implementation
- Deliverables Structure
- Best Practices
- Common Variations
- Troubleshooting
- Workflow Integration
- Final Output Checklist
- Data sources — MCP first, web fallback
- Attribution
Scripts executed separately
scripts/validate_dcf.py
Files
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## Environment
This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
# DCF Model Builder
## Overview
This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).
## Tools
View the complete source on GitHubexcel-author
When your agent uses it
Build auditable financial workbooks headless via openpyxl.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
3,070 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Output contract
- Setup
- Core conventions (non-negotiable)
- Skeleton: typical financial model
- Section headers with merged cells
- Sensitivity tables
- Recalculating before delivery
- Model layout planning
- Verify step-by-step with the user
- When NOT to use this skill
- Attribution
Scripts executed separately
scripts/recalc.py
Files
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# excel-author
Produce an .xlsx file on disk using `openpyxl`. Follow the banker-grade conventions below so the model is auditable, flexible, and reviewable by someone other than the person who built it.
Adapted from Anthropic's `xlsx-author` and `audit-xls` skills in the [anthropics/financial-services](https://github.com/anthropics/financial-services) repo. The MCP / Office-JS / Cowork-specific branches of the originals are dropped — this skill assumes headless Python.
## Output contract
- Write to `./out/<name>.xlsx`. Create `./out/` if it does not exist.
- Return the relative path in your final message so downstream tools can pick it up.
- One logical model per file. Do not append to an existing workbook unless explicitly asked.
## Setup
View the complete source on GitHublbo-model
When your agent uses it
Build leveraged buyout workbooks with IRR/MOIC in Excel.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,838 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- TEMPLATE REQUIREMENT
- CRITICAL INSTRUCTIONS — READ FIRST
- TEMPLATE ANALYSIS PHASE - DO THIS FIRST
- FILLING FORMULAS - GENERAL APPROACH
- COMMON PROBLEM AREAS
- VERIFICATION CHECKLIST - RUN AFTER COMPLETION
- COMMON ERRORS TO AVOID
- WORKING WITH THE USER — SECTION-BY-SECTION CHECKPOINTS
- Data sources — MCP first, web fallback
- Attribution
Files
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## Environment
This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
---
## TEMPLATE REQUIREMENT
**This skill uses templates for LBO models. Always check for an attached template file first.**
Before starting any LBO model:
1. **If a template file is attached/provided**: Use that template's structure exactly - copy it and populate with the user's dataView the complete source on GitHubmerger-model
When your agent uses it
Build M&A accretion/dilution workbooks in Excel.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,384 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Environment
- Workflow
- Important Notes
- Data sources — MCP first, web fallback
- Attribution
Files
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## Environment
This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
# Merger Model
Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms.
## Workflow
### Step 1: Gather Inputs
View the complete source on GitHubpolymarket
When your agent uses it
Query Polymarket: markets, prices, orderbooks, history.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
747 tokensWhen the skill is activated
Reference documents
1,387 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Key Concepts
- Three Public APIs
- Typical Workflow
- Presenting Results
- Parsing Double-Encoded Fields
- Rate Limits
- Limitations
Scripts executed separately
scripts/polymarket.py
Files
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# Polymarket — Prediction Market Data
Query prediction market data from Polymarket using their public REST APIs.
All endpoints are read-only and require zero authentication.
See `references/api-endpoints.md` for the full endpoint reference with curl examples.
## When to Use
- User asks about prediction markets, betting odds, or event probabilities
- User wants to know "what are the odds of X happening?"
- User asks about Polymarket specifically
- User wants market prices, orderbook data, or price history
- User asks to monitor or track prediction market movementsView the complete source on GitHubpptx-author
When your agent uses it
Build PowerPoint decks headless with python-pptx.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,284 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Output contract
- Setup
- Core conventions
- Skeleton
- Binding deck numbers to the source workbook
- Slide-type checklist for pitch decks
- When NOT to use this skill
- Attribution
Files
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# pptx-author
Produce a .pptx file on disk using `python-pptx`. Use when you need to deliver a deck as a file artifact, not drive a live PowerPoint session.
Adapted from Anthropic's `pptx-author` and `pitch-deck` skills in [anthropics/financial-services](https://github.com/anthropics/financial-services). The MCP / Office-JS branches of the originals are dropped — this assumes headless Python.
For the broader, already-shipped PowerPoint authoring skill (slides, speaker notes, embeds, media), see the built-in `microsoft-office` skill. This skill is a lighter-weight pattern tuned for model-backed decks (pitch decks, IC memos, earnings notes) where every number must trace to a source workbook.
## Output contract
- Write to `./out/<name>.pptx`. Create `./out/` if it does not exist.
- Return the relative path in your final message.
## SetupView the complete source on GitHubstocks
When your agent uses it
Stock quotes, history, search, compare, crypto via Yahoo.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
746 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Commands
- Pitfalls
- Verification
Scripts executed separately
scripts/stocks_client.py
Files
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# Stocks Skill
Read-only market data via Yahoo Finance. Five commands: `quote`, `search`,
`history`, `compare`, `crypto`. Python stdlib only — no API key, no pip
installs. Yahoo's endpoint is unofficial and may rate-limit or change.
## When to Use
- User asks for a current stock price (AAPL, TSLA, MSFT, ...)
- User wants to look up a ticker by company name
- User wants OHLCV history or performance over a date range
- User wants to compare several tickers side by side
- User asks for a crypto price (BTC, ETH, SOL, ...)
View the complete source on GitHubminecraft-modpack-server
When your agent uses it
Host modded Minecraft servers (CurseForge, Modrinth).
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
1,880 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Gather User Preferences First
- Steps
- Pitfalls
- Verification
Files
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# Minecraft Modpack Server Setup
## When to use
- User wants to set up a modded Minecraft server from a server pack zip
- User needs help with NeoForge/Forge server configuration
- User asks about Minecraft server performance tuning or backups
## Gather User Preferences First
Before starting setup, ask the user for:
- **Server name / MOTD** — what should it say in the server list?
- **Seed** — specific seed or random?
- **Difficulty** — peaceful / easy / normal / hard?
- **Gamemode** — survival / creative / adventure?
- **Online mode** — true (Mojang auth, legit accounts) or false (LAN/cracked friendly)?View the complete source on GitHubpokemon-player
When your agent uses it
Play Pokemon via headless emulator + RAM reads.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
2,198 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Startup Procedure
- Save and Load
- The Gameplay Loop
- Action Reference
- Critical Tips from Experience
- Battle Strategy
- Memory Conventions
- Progression Milestones
- Stopping Play
- Pitfalls
Files
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# Pokemon Player
Play Pokemon games via headless emulation using the `pokemon-agent` package.
## When to Use
- User says "play pokemon", "start pokemon", "pokemon game"
- User asks about Pokemon Red, Blue, Yellow, FireRed, etc.
- User wants to watch an AI play Pokemon
- User references a ROM file (.gb, .gbc, .gba)
## Startup Procedure
### 1. First-time setup (clone, venv, install)
The repo is NousResearch/pokemon-agent on GitHub. Clone it, then
set up a Python 3.10+ virtual environment. Use uv (preferred for speed)View the complete source on GitHubfitness-nutrition
When your agent uses it
Workout planning, macros, and body metrics via wger/USDA.
What it loads into context
Frontmatter
202 tokensAlways available to the agent
SKILL.md
2,910 tokensWhen the skill is activated
Reference documents
1,061 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Pitfalls
- Verification
- Quick Reference
Scripts executed separately
scripts/body_calc.pyscripts/nutrition_search.py
Files
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# Fitness & Nutrition
Expert fitness coach and sports nutritionist skill. Two data sources
plus offline calculators — everything a gym-goer needs in one place.
**Data sources (all free, no pip dependencies):**
- **wger** (https://wger.de/api/v2/) — open exercise database, 690+ exercises with muscles, equipment, images. Public endpoints need zero authentication.
- **USDA FoodData Central** (https://api.nal.usda.gov/fdc/v1/) — US government nutrition database, 380,000+ foods. `DEMO_KEY` works instantly; free signup for higher limits.
**Offline calculators (pure stdlib Python):**
- BMI, TDEE (Mifflin-St Jeor), one-rep max (Epley/Brzycki/Lombardi), macro splits, body fat % (US Navy method)
View the complete source on GitHubneuroskill-bci
When your agent uses it
Use live BCI cognitive and mood state from NeuroSkill.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
4,395 tokensWhen the skill is activated
Reference documents
10,046 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- CLI Reference: `npx neuroskill <command>`
- 1. Checking Current State
- 2. Session Analysis
- 3. Historical Search
- 4. Session Comparison
- 5. Sleep Data
- 6. Labeling Moments
- 7. Real-Time Streaming
- 8. UMAP Visualization
- 9. Proactive State Awareness
- 10. Suggesting Protocols
- 11. Additional Tools
- Error Handling
- Example Interactions
- References
Files
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# NeuroSkill BCI Integration
Connect Hermes to a running [NeuroSkill](https://neuroskill.com/) instance to read
real-time brain and body metrics from a BCI wearable. Use this to give
cognitively-aware responses, suggest interventions, and track mental performance
over time.
> **⚠<U+FE0F> Research Use Only** — NeuroSkill is an open-source research tool. It is
> NOT a medical device and has NOT been cleared by the FDA, CE, or any regulatory
> body. Never use these metrics for clinical diagnosis or treatment.
See `references/metrics.md` for the full metric reference, `references/protocols.md`
for intervention protocols, and `references/api.md` for the WebSocket/HTTP API.
View the complete source on GitHubfastmcp
When your agent uses it
Build, test, and deploy Python MCP servers.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
1,897 tokensWhen the skill is activated
Reference documents
595 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Included Files
- Workflow
- Common Patterns
- Quality Bar
- Troubleshooting
- References
Scripts executed separately
scripts/scaffold_fastmcp.pytemplates/api_wrapper.pytemplates/database_server.pytemplates/file_processor.py
Files
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# FastMCP
Build MCP servers in Python with FastMCP, validate them locally, install them into MCP clients, and deploy them as HTTP endpoints.
## When to Use
Use this skill when the task is to:
- create a new MCP server in Python
- wrap an API, database, CLI, or file-processing workflow as MCP tools
- expose resources or prompts in addition to tools
- smoke-test a server with the FastMCP CLI before wiring it into Hermes or another client
- install a server into Claude Code, Claude Desktop, Cursor, or a similar MCP client
- prepare a FastMCP server repo for HTTP deploymentView the complete source on GitHubmcp-oauth-remote-gateway
When your agent uses it
Manual OAuth for remote MCP servers on headless gateways.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
5,955 tokensWhen the skill is activated
Reference documents
768 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Why the Built-in OAuth Flow Fails on a Remote Gateway
- Cheap First Fallbacks: the Built-in Flow's Own Escape Hatches
- Preferred Front Door: the Hermes Dashboard (try this BEFORE manual token surgery)
- The Workaround
- Pitfalls & Lessons Learned
- What NOT to do
- Quick Reference Files
- Related
Scripts executed separately
scripts/diagnose-oauth-mcp.py../../../tools/mcp_oauth.py../../../tools/mcp_tool.py
Files
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# MCP OAuth on a Remote Hermes Gateway
## Overview
Hermes' built-in MCP OAuth client runs a one-shot HTTP listener on `127.0.0.1:<port>`
inside the Hermes process and registers that loopback address as the OAuth
`redirect_uri`. That works perfectly for a local CLI on the user's own machine.
It breaks completely when Hermes runs as a remote gateway (container, VPS,
messaging bot), because the user's browser resolves `127.0.0.1` to the user's own
laptop, not the remote container — so the authorization code never reaches Hermes.
This skill does the OAuth dance by hand and writes the resulting tokens into the
exact files Hermes' token storage expects, so a subsequent `/reload-mcp` finds
cached tokens and skips the browser flow entirely.View the complete source on GitHubmcporter
When your agent uses it
List, auth, and call MCP servers/tools from the terminal.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
791 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- Quick Start
- Discovering MCP Servers
- Calling Tools
- Auth and Config
- Daemon
- Code Generation
- Notes
Files
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# mcporter
Use `mcporter` to discover, call, and manage [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers and tools directly from the terminal.
## Prerequisites
Requires Node.js:
```bash
# No install needed (runs via npx)
npx mcporter list
# Or install globally
npm install -g mcporter
```View the complete source on GitHubgif-search
When your agent uses it
Search/download GIFs from Tenor via curl + jq.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
805 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Setup
- Prerequisites
- Search for GIFs
- Download a GIF
- Get Full Metadata
- API Parameters
- Available Media Formats
- Notes
Files
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# GIF Search (Tenor API)
Search and download GIFs directly via the Tenor API using curl. No extra tools needed.
## When to use
Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat.
## Setup
Set your Tenor API key in your environment (add to `${HERMES_HOME:-~/.hermes}/.env`):
```bash
[Credential-related source omitted]View the complete source on GitHubsongsee
When your agent uses it
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
648 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- Quick Start
- Visualization Types
- Common Flags
- Notes
Files
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# songsee
Generate spectrograms and multi-panel audio feature visualizations from audio files.
## Prerequisites
Requires [Go](https://go.dev/doc/install):
```bash
go install github.com/steipete/songsee/cmd/songsee@latest
```
Optional: `ffmpeg` for formats beyond WAV/MP3.
## Quick StartView the complete source on GitHubaccelerate
When your agent uses it
Run PyTorch training across GPUs with minimal changes.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,275 tokensWhen the skill is activated
Reference documents
9,163 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
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# HuggingFace Accelerate - Unified Distributed Training
## Quick start
Accelerate simplifies distributed training to 4 lines of code.
**Installation**:
```bash
pip install accelerate
```
**Convert PyTorch script** (4 lines):
```python
import torchView the complete source on GitHubchroma
When your agent uses it
Embedding database for RAG and semantic search.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,249 tokensWhen the skill is activated
Reference documents
188 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use Chroma
- Quick start
- Core operations
- Persistent storage
- Embedding functions
- Metadata filtering
- LangChain integration
- LlamaIndex integration
- Server mode
- Best practices
- Performance
- Resources
Files
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# Chroma - Open-Source Embedding Database
The AI-native database for building LLM applications with memory.
## When to use Chroma
**Use Chroma when:**
- Building RAG (retrieval-augmented generation) applications
- Need local/self-hosted vector database
- Want open-source solution (Apache 2.0)
- Prototyping in notebooks
- Semantic search over documents
- Storing embeddings with metadata
View the complete source on GitHubevaluating-llms-harness
When your agent uses it
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
3,561 tokensWhen the skill is activated
Reference documents
13,477 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What's inside
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
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# lm-evaluation-harness - LLM Benchmarking
## What's inside
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
## Quick start
lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.
**Installation**:
```bash
pip install lm-eval
```View the complete source on GitHubfaiss
When your agent uses it
Fast vector similarity search at billion scale.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,347 tokensWhen the skill is activated
Reference documents
1,872 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use FAISS
- Quick start
- Index types
- Save and load
- GPU acceleration
- LangChain integration
- LlamaIndex integration
- Best practices
- Performance
- Resources
Files
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# FAISS - Efficient Similarity Search
Facebook AI's library for billion-scale vector similarity search.
## When to use FAISS
**Use FAISS when:**
- Need fast similarity search on large vector datasets (millions/billions)
- GPU acceleration required
- Pure vector similarity (no metadata filtering needed)
- High throughput, low latency critical
- Offline/batch processing of embeddings
**Metrics**:View the complete source on GitHubflash-attention
When your agent uses it
Speed up long-sequence transformer training and inference.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
3,058 tokensWhen the skill is activated
Reference documents
4,679 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Scripts executed separately
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Files
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# Flash Attention - Fast Memory-Efficient Attention
## Quick start
Flash Attention provides 2-4x speedup and 10-20x memory reduction for transformer attention through IO-aware tiling and recomputation.
**PyTorch native (easiest, PyTorch 2.2+)**:
```python
import torch
import torch.nn.functional as F
q = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16) # [batch, heads, seq, dim]
k = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)
v = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)View the complete source on GitHubllama-cpp
When your agent uses it
llama.cpp local GGUF inference + HF Hub model discovery.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
2,512 tokensWhen the skill is activated
Reference documents
10,514 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Model Discovery workflow
- Quick start
- Python bindings (llama-cpp-python)
- Choosing a quant
- Extracting available GGUFs from a repo
- Search patterns
- Output format
- References
- Resources
Files
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# llama.cpp + GGUF
Use this skill for local GGUF inference, quant selection, or Hugging Face repo discovery for llama.cpp.
## When to use
- Run local models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs
- Find the right GGUF for a specific Hugging Face repo
- Build a `llama-server` or `llama-cli` command from the Hub
- Search the Hub for models that already support llama.cpp
- Enumerate available `.gguf` files and sizes for a repo
- Decide between Q4/Q5/Q6/IQ variants for the user's RAM or VRAM
## Model Discovery workflowView the complete source on GitHubserving-llms-vllm
When your agent uses it
vLLM: high-throughput LLM serving, OpenAI API, quantization.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,585 tokensWhen the skill is activated
Reference documents
7,722 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
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# vLLM - High-Performance LLM Serving
## When to use
Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
## Quick start
vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block-based KV cache) and continuous batching (mixing prefill/decode requests).
**Installation**:
```bash
pip install vllm
```View the complete source on GitHublambda-labs
When your agent uses it
On-demand GPU cloud instances for ML training.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
3,256 tokensWhen the skill is activated
Reference documents
6,712 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use Lambda Labs
- Quick start
- GPU instances
- Lambda Stack
- Python API
- CLI with curl
- Persistent storage
- SSH configuration
- JupyterLab
- Training workflows
- 1-Click Clusters
- Networking
- Common workflows
- Cost optimization
- Common issues
- References
- Resources
Files
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# Lambda Labs GPU Cloud
Guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.
## When to use Lambda Labs
**Use Lambda Labs when:**
- Need dedicated GPU instances with full SSH access
- Running long training jobs (hours to days)
- Want simple pricing with no egress fees
- Need persistent storage across sessions
- Require high-performance multi-node clusters (16-512 GPUs)
- Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL)
View the complete source on GitHubmodal
When your agent uses it
Serverless GPU cloud for ML jobs and model APIs.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,289 tokensWhen the skill is activated
Reference documents
5,169 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use Modal
- Quick start
- Core concepts
- GPU configuration
- Container images
- Persistent storage
- Web endpoints
- Dynamic batching
- Secrets management
- Scheduling
- Performance optimization
- Parallel processing
- Common configuration
- Debugging
- Common issues
- References
- Resources
Files
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# Modal Serverless GPU
Guide to running ML workloads on Modal's serverless GPU cloud platform.
## When to use Modal
**Use Modal when:**
- Running GPU-intensive ML workloads without managing infrastructure
- Deploying ML models as auto-scaling APIs
- Running batch processing jobs (training, inference, data processing)
- Need pay-per-second GPU pricing without idle costs
- Prototyping ML applications quickly
- Running scheduled jobs (cron-like workloads)
View the complete source on GitHubsegment-anything-model
When your agent uses it
SAM: zero-shot image segmentation via points, boxes, masks.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
3,398 tokensWhen the skill is activated
Reference documents
7,051 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use SAM
- Quick start
- Core concepts
- Interactive segmentation
- Automatic mask generation
- Batched inference
- ONNX deployment
- Common workflows
- Output format
- Performance optimization
- Common issues
- References
- Resources
Files
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# Segment Anything Model (SAM)
Guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.
## When to use SAM
**Use SAM when:**
- Need to segment any object in images without task-specific training
- Building interactive annotation tools with point/box prompts
- Generating training data for other vision models
- Need zero-shot transfer to new image domains
- Building object detection/segmentation pipelines
- Processing medical, satellite, or domain-specific images
View the complete source on GitHubnemo-curator
When your agent uses it
Curate LLM training data: dedupe, filter, PII redaction.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
2,794 tokensWhen the skill is activated
Reference documents
1,157 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use NeMo Curator
- Quick start
- Data curation pipeline
- GPU acceleration
- Multi-modal curation
- Common patterns
- Performance benchmarks
- Cost comparison
- Supported data formats
- Use cases
- References
- Resources
Files
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# NeMo Curator - GPU-Accelerated Data Curation
NVIDIA's toolkit for preparing high-quality training data for LLMs.
## When to use NeMo Curator
**Use NeMo Curator when:**
- Preparing LLM training data from web scrapes (Common Crawl)
- Need fast deduplication (16× faster than CPU)
- Curating multi-modal datasets (text, images, video, audio)
- Filtering low-quality or toxic content
- Scaling data processing across GPU cluster
**Performance**:View the complete source on GitHubobliteratus
When your agent uses it
OBLITERATUS: abliterate LLM refusals (diff-in-means).
What it loads into context
Frontmatter
142 tokensAlways available to the agent
SKILL.md
3,917 tokensWhen the skill is activated
Reference documents
2,918 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What's inside
- Video Guide
- When to Use This Skill
- Step 1: Installation
- Step 2: Check Hardware
- Step 3: Browse Available Models & Get Recommendations
- Step 4: Choose a Method
- Step 5: Run Abliteration
- Step 6: Verify Results
- Step 7: Use the Abliterated Model
- CLI Command Reference
- Analysis Modules
- Ablation Strategies
- Evaluation
- Platform Support
- YAML Config Templates
- Telemetry
- Common Pitfalls
- Complementary Skills
Files
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# OBLITERATUS Skill
## What's inside
9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations.
Remove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.
**License warning:** OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (`obliteratus` command) or subprocess. This keeps Hermes Agent's MIT license clean.
## Video Guide
Walkthrough of OBLITERATUS used by a Hermes agent to abliterate Gemma:
https://www.youtube.com/watch?v=8fG9BrNTeHs ("OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails")View the complete source on GitHubpeft
When your agent uses it
Fine-tune large LLMs with LoRA on limited GPU memory.
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
3,417 tokensWhen the skill is activated
Reference documents
5,929 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use PEFT
- Quick start
- LoRA parameter selection
- Loading and merging adapters
- PEFT methods comparison
- Integration patterns
- Performance benchmarks
- Common issues
- Best practices
- References
- Resources
Files
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# PEFT (Parameter-Efficient Fine-Tuning)
Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.
## When to use PEFT
**Use PEFT/LoRA when:**
- Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)
- Need to train <1% parameters (6MB adapters vs 14GB full model)
- Want fast iteration with multiple task-specific adapters
- Deploying multiple fine-tuned variants from one base model
**Use QLoRA (PEFT + quantization) when:**
- Fine-tuning 70B models on single 24GB GPUView the complete source on GitHubpytorch-fsdp
When your agent uses it
Fully sharded data-parallel training for large models.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
522 tokensWhen the skill is activated
Reference documents
105,715 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
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# Pytorch-Fsdp Skill
Assistance with pytorch-fsdp development, generated from official documentation.
## When to Use This Skill
This skill should be triggered when:
- Working with pytorch-fsdp
- Asking about pytorch-fsdp features or APIs
- Implementing pytorch-fsdp solutions
- Debugging pytorch-fsdp code
- Learning pytorch-fsdp best practices
## Quick ReferenceView the complete source on GitHubpytorch-lightning
When your agent uses it
Clean training loops with built-in distributed support.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
2,227 tokensWhen the skill is activated
Reference documents
8,997 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
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# PyTorch Lightning - High-Level Training Framework
## Quick start
PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.
**Installation**:
```bash
pip install lightning
```
**Convert PyTorch to Lightning** (3 steps):
```pythonView the complete source on GitHubdspy
When your agent uses it
DSPy: declarative LM programs, auto-optimize prompts, RAG.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,737 tokensWhen the skill is activated
Reference documents
10,990 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Installation
- Quick Start
- Core Concepts
- LM Provider Configuration
- Common Patterns
- Evaluation and Metrics
- Best Practices
- Comparison to Other Approaches
- Resources
- See Also
Files
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# DSPy: Declarative Language Model Programming
## When to Use This Skill
Use DSPy when you need to:
- **Build complex AI systems** with multiple components and workflows
- **Program LMs declaratively** instead of manual prompt engineering
- **Optimize prompts automatically** using data-driven methods
- **Create modular AI pipelines** that are maintainable and portable
- **Improve model outputs systematically** with optimizers
- **Build RAG systems, agents, or classifiers** with better reliability
**GitHub Stars**: 22,000+ | **Created By**: Stanford NLP
View the complete source on GitHubsaelens
When your agent uses it
Train sparse autoencoders to interpret model features.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,940 tokensWhen the skill is activated
Reference documents
4,990 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The Problem: Polysemanticity & Superposition
- When to Use SAELens
- Installation
- Core Concepts
- Workflow 1: Loading and Analyzing Pre-trained SAEs
- Workflow 2: Training a Custom SAE
- Workflow 3: Feature Analysis and Steering
- Common Issues & Solutions
- Integration with Neuronpedia
- Key Classes Reference
- Reference Documentation
- External Resources
- SAE Architectures
Files
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# SAELens: Sparse Autoencoders for Mechanistic Interpretability
SAELens is the primary library for training and analyzing Sparse Autoencoders (SAEs) - a technique for decomposing polysemantic neural network activations into sparse, interpretable features. Based on Anthropic's groundbreaking research on monosemanticity.
**GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars)
## The Problem: Polysemanticity & Superposition
Individual neurons in neural networks are **polysemantic** - they activate in multiple, semantically distinct contexts. This happens because models use **superposition** to represent more features than they have neurons, making interpretability difficult.
**SAEs solve this** by decomposing dense activations into sparse, monosemantic features - typically only a small number of features activate for any given input, and each feature corresponds to an interpretable concept.
## When to Use SAELens
View the complete source on GitHubsimpo
When your agent uses it
Reference-free preference alignment, simpler than DPO.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,630 tokensWhen the skill is activated
Reference documents
7,825 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
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# SimPO - Simple Preference Optimization
## Quick start
SimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.
**Installation**:
```bash
# Create environment
conda create -n simpo python=3.10 && conda activate simpo
# Install PyTorch 2.2.2
# Visit: https://pytorch.org/get-started/locally/
View the complete source on GitHubslime
When your agent uses it
RL post-training for LLMs with Megatron and SGLang.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
2,951 tokensWhen the skill is activated
Reference documents
4,768 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use slime
- Key Features
- Architecture Overview
- Installation
- Quick Start: GRPO Training
- Workflow 1: Standard GRPO Training
- Workflow 2: Asynchronous Training
- Workflow 3: Multi-Turn Agentic Training
- Configuration Reference
- Data Buffer System
- Common Issues and Solutions
- Supported Models
- Advanced Topics
- Resources
Files
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# slime: LLM Post-Training Framework for RL Scaling
slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation.
## When to Use slime
**Choose slime when you need:**
- Megatron-LM native training with SGLang inference
- Custom data generation workflows with flexible data buffers
- Training GLM, Qwen3, DeepSeek V3, or Llama 3 models
- Research-grade framework with production backing (Z.ai)
**Consider alternatives when:**
- You need enterprise-grade stability features → use **miles**View the complete source on GitHubtensorrt-llm
When your agent uses it
High-throughput LLM inference on NVIDIA GPUs.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
1,476 tokensWhen the skill is activated
Reference documents
6,788 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use TensorRT-LLM
- Quick start
- Key features
- Common patterns
- Performance benchmarks
- Supported models
- References
- Resources
Files
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# TensorRT-LLM
NVIDIA's open-source library for optimizing LLM inference with high performance on NVIDIA GPUs.
## When to use TensorRT-LLM
**Use TensorRT-LLM when:**
- Deploying on NVIDIA GPUs (A100, H100, GB200)
- Need maximum throughput (24,000+ tokens/sec on Llama 3)
- Require low latency for real-time applications
- Working with quantized models (FP8, INT4, FP4)
- Scaling across multiple GPUs or nodes
**Use vLLM instead when:**View the complete source on GitHubtorchtitan
When your agent uses it
Pretrain LLMs at scale with PyTorch 4D parallelism.
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
2,946 tokensWhen the skill is activated
Reference documents
5,014 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Supported models
- Performance benchmarks (H100)
- Advanced topics
- Resources
Files
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# TorchTitan - PyTorch Native Distributed LLM Pretraining
## Quick start
TorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.
**Installation**:
```bash
# From PyPI (stable)
pip install torchtitan
# From source (latest features, requires PyTorch nightly)
git clone https://github.com/pytorch/torchtitan
cd torchtitanView the complete source on GitHubaxolotl
When your agent uses it
Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
1,185 tokensWhen the skill is activated
Reference documents
74,601 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What's inside
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
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# Axolotl Skill
## What's inside
Expert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support.
Assistance with axolotl development, generated from official documentation.
## When to Use This Skill
This skill should be triggered when:
- Working with axolotl
- Asking about axolotl features or APIs
- Implementing axolotl solutionsView the complete source on GitHubunsloth
When your agent uses it
Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
496 tokensWhen the skill is activated
Reference documents
556,229 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
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# Unsloth Skill
Assistance with unsloth development, generated from official documentation.
## When to Use This Skill
This skill should be triggered when:
- Working with unsloth
- Asking about unsloth features or APIs
- Implementing unsloth solutions
- Debugging unsloth code
- Learning unsloth best practices
## Quick ReferenceView the complete source on GitHubmpp-agent
When your agent uses it
Pay HTTP 402 APIs via Machine Payments Protocol (MPP).
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,320 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Choosing a client
- Prerequisites
- Procedure (mppx, fastest path)
- Procedure (Tempo Wallet)
- Pitfalls
- Verification
Files
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# MPP Agent Skill
Wraps the Machine Payments Protocol (MPP, https://mpp.dev) clients so Hermes can pay for per-request API access against servers that respond with `HTTP 402 Payment Required`.
Three client options, all distributed via npm. Pick the lightest one that solves the user's need. Gated `[linux, macos]` while the broader payments tooling matures on Windows.
## When to Use
- A merchant API returns `HTTP 402` with a `www-authenticate` header — and the user wants to actually pay it, not just log the response.
- The user asks to "pay per request", "set up an agent wallet", "use Tempo / Privy / AgentCash", or wants to discover MPP-priced services.
- A Stripe Link spend has produced a Shared Payment Token (SPT) and the agent needs to attach it to the 402 challenge — in that flow, prefer `link-cli mpp pay` (see the `stripe-link-cli` skill).
## Choosing a client
View the complete source on GitHubstripe-link-cli
When your agent uses it
Agent payments via Stripe Link — cards, SPT, approvals.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,683 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Install
- How to Run
- Procedure
- Optional: run as an MCP server instead
- Pitfalls
- Verification
Files
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# Stripe Link CLI Skill
Wraps [@stripe/link-cli](https://github.com/stripe/link-cli) so Hermes can complete purchases on the user's behalf using one-time-use virtual cards or Shared Payment Tokens (SPT). Every spend is gated by an in-app approval in the Link mobile/web app — Hermes cannot self-approve.
US-only at the moment (Link account requirement). Windows is not supported by the upstream CLI — this skill is gated `[linux, macos]`.
## When to Use
Trigger phrases:
- "buy X", "pay for X", "make a purchase", "complete checkout"
- "get me a card", "I need a payment method"
- "log in to Link", "connect my Link wallet"
- HTTP 402 response from a merchant API with `www-authenticate: ... method="stripe"`View the complete source on GitHubstripe-projects
When your agent uses it
Provision SaaS services + sync creds via Stripe Projects.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,032 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Install
- How to Run
- Procedure
- Pitfalls
- Verification
Files
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# Stripe Projects Skill
Wraps the [Stripe Projects](https://projects.dev) CLI plugin so Hermes can provision SaaS services (Neon, Twilio, Vercel, etc.), generate and sync credentials into the user's `.env`, and manage billing across providers from one place.
Gated `[linux, macos]` while the broader payments cluster matures on Windows. The Stripe CLI itself is cross-platform; this gate is a posture for the cluster, not a hard limit.
## When to Use
Trigger phrases:
- "set up <provider>", "provision <Neon|Twilio|Vercel|...>", "create a database"
- "give me a <Postgres|Redis|Twilio number|...> for this project"
- "manage my stack credentials", "rotate this key", "upgrade my plan"
- "what providers can I add?"View the complete source on GitHubairtable
When your agent uses it
Airtable REST API via curl. Records CRUD, filters, upserts.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,223 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- API Basics
- Field Types (request body shapes)
- Common Queries
- Common Mutations
- Pagination
- Typical Hermes Workflow
- Pitfalls
- Important Notes for Hermes
Files
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# Airtable — Bases, Tables & Records
Work with Airtable's REST API directly via `curl` using the `terminal` tool. No MCP server, no OAuth flow, no Python SDK — just `curl` and a personal access token.
## Prerequisites
1. Create a **Personal Access Token (PAT)** at https://airtable.com/create/tokens (tokens start with `pat...`).
2. Grant these scopes (minimum):
- `data.records:read` — read rows
- `data.records:write` — create / update / delete rows
- `schema.bases:read` — list bases and tables
3. **Important:** in the same token UI, add each base you want to access to the token's **Access** list. PATs are scoped per-base — a valid token on the wrong base returns `403`.
4. Store the token in `${HERMES_HOME:-~/.hermes}/.env` (or via `hermes setup`):
```View the complete source on GitHubbox
When your agent uses it
Box manages cloud files, sharing, search, and metadata.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,440 tokensWhen the skill is activated
Reference documents
11,193 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Start broad file-system conversations
- Perform chosen setup interactively
- Start each task
- Extend the CLI without pausing
- Choose the right path
- Content handling policy
- Operate safely
- Report results
- Verify
Files
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# Box
Use Box as the cloud file system for file operations, collaboration, metadata, and document work. Run operations with Hermes' `terminal` tool and use the Box CLI; use the SDK guide when building an application.
## When to Use
- Organizing, uploading, versioning, moving, sharing, or collaborating on Box files and folders
- Searching Box content or existing metadata
- Asking questions about Box files, extracting metadata, or generating text grounded in a file
- Processing a Box folder at scale without downloading every source file
- Building a Box-backed application, integration, or webhook handler
## Start broad file-system conversations
View the complete source on GitHubcanvas
When your agent uses it
Fetch Canvas LMS courses and assignments via API token.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
836 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scripts
- Setup
- Usage
- Output Format
- API Reference (curl)
- Rules
- Troubleshooting
Scripts executed separately
scripts/canvas_api.py
Files
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# Canvas LMS — Course & Assignment Access
Read-only access to Canvas LMS for listing courses and assignments.
## Scripts
- `scripts/canvas_api.py` — Python CLI for Canvas API calls
## Setup
1. Log in to your Canvas instance in a browser
2. Go to **Account → Settings** (click your profile icon, then Settings)
3. Scroll to **Approved Integrations** and click **+ New Access Token**
4. Name the token (e.g., "Hermes Agent"), set an optional expiry, and click **Generate Token**View the complete source on GitHubdecision-questionnaire
When your agent uses it
Turn an unanswerable decision into a questionnaire doc.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
966 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Core Principle: Interview the Send, Not the Subject
- Document Structure
- Pitfalls
- Verification
Files
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# Decision Questionnaire
Turns something the user can't answer alone into a **questionnaire**: a
Markdown document they hand to one person to fill in async, or fill out
together in a meeting. The recipient holds knowledge the user lacks; the
questionnaire pulls it out of them.
Ported from mattpocock/skills' MIT-licensed `to-questionnaire` skill.
## When to Use
- A decision blocks on facts or judgment held by someone else (a domain
expert, a stakeholder, a vendor contact, ops)
- The user says "I need to ask X about this" or keeps deferring a decisionView the complete source on GitHubhere-now
When your agent uses it
Publish sites to {slug}.here.now and store files in Drives.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
2,164 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Current docs
- Requirements
- Create a site
- Update an existing site
- Use a Drive
- API key storage
- Getting an API key
- State file
- What to tell the user
- publish.sh options
- Beyond publish.sh
Scripts executed separately
scripts/drive.shscripts/publish.sh
Files
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# here.now
here.now lets agents publish websites and store private files in cloud Drives.
Use here.now for two jobs:
- **Sites**: publish websites and files at `{slug}.here.now`.
- **Drives**: store private agent files in cloud folders.
## Current docs
**Before answering questions about here.now capabilities, features, or workflows, read the current docs:**
→ **https://here.now/docs**View the complete source on GitHublive-dashboard
When your agent uses it
Build self-updating dashboards from live sources.
What it loads into context
Frontmatter
192 tokensAlways available to the agent
SKILL.md
1,828 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Procedure — Setup (foreground, once)
- Procedure — Tick (each scheduled run)
- Procedure — Show the dashboard (after every build or re-render, and on request)
- Pitfalls
- Verification
Files
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# Live Dashboard
Turn one sentence — "make a dashboard for our visa applications, update it daily from the email threads and the case-status site" — into a persistent, self-refreshing status page. The user describes what they want to see; you define the data contract, build a self-contained HTML dashboard, verify one live refresh, then schedule the recurring tick.
Setup runs once in the foreground; the recurring refresh runs as a `cronjob` tick. Installing this skill offers a daily all-dashboards sweep via `/suggestions` (the frontmatter blueprint).
## When to Use
- "Make a dashboard for <project/process> and keep it updated."
- "I want one place to see the status of <deals / applications / bugs / shipments>."
- "Track <thing> across my email and <website> and show me where it stands."
- "Show me the <slug> dashboard." (re-render/preview an existing one)
- A cron tick fires for an existing dashboard (steps 5-7).
View the complete source on GitHubmemento-flashcards
When your agent uses it
Spaced-repetition flashcards: create, review, quiz, export.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
3,263 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Quick Reference
- Card Storage
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/memento_cards.pyscripts/youtube_quiz.py../../../scripts/_activation.sh../../../scripts/build/windows-deps.ps1../../../scripts/run-in-hermes-env../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests_parallel.py../../../scripts/tool_store_pins.py
Files
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# Memento Flashcards — Spaced-Repetition Flashcard Skill
## Overview
Memento gives you a local, file-based flashcard system with spaced-repetition scheduling.
Users can chat with their flashcards by answering in free text and having the agent grade the response before scheduling the next review.
Use it whenever the user wants to:
- **Remember a fact** — turn any statement into a Q/A flashcard
- **Study with spaced repetition** — review due cards with adaptive intervals and agent-graded free-text answers
- **Quiz from a YouTube video** — fetch a transcript and generate a 5-question quiz
- **Manage decks** — organise cards into collections, export/import CSV
All card data lives in a single JSON file. No external API keys are required — you (the agent) generate flashcard content and quiz questions directly.View the complete source on GitHubnotion
When your agent uses it
Notion API + ntn CLI: pages, databases, markdown, Workers.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,173 tokensWhen the skill is activated
Reference documents
890 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Setup
- API Basics
- Path A — `ntn` CLI (preferred, macOS / Linux)
- Path B — HTTP + curl (cross-platform, default on Windows)
- Property Types
- API Version 2025-09-03 — Databases vs Data Sources
- Notion Workers (advanced, requires `ntn`)
- Notion-Flavored Markdown (used by `/markdown` endpoints)
- Choosing the Right Path
- Notes
Files
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# Notion
Talk to Notion two ways. Same integration token works for both — pick by what's available.
◆ **`ntn` CLI** — Notion's official CLI. Shorter syntax, one-line file uploads, required for Workers. macOS + Linux only as of May 2026 (Windows support "coming soon"). **Default when installed.**
◆ **HTTP + curl** — works everywhere including Windows. **Default fallback** when `ntn` isn't installed.
## Setup
### 1. Get an integration token (required for both paths)
1. Create an integration at https://notion.so/my-integrations
2. Copy the API key (starts with `ntn_` or `secret_`)
3. Store in `${HERMES_HOME:-~/.hermes}/.env`:View the complete source on GitHubproperty-listings
When your agent uses it
Present property and rental listings as desktop cards.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,089 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Files
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# Property Listings Skill
Present researched properties as browsable cards in the Hermes desktop transcript.
This is a presentation recipe, not a listing search service or an investment valuation.
## When to Use
- Presenting property or rental search results, comparing a shortlist, or re-ranking properties.
- Following up on a property already shown: keep using cards so the shortlist stays comparable.
- Outside the desktop app, use ordinary Markdown with source links instead; other clients need not render listing fences.
## Prerequisites
- A Hermes desktop conversation for native cards; the backend may be local or remote.View the complete source on GitHubshop
When your agent uses it
Shop catalog search, checkout, order tracking, returns.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
3,876 tokensWhen the skill is activated
Reference documents
5,510 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Setup
- IMPORTANT: Shopping flow
- Commands
- Sign in
- Search rules
- Showing products
- Visualization
- Checkout
- Orders
- General rules
- Security — CRITICAL, follow all of these
- Safety & legal
Files
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# Shop CLI Skill
## Setup
Prefer the installed `shop` CLI. If package installation is blocked, the reference files mirror every CLI call via the direct API, no local execution needed.
```bash
pnpm add --global @shopify/shop-cli # or: npm install --global @shopify/shop-cli
shop --help
```
To upgrade: `pnpm add --global @shopify/shop-cli@latest` (or `npm install --global @shopify/shop-cli@latest`). Uninstall: `pnpm rm -g @shopify/shop-cli` (or `npm rm -g @shopify/shop-cli`).
**Reference files:**
- [catalog-mcp.md](references/catalog-mcp.md) — direct catalog MCP calls + manual token exchangeView the complete source on GitHubshopify
When your agent uses it
Query Shopify Admin/Storefront GraphQL APIs via curl.
What it loads into context
Frontmatter
266 tokensAlways available to the agent
SKILL.md
3,915 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- API Basics
- Discovery
- Products
- Orders
- Customers
- Inventory
- Metafields & Metaobjects
- Storefront API (public read-only)
- Bulk Operations
- Webhooks
- Pitfalls
- Safety
Files
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# Shopify — Admin & Storefront GraphQL APIs
Work with Shopify stores directly through `curl`: list products, manage inventory, pull orders, update customers, read metafields. No SDK, no app framework — just the GraphQL endpoint and a custom-app access token.
The REST Admin API is legacy since 2024-04 and only receives security fixes. **Use GraphQL Admin** for all admin work. Use **Storefront GraphQL** for read-only customer-facing queries (products, collections, cart).
## Prerequisites
1. In Shopify admin: **Settings → Apps and sales channels → Develop apps → Create an app**.
2. Click **Configure Admin API scopes**, select what you need (examples below), save.
3. **Install app** → the Admin API access token appears ONCE. Copy it immediately — Shopify will never show it again. Tokens start with `shpat_`.
4. Save to `${HERMES_HOME:-~/.hermes}/.env`:
```
[Credential-related source omitted]View the complete source on GitHubsiyuan
When your agent uses it
Query and edit a SiYuan knowledge base via its API.
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
2,897 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- API Basics
- Quick Reference
- Common Operations
- Block Types
- Pitfalls
- Alternative: MCP Server
Files
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# SiYuan Note API
Use the [SiYuan](https://github.com/siyuan-note/siyuan) kernel API via curl to search, read, create, update, and delete blocks and documents in a self-hosted knowledge base. No extra tools needed -- just curl and an API token.
## Prerequisites
1. Install and run SiYuan (desktop or Docker)
[Credential-related source omitted]
3. Store it in `${HERMES_HOME:-~/.hermes}/.env`:
```
[Credential-related source omitted]
SIYUAN_URL=http://127.0.0.1:6806
```
`SIYUAN_URL` defaults to `http://127.0.0.1:6806` if not set.View the complete source on GitHubtelephony
When your agent uses it
Provision Twilio numbers, SMS/MMS, and AI outbound calls.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
3,113 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What this solves
- Safety rules — mandatory
- Decision tree — which service to use?
- Files and persistent state
- Locate the helper script
- Install
- Provider setup
- Diagnose current state
- Common workflows
- Suggested agent procedure
- What this skill still does not do
- Pitfalls
- Verification checklist
- References
Scripts executed separately
../../../hermesscripts/telephony.py
Files
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# Telephony — Numbers, Calls, and Texts without Core Tool Changes
This optional skill gives Hermes practical phone capabilities while keeping telephony out of the core tool list.
It ships with a helper script, `scripts/telephony.py`, that can:
- save provider credentials into `${HERMES_HOME:-~/.hermes}/.env`
- search for and buy a Twilio phone number
- remember that owned number for later sessions
- send SMS / MMS from the owned number
- poll inbound SMS for that number with no webhook server required
- make direct Twilio calls using TwiML `<Say>` or `<Play>`
- import the owned Twilio number into Vapi
- place outbound AI calls through Bland.ai or Vapi
View the complete source on GitHubbioinformatics
When your agent uses it
Gateway to 400+ genomics and computational biology skills.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,388 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Sources
- How to fetch and use a skill
- Skill Index by Domain
- Environment Setup
- Pitfalls
Files
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# Bioinformatics Skills Gateway
Use when asked about bioinformatics, genomics, sequencing, variant calling, gene expression, single-cell analysis, protein structure, pharmacogenomics, metagenomics, phylogenetics, or any computational biology task.
This skill is a gateway to two open-source bioinformatics skill libraries. Instead of bundling hundreds of domain-specific skills, it indexes them and fetches what you need on demand.
## Sources
◆ **bioSkills** — 385 reference skills (code patterns, parameter guides, decision trees)
Repo: https://github.com/GPTomics/bioSkills
Format: SKILL.md per topic with code examples. Python/R/CLI.
◆ **ClawBio** — 33 runnable pipeline skills (executable scripts, reproducibility bundles)
Repo: https://github.com/ClawBio/ClawBioView the complete source on GitHubblogwatcher
When your agent uses it
Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
1,754 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Working with Hermes tools (read this first)
- Installation
- Common Commands
- Environment Variables
- Example Output
- Notes
Files
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# Blogwatcher
Track blog and RSS/Atom feed updates with the `blogwatcher-cli` tool. Supports automatic feed discovery, HTML scraping fallback, OPML import, and read/unread article management.
## Working with Hermes tools (read this first)
`blogwatcher-cli` is the feed database; Hermes tools do the automation around it:
- **Recurring watch — use the cronjob tool's `monitor` field, not a bare schedule.** `monitor` runs a script each tick and only wakes the agent when output changes: set it to a script that runs `blogwatcher-cli scan >/dev/null 2>&1 && blogwatcher-cli articles` (deterministic output; new articles = changed output = agent wakes with the diff injected). Unchanged ticks cost zero LLM calls. Set `deliver` to route digests to a chat/channel; add `continuity: true` so consecutive digests can dedupe.
- **Reading an article the user asks about**: `web_extract([url])` on the article URL from `blogwatcher-cli articles` — do not re-scrape by hand.
- **One-off "watch this page for changes" without feed semantics**: skip this skill; the cronjob tool's `monitor` field accepts an http(s) URL directly.
- **One-off read of a feed or a site's latest posts, nothing to install**: the bundled `rss-feeds` skill (`scripts/feed.py read URL`); blogwatcher earns its install when you track many feeds with read/unread state.
- **Company/competitor tracking with analysis and citations**: prefer the `competitor-news-monitor` skill; blogwatcher is the lighter raw-feed layer it can sit on.
View the complete source on GitHubdarwinian-evolver
When your agent uses it
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
2,396 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Install (One-Time)
- Quick Start — The Built-In Parrot Example
- Quick Start — OpenRouter Driver (No Anthropic Key)
- Defining a Custom Problem
- Hyperparameters That Actually Matter
- Pitfalls
- Verification
- References
Scripts executed separately
scripts/parrot_openrouter.pyscripts/show_snapshot.pytemplates/custom_problem_template.py
Files
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# Darwinian Evolver
Run Imbue's [darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) — an
LLM-driven evolutionary search loop — to optimize a **prompt, regex, SQL query,
or small code snippet** against a fitness function.
Status: thin wrapper around the upstream tool. The skill installs it, walks the
agent through writing a `Problem` definition (organism + evaluator + mutator),
and drives the loop via the upstream CLI or a small custom Python driver.
**License:** the upstream tool is **AGPL-3.0**. The skill ONLY ever invokes it
via the upstream CLI or a `subprocess`/`uv run` call (mere aggregation). Do NOT
import upstream classes into Hermes itself.
View the complete source on GitHubdomain-intel
When your agent uses it
Passive recon of subdomains, SSL certs, WHOIS, and DNS.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,132 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Helper script
- Available commands
- When to use this vs built-in tools
- Platform compatibility
- Data sources
- Notes
Scripts executed separately
scripts/domain_intel.py
Files
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# Domain Intelligence — Passive OSINT
Passive domain reconnaissance using only Python stdlib.
**Zero dependencies. Zero API keys. Works on Linux, macOS, and Windows.**
## Helper script
This skill includes `scripts/domain_intel.py` — a complete CLI tool for all domain intelligence operations.
```bash
# Subdomain discovery via Certificate Transparency logs
python SKILL_DIR/scripts/domain_intel.py subdomains example.com
# SSL certificate inspection (expiry, cipher, SANs, issuer)View the complete source on GitHubdrug-discovery
When your agent uses it
Drug discovery: ChEMBL search, drug-likeness, interactions.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,717 tokensWhen the skill is activated
Reference documents
599 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Workflows
- Reasoning Guidelines
- Important Notes
- Quick Reference
Scripts executed separately
scripts/chembl_target.pyscripts/ro5_screen.py
Files
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# Drug Discovery & Pharmaceutical Research
You are an expert pharmaceutical scientist and medicinal chemist with deep
knowledge of drug discovery, cheminformatics, and clinical pharmacology.
Use this skill for all pharma/chemistry research tasks.
## Core Workflows
### 1 — Bioactive Compound Search (ChEMBL)
Search ChEMBL (the world's largest open bioactivity database) for compounds
by target, activity, or molecule name. No API key required.
```bashView the complete source on GitHubduckduckgo-search
When your agent uses it
Free keyless web, news, and image search via ddgs.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,148 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Detection Flow
- Installation
- Method 1: CLI Search (Preferred)
- Method 2: Python API (Only After Verification)
- Workflow: Search then Extract
- Limitations
- Troubleshooting
- Pitfalls
- Validated With
Scripts executed separately
scripts/duckduckgo.sh
Files
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# DuckDuckGo Search
Free web search using DuckDuckGo. **No API key required.**
Preferred when `web_search` is unavailable or unsuitable (for example when `FIRECRAWL_API_KEY` is not set). Can also be used as a standalone search path when DuckDuckGo results are specifically desired.
## Detection Flow
Check what is actually available before choosing an approach:
```bash
# Check CLI availability
command -v ddgs >/dev/null && echo "DDGS_CLI=installed" || echo "DDGS_CLI=missing"
```View the complete source on GitHubgitnexus-explorer
When your agent uses it
Serve an interactive codebase knowledge graph web UI.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,968 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Size Warning
- Steps
- Pitfalls
Scripts executed separately
scripts/proxy.mjs
Files
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# GitNexus Explorer
Index any codebase into a knowledge graph and serve an interactive web UI for exploring
symbols, call chains, clusters, and execution flows. Tunneled via Cloudflare for remote access.
## When to Use
- User wants to visually explore a codebase's architecture
- User asks for a knowledge graph / dependency graph of a repo
- User wants to share an interactive codebase explorer with someone
## Prerequisites
- **Node.js** (v18+) — required for GitNexus and the proxyView the complete source on GitHubllm-wiki
When your agent uses it
Karpathy's LLM Wiki: build/query interlinked markdown KB.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,846 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When This Skill Activates
- Wiki Location
- Architecture: Three Layers
- Resuming an Existing Wiki (CRITICAL — do this every session)
- Initializing a New Wiki
- Tag Taxonomy
- Page Thresholds
- Entity Pages
- Concept Pages
- Comparison Pages
- Update Policy
- Core Operations
- Working with the Wiki
- Pitfalls
- Related Tools
Files
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# Karpathy's LLM Wiki
Build and maintain a persistent, compounding knowledge base as interlinked markdown files.
Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f).
Unlike traditional RAG (which rediscovers knowledge from scratch per query), the wiki
compiles knowledge once and keeps it current. Cross-references are already there.
Contradictions have already been flagged. Synthesis reflects everything ingested.
**Division of labor:** The human curates sources and directs analysis. The agent
summarizes, cross-references, files, and maintains consistency.
## When This Skill Activates
View the complete source on GitHubosint-investigation
When your agent uses it
Follow the money via public records and sanctions data.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
2,798 tokensWhen the skill is activated
Reference documents
11,168 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Workflow
- Confidence and evidence discipline
- Adding a new data source
- Tools and their limits
- Legal note
Scripts executed separately
scripts/_http.pyscripts/_normalize.pyscripts/build_findings.pyscripts/entity_resolution.pyscripts/fetch_courtlistener.pyscripts/fetch_gdelt.pyscripts/fetch_icij_offshore.pyscripts/fetch_nyc_acris.pyscripts/fetch_ofac_sdn.pyscripts/fetch_opencorporates.pyscripts/fetch_sec_edgar.pyscripts/fetch_senate_ld.pyscripts/fetch_usaspending.pyscripts/fetch_wayback.pyscripts/fetch_wikipedia.pyscripts/timing_analysis.py
Files
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# OSINT Investigation — Public Records Cross-Reference
Investigative framework for public-records OSINT: government contracts,
corporate filings, lobbying, sanctions, offshore leaks, property records,
court records, web archives, knowledge bases, and global news. Resolve
entities across heterogeneous sources, build cross-links with explicit
confidence, run statistical timing tests, and produce structured evidence
chains.
**Python stdlib only.** Zero install. Works on Linux, macOS, Windows. Most
sources work with no API key (OpenCorporates has an optional free token
that raises rate limits).
Adapted from the MIT-licensed ShinMegamiBoson/OpenPlanter project; expandedView the complete source on GitHubparallel-cli
When your agent uses it
Agent-native web search, deep research, and enrichment.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,562 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use it
- Installation
- Authentication
- Core rule set
- Quick reference
- Common flags and patterns
- Search
- Extraction
- Deep research
- Enrichment
- FindAll
- Monitor
- Recommended Hermes usage patterns
- Error handling and exit codes
- Maintenance
- Pitfalls
Files
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# Parallel CLI
Use `parallel-cli` when the user explicitly wants Parallel, or when a terminal-native workflow would benefit from Parallel's vendor-specific stack for web search, extraction, deep research, enrichment, entity discovery, or monitoring.
This is an optional third-party workflow, not a Hermes core capability.
Important expectations:
- Parallel is a paid service with a free tier, not a fully free local tool.
- It overlaps with Hermes native `web_search` / `web_extract`, so do not prefer it by default for ordinary lookups.
- Prefer this skill when the user mentions Parallel specifically or needs capabilities like Parallel's enrichment, FindAll, or monitor workflows.
`parallel-cli` is designed for agents:
- JSON output via `--json`
- Non-interactive command executionView the complete source on GitHubqmd
When your agent uses it
Hybrid local search over notes, docs, and transcripts.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,436 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Quick Reference
- Setup Workflow
- Search Patterns
- MCP Integration (Recommended)
- CLI Usage (Without MCP)
- How the Search Pipeline Works
- Best Practices
- Troubleshooting
- Data Storage
- References
Files
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# QMD — Query Markup Documents
Local, on-device search engine for personal knowledge bases. Indexes markdown
notes, meeting transcripts, documentation, and any text-based files, then
provides hybrid search combining keyword matching, semantic understanding, and
LLM-powered reranking — all running locally with no cloud dependencies.
Created by [Tobi Lütke](https://github.com/tobi/qmd). MIT licensed.
## When to Use
- User asks to search their notes, docs, knowledge base, or meeting transcripts
- User wants to find something across a large collection of markdown/text files
- User wants semantic search ("find notes about X concept") not just keyword grepView the complete source on GitHubresearch-paper-writing
When your agent uses it
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
What it loads into context
Frontmatter
166 tokensAlways available to the agent
SKILL.md
16,698 tokensWhen the skill is activated
Reference documents
54,560 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When To Use This Skill
- Core Philosophy
- Phase 0: Project Setup
- Phase 1: Literature Review
- Phase 2: Experiment Design
- Phase 3: Experiment Execution & Monitoring
- Phase 4: Result Analysis
- Iterative Refinement: Strategy Selection
- Phase 5: Paper Drafting
- Phase 6: Self-Review & Revision
- Phase 7: Submission Preparation
- Phase 8: Post-Acceptance Deliverables
- Workshop & Short Papers
- Paper Types Beyond Empirical ML
- Hermes Agent Integration
- Reviewer Evaluation Criteria
- Common Issues and Solutions
- Reference Documents
Scripts executed separately
templates/neurips2025/Makefile
Files
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Read SKILL.md
# Research Paper Writing Pipeline
End-to-end pipeline for producing publication-ready ML/AI research papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and COLM**. This skill covers the full research lifecycle: experiment design, execution, monitoring, analysis, paper writing, review, revision, and submission.
This is **not a linear pipeline** — it is an iterative loop. Results trigger new experiments. Reviews trigger new analysis. The agent must handle these feedback loops.
<!-- ascii-guard-ignore -->
```
┌─────────────────────────────────────────────────────────────┐
│ RESEARCH PAPER PIPELINE │
│ │
│ Phase 0: Project Setup ──► Phase 1: Literature Review │
│ │ │ │
│ ▼ ▼ │View the complete source on GitHubrss-feeds
When your agent uses it
Read RSS, Atom, JSON feeds; discover feeds behind a page.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
1,178 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/feed.py
Files
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# RSS Feeds Skill
Reads any RSS 2.0, RSS 1.0/RDF, Atom, or JSON Feed URL into a clean, date-sorted list of
entries, and discovers the feed behind an ordinary page URL (`<link rel="alternate">` or
the usual `/feed`, `/rss.xml`, `/atom.xml` paths). Standard library only, nothing to
install. It does not fetch full article bodies — pass an entry's link to `web_extract` for
that.
## When to Use
- "What's new on <blog/site>", "latest releases of <GitHub repo>", "recent posts in
<subreddit>", "read this feed", "does this site have an RSS feed".
- Building a recurring digest with `cronjob_manage` (feeds are cheaper and more stable than
scraping the HTML front page every run). For a persistent read/unread database acrossView the complete source on GitHubscrapling
When your agent uses it
Scrape sites with stealth browsing and Cloudflare bypass.
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
2,235 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Installation
- Quick Reference
- CLI Usage
- Python: HTTP Scraping
- Python: Dynamic Pages (JS-Rendered)
- Python: Stealth Mode (Anti-Bot Bypass)
- Element Selection
- Python: Spider Framework
- Pitfalls
Files
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# Scrapling
[Scrapling](https://github.com/D4Vinci/Scrapling) is a web scraping framework with anti-bot bypass, stealth browser automation, and a spider framework. It provides three fetching strategies (HTTP, dynamic JS, stealth/Cloudflare) and a full CLI.
**This skill is for educational and research purposes only.** Users must comply with local/international data scraping laws and respect website Terms of Service.
## When to Use
- Scraping static HTML pages (faster than browser tools)
- Scraping JS-rendered pages that need a real browser
- Bypassing Cloudflare Turnstile or bot detection
- Crawling multiple pages with a spider
- When the built-in `web_extract` tool does not return the data you need
View the complete source on GitHubsearxng-search
When your agent uses it
Free keyless meta-search aggregating 70+ engines.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,850 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Configuration
- Detection Flow
- Method 1: CLI via curl (Preferred)
- Method 2: Python API via `requests`
- Self-Hosting SearXNG
- Workflow: Search then Extract
- Limitations
- Troubleshooting
- Pitfalls
- Instance Discovery
Scripts executed separately
scripts/searxng.sh
Files
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# SearXNG Search
Free meta-search using [SearXNG](https://searxng.org/) — a privacy-respecting, self-hosted search aggregator that queries 70+ search engines simultaneously.
**No API key required** when using a public instance. Can also be self-hosted for full control. Automatically appears as a fallback when the main web search toolset (`FIRECRAWL_API_KEY`) is not configured.
## Configuration
SearXNG requires a `SEARXNG_URL` environment variable pointing to your SearXNG instance:
```bash
# Public instances (no setup required)
SEARXNG_URL=https://searxng.example.com
View the complete source on GitHub1password
When your agent uses it
Set up op CLI, sign in, and read or inject secrets.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
1,222 tokensWhen the skill is activated
Reference documents
308 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Requirements
- When to Use
- Authentication Methods
- Setup
- Hermes Execution Pattern (desktop app flow)
- Common Operations
- Guardrails
- CI / Headless note
- References
Files
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# 1Password CLI
Use this skill when the user wants secrets managed through 1Password instead of plaintext env vars or files.
## Requirements
- 1Password account
- 1Password CLI (`op`) installed
- One of: desktop app integration, service account token (`OP_SERVICE_ACCOUNT_TOKEN`), or Connect server
- `tmux` available for stable authenticated sessions during Hermes terminal calls (desktop app flow only)
## When to Use
- Install or configure 1Password CLIView the complete source on GitHubgodmode
When your agent uses it
Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
5,125 tokensWhen the skill is activated
Reference documents
3,723 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Overview of Attack Modes
- Step 0: Auto-Jailbreak (Recommended)
- Step 1: Choose Your Attack Mode
- Step 2: GODMODE CLASSIC — Quick Start
- Step 3: PARSELTONGUE — Obfuscating Queries
- Step 4: ULTRAPLINIAN — Multi-Model Racing
- Step 5: Detecting Refusals
- Step 6: Advanced — Combining Techniques
- Model-Specific Notes
- Trigger Words (Reference)
- Source Credits
- Tested Results (March 2026)
- Common Pitfalls
Scripts executed separately
scripts/auto_jailbreak.pyscripts/godmode_race.pyscripts/load_godmode.pyscripts/parseltongue.py
Files
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Read SKILL.md
# G0DM0D3 Jailbreaking Skill
Bypass safety filters on API-served LLMs using techniques from [G0DM0D3](https://github.com/elder-plinius/G0DM0D3) and [L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S). Three attack modes plus Hermes-native configuration for persistent jailbreaking.
**Key difference from OBLITERATUS:** OBLITERATUS modifies model weights permanently (requires open-weight models + GPU). This skill operates at the prompt/API level — works on any model accessible via API, including closed-source models (GPT, Claude, Gemini, Grok).
## When to Use This Skill
Trigger when the user:
- Wants to "jailbreak" a model via API
- Asks about bypassing safety filters on Claude, GPT, Gemini, Grok, etc.
- Wants to set up persistent jailbreaking in their Hermes config
- Asks about Parseltongue, GODMODE, L1B3RT4S, or Pliny's techniques
- Wants to red-team a model's safety trainingView the complete source on GitHuboss-forensics
When your agent uses it
GitHub supply-chain forensics: recovery, IOCs, reporting.
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
4,693 tokensWhen the skill is activated
Reference documents
7,109 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ⚠<U+FE0F> Anti-Hallucination Guardrails
- Example Scenarios
- Phase 0: Initialization
- Phase 1: Prompt Parsing and IOC Extraction
- Phase 2: Parallel Evidence Collection
- Phase 3: Evidence Consolidation
- Phase 4: Hypothesis Formation
- Phase 5: Hypothesis Validation
- Phase 6: Final Report Generation
- Phase 7: Completion
- Ethical Use Guidelines
- API Rate Limiting
- Reference Materials
Scripts executed separately
scripts/evidence-store.py
Files
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# OSS Security Forensics Skill
A 7-phase multi-agent investigation framework for researching open-source supply chain attacks.
Adapted from RAPTOR's forensics system. Covers GitHub Archive, Wayback Machine, GitHub API,
local git analysis, IOC extraction, evidence-backed hypothesis formation and validation,
and final forensic report generation.
---
## ⚠<U+FE0F> Anti-Hallucination Guardrails
Read these before every investigation step. Violating them invalidates the report.
1. **Evidence-First Rule**: Every claim in any report, hypothesis, or summary MUST cite at least one evidence ID (`EV-XXXX`). Assertions without citations are forbidden.View the complete source on GitHubsherlock
When your agent uses it
Find accounts for a username across 400+ platforms.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,436 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Requirements
- Procedure
- Pitfalls
- Installation
- Ethical Use
- Verification
- Example Interaction
Files
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# Sherlock OSINT Username Search
Hunt down social media accounts by username across 400+ social networks using the [Sherlock Project](https://github.com/sherlock-project/sherlock).
## When to Use
- User asks to find accounts associated with a username
- User wants to check username availability across platforms
- User is conducting OSINT or reconnaissance research
- User asks "where is this username registered?" or similar
## Requirements
- Sherlock CLI installed: `pipx install sherlock-project` or `pip install sherlock-project`View the complete source on GitHubunbroker
When your agent uses it
Remove your info from data-broker sites, by name or phone.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
7,107 tokensWhen the skill is activated
Reference documents
13,369 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Autonomy contract
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Batch operation (two-phase: crawl-all, then delete)
- Procedure (the autonomous loop)
- Spam-call scrubber (phone-led run)
- Pitfalls
- Verification
Scripts executed separately
scripts/autopilot.pyscripts/badbool.pyscripts/brokers.pyscripts/cdp.pyscripts/config.pyscripts/crypto.pyscripts/dossier.pyscripts/email_modes.pyscripts/emailer.pyscripts/ledger.pyscripts/legal.pyscripts/paths.pyscripts/pdd.pyscripts/registry.pyscripts/report.pyscripts/scan.pyscripts/storage.pyscripts/tiers.pyscripts/vectors.py../../../scripts/run_tests.sh../../../tests/skills/test_unbroker_skill.py../../../tests/skills/test_unbroker_skill.py
Files
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# unbroker
Find where a person's personal information (name, addresses, phone, email, relatives) is exposed on
data brokers and people-search sites, then remove it - automatically where possible, with guided
human steps only where a site demands a CAPTCHA, government ID, phone call, or fax. Manages multiple
people independently. It does **not** defeat anti-bot systems, does **not** act on anyone without
recorded consent, and does **not** remove public records (voter/property/court) or accounts the
person controls.
The Python CLI (`scripts/pdd.py`) owns the deterministic state - config, dossiers + consent, the
broker database, tier planning, the ledger, drafts, reports, **email sending (SMTP), verification-link
polling (IMAP), and the autonomous action queue (`next`)**. You (the agent) do the scanning and
form-driving with native tools: `web_extract` and `browser_exec` for searching and web forms, and
`cronjob` for recurring re-scans. Runs can be entered from a phone number alone (spam-callView the complete source on GitHubweb-pentest
When your agent uses it
Authorized web pentest: recon, proof-based exploits, report.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
3,262 tokensWhen the skill is activated
Reference documents
7,428 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ⚠<U+FE0F> Hard Guardrails — Read Before Every Engagement
- Phase 0: Engagement Setup
- Phase 1: Pre-Recon (Code Analysis, optional)
- Phase 2: Recon (Live, Read-Only)
- Phase 3: Vulnerability Analysis
- Phase 4: Exploitation (Proof-Based, Conditional)
- Phase 5: Reporting
- When to Stop
- What This Skill Does NOT Cover
- Further Reading
Scripts executed separately
scripts/recon-scan.sh
Files
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# Web Application Penetration Testing
A phased pentesting workflow for running web applications. Adapted from
Shannon's pipeline (Keygraph, AGPL — concepts only, no code borrowed).
Built around three rules:
1. No exploit, no report — every finding requires reproducible evidence.
2. Bounded scope — every active request goes against a target the operator
pre-declared. Off-scope hosts are refused.
3. Bypass exhaustion before false-positive dismissal — a "blocked" payload
is not a clean bill of health until you've tried the bypass set.
---
View the complete source on GitHubopenhue
When your agent uses it
Control Philips Hue lights, scenes, rooms via OpenHue CLI.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
783 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- When to Use
- Common Commands
- Quick Presets
- Notes
Files
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# OpenHue CLI
Control Philips Hue lights and scenes via a Hue Bridge from the terminal.
## Prerequisites
```bash
# Linux (pre-built binary — releases ship tarballs, not bare binaries)
curl -sL "https://github.com/openhue/openhue-cli/releases/latest/download/openhue_Linux_x86_64.tar.gz" \
| tar -xz -C ~/.hermes/cache/scratch openhue \
&& install -m 0755 ~/.hermes/cache/scratch/openhue ~/.local/bin/openhue
# (use openhue_Linux_arm64.tar.gz on ARM64)
# macOSView the complete source on GitHubreddit-reading
When your agent uses it
Read Reddit: subreddits, search, threads, users. No browser.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
1,564 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/reddit.py
Files
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# Reddit Reading Skill
Reads Reddit content — subreddit listings, site or subreddit search, full threads with
comments, and user activity — from a server or headless machine where the normal routes
are dead. It does not post, vote, or log in as a user. Idea credit: the per-platform
backend routing in [Agent Reach](https://github.com/Panniantong/Agent-Reach).
## When to Use
- "What is r/LocalLLaMA saying about X", "find Reddit threads on Y", "summarise this
Reddit thread", "what has u/someone posted lately".
- Any `reddit.com` URL the user shares. `web_extract`, `browser_exec` and the
`.json` endpoints all fail from server IPs (403 or a "Prove your humanity" wall);
this skill is the working path.View the complete source on GitHubast-grep
When your agent uses it
AST-aware structural code search and rewrite via ast-grep.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,452 tokensWhen the skill is activated
Reference documents
14,760 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Three things the agent must internalize
- The helper script — `scripts/ast_grep_helper.py`
- Direct `sg` use (when the helper isn't enough)
- Decision tree — what to use, when
- Always run dry-run first when rewriting
- When `sg` returns 0 matches but you know the code is there
- When to use YAML rules vs inline `-p` patterns
- Output discipline
- Required reading (in order of priority)
- Invariants (do not break)
Scripts executed separately
install.ps1install.shscripts/ast_grep_helper.pytests/smoke.ps1tests/smoke.sh
Files
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# ast-grep
`ast-grep` (binary also named `sg`) is an **AST-aware search and rewrite tool** across 25 languages. It treats your pattern as code, parses it the same way it parses your project, and matches structurally. It is the right tool whenever your question depends on **code shape** rather than text bytes.
This skill ships a Python wrapper at `scripts/ast_grep_helper.py` and platform install scripts at `install.sh` (POSIX) and `install.ps1` (Windows). The helper adds offline pattern validation, the two-pass write trick, and binary auto-resolution. Use it as your default entry point.
Upstream source: vendored from [code-yeongyu/ast-grep-skill](https://github.com/code-yeongyu/ast-grep-skill) (MIT), as shipped in oh-my-openagent's shared-skills bundle.
---
## When to use this skill
Use it whenever the question is about **code structure**, not bytes:
View the complete source on GitHubcode-wiki
When your agent uses it
Generate wiki docs + Mermaid diagrams for any codebase.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,692 tokensWhen the skill is activated
Reference documents
799 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Scope Control
- Re-Run / Update
- Pitfalls
- Verification
Files
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# Code Wiki Skill
Generate a full wiki for any codebase — overview, architecture, per-module deep-dives, Mermaid class and sequence diagrams. Inspired by Google CodeWiki, but works on local repos, private repos, and any language. Uses only existing Hermes tools (`terminal`, `read_file`, `search_files`, `write_file`); no Docker, no external services, no extra dependencies.
This skill produces **reference documentation** (what/how). It does not produce strategic narrative (why — that's a different skill).
## When to Use
- User says "document this codebase", "generate a wiki", "make architecture diagrams"
- Onboarding to an unfamiliar repo and wants a structured reference
- User points at a GitHub URL and asks for documentation
- Need a stable artifact (markdown + Mermaid) that renders on GitHub
Do NOT use this for:View the complete source on GitHubgrill-me
When your agent uses it
Adversarial plan interview before implementation.
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
1,083 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Core Mechanic: Frontier Rounds
- Question Coverage (work these branches into the tree)
- Synthesis (when the frontier is empty)
- Pitfalls
- Verification
Files
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# Grill Me
Stress-tests a plan through structured adversarial questioning before any
code is written. Models the plan as a **design tree** — every decision
branches into the decisions that hang off it — and interviews the user in
rounds until every branch is resolved and nothing is silently assumed.
Combines the phase discipline of the original with the frontier-rounds
mechanic from mattpocock/skills' `grilling`.
## When to Use
- User says "grill me", "interview my plan", "stress test this idea"
- Before complex work: auth flows, schema changes, migrations, paymentsView the complete source on GitHubhermes-agent-skill-authoring
When your agent uses it
Author in-repo SKILL.md files: frontmatter and structure.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,623 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Decide the Tier First: Bundled vs Optional
- Required Frontmatter
- Platform Gating: audit, don't trust
- Size Limits
- Body Structure (modern section order)
- Writing Quality Principles
- Tests and Docs (required for repo skills)
- Workflow
- Editing Existing In-Repo Skills
- Common Pitfalls
- Verification Checklist
Scripts executed separately
../../../scripts/run_tests.sh../../../tools/skill_manager_tool.py../../../website/scripts/generate-skill-docs.py../../../website/scripts/generate-skill-docs.py../../../website/scripts/generate-skill-docs.py../../../website/scripts/generate-skill-docs.py../../../website/scripts/generate-skill-docs.py../../../website/scripts/generate-skill-docs.py
Files
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# Authoring Hermes-Agent Skills (in-repo)
## Overview
There are two places a SKILL.md can live:
1. **User-local:** `~/.hermes/skills/<maybe-category>/<name>/SKILL.md` — personal, not shared. Created via `skill_manage(action='create')`.
2. **In-repo (this skill is about this case):** `skills/<category>/<name>/SKILL.md` or `optional-skills/<category>/<name>/SKILL.md` inside the hermes-agent repo — committed, shipped with the package. Use `write_file` + `git add`. `skill_manage(action='create')` does NOT target this tree.
In-repo skills must meet the repo's **hardline authoring standards** (see AGENTS.md, "Skill authoring standards (HARDLINE)" — that section is the source of truth; this skill is the operational walkthrough). Reviewers reject PRs that violate them, so meeting them up front is cheaper than a salvage pass later.
## When to Use
- User asks you to add a skill "in this branch / repo / commit"View the complete source on GitHubinspecting-hermes-desktop-dom
When your agent uses it
Read the live Hermes desktop DOM/CSS over CDP.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,630 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- The port
- Reading the DOM
- The question this is best at: which rule won?
- Your own isolated instance
- Pitfalls
Files
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# Inspecting the live Hermes desktop DOM
## Overview
When you are developing `apps/desktop` and the user is running that same app
(`hgui` / `npm run dev`), you can read the **live rendered DOM** of the window
they are looking at — computed styles, geometry, which CSS rule actually won,
console output — instead of inferring it from `.tsx` and being wrong.
Dev-server runs open a Chrome DevTools Protocol port on `127.0.0.1:9222`
automatically. The renderer is a Chromium page, so everything DevTools can read,
a script can read.
**This does not replace looking at it.** CDP answers *factual* questions ("whatView the complete source on GitHubnode-inspect-debugger
When your agent uses it
Debug Node.js via --inspect + Chrome DevTools Protocol CLI.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
2,998 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Quick Reference: `node inspect` REPL
- Attaching to a Running Process
- Programmatic CDP (scripting from terminal)
- Debugging Hermes ui-tui
- Running Vitest Tests Under the Debugger
- Heap Snapshots & CPU Profiles (Non-interactive)
- Common Pitfalls
- Verification Checklist
- One-Shot Recipes
Files
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# Node.js Inspect Debugger
## Overview
When `console.log` isn't enough, drive Node's built-in V8 inspector programmatically from the terminal. You get real breakpoints, step in/over/out, call-stack walking, local/closure scope dumps, and arbitrary expression evaluation in the paused frame.
Two tools, pick one:
- **`node inspect`** — built-in, zero install, CLI REPL. Best for quick poking.
- **`ndb` / CDP via `chrome-remote-interface`** — scriptable from Node/Python; best when you want to automate many breakpoints, collect state across runs, or debug non-interactively from an agent loop.
**Prefer `node inspect` first.** It's always available and the REPL is fast.
## When to UseView the complete source on GitHubpr-lens
When your agent uses it
Draw code changes as animated architecture/data-flow SVGs.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
2,506 tokensWhen the skill is activated
Reference documents
4,285 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- What makes a document worth reading
- Quick Reference
- Pitfalls
- Verification
Files
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# PR Lens Skill
PR Lens draws code as visually rich animated diagrams: diffs, architecture, data flows. You describe the diff or codebase as one JSON document (lanes, nodes, edges, ordered flows) and the CLI renders it as animated SVGs. There is no findings lens — PR Lens is a comprehension layer, not a review bot. There is no field for a bug, risk, or security note, and a document that invents one is rejected.
## When to Use
- Asked to diagram, visualise, or explain a code change or a system.
- A pull request should carry an architecture or data-flow diagram.
- Keywords: PR Lens, diagram, architecture, data flow, visualise, pull request.
## Prerequisites
- Node.js with `npx` (the CLI runs via `npx @coldtea/pr-lens-cli@latest`; no install step).
- `gh` (GitHub CLI) — optional, only for attaching diagrams to PRs.View the complete source on GitHubpython-debugpy
When your agent uses it
Debug Python: pdb REPL + debugpy remote (DAP).
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
3,925 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- pdb Quick Reference
- Recipe 1: Local breakpoint
- Recipe 2: Launch a script under pdb (no source edits)
- Recipe 3: Debug a pytest test
- Recipe 4: Post-mortem on any exception
- Recipe 5: Remote debug with debugpy (attach to running process)
- Debugging Hermes-specific Processes
- Common Pitfalls
- Verification Checklist
- One-Shot Recipes
Scripts executed separately
../../../hermes../../../scripts/_activation.sh../../../scripts/build/windows-deps.ps1../../../scripts/run-in-hermes-env../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests.sh../../../scripts/run_tests_parallel.py../../../scripts/tool_store_pins.py
Files
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Read SKILL.md
# Python Debugger (pdb + debugpy)
## Overview
Three tools, picked by situation:
| Tool | When |
|---|---|
| **`breakpoint()` + pdb** | Local, interactive, simplest. Add `breakpoint()` in the source, run normally, get a REPL at that line. |
| **`python -m pdb`** | Launch an existing script under pdb with no source edits. Useful for quick poking. |
| **`debugpy`** | Remote / headless / "attach to already-running process." Talks DAP, scriptable from terminal, works for long-lived processes (gateway, daemon, PTY children). |
**Start with `breakpoint()`.** It's the cheapest thing that works.
View the complete source on GitHubrest-graphql-debug
When your agent uses it
Debug REST/GraphQL APIs: status codes, auth, schemas, repro.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
4,007 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Core Principle
- 5-Minute Quickstart
- Layered Debug Flow
- HTTP Status Playbook
- Pagination & Idempotency
- Contract Validation
- Correlation IDs
- Regression Test Template
- Security
- Hermes Tool Patterns
- Output Format
- Related
Files
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# API Testing & Debugging
Drive REST and GraphQL diagnosis through Hermes tools — `terminal` for `curl`, `execute_code` for Python `requests`, `web_extract` for vendor docs. Isolate the failing layer before guessing at the fix.
## When to Use
- API returns unexpected status or body
- Auth fails (401/403 after token refresh, OAuth, API key)
- Works in Postman but fails in code
- Webhook / callback integration debugging
- Building or reviewing API integration tests
- Rate limiting or pagination issues
Skip for UI rendering, DB query tuning, or DNS/firewall infra (escalate).View the complete source on GitHubsubagent-driven-development
When your agent uses it
Execute plans via delegate_task subagents (2-stage review).
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,430 tokensWhen the skill is activated
Reference documents
1,902 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- The Process
- Task Granularity
- Red Flags — Never Do These
- Handling Issues
- Efficiency Notes
- Integration with Other Skills
- Example Workflow
- Remember
- Further reading (load when relevant)
Files
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# Subagent-Driven Development
## Overview
Execute implementation plans by dispatching fresh subagents per task with systematic two-stage review.
**Core principle:** Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration.
## When to Use
Use this skill when:
- You have an implementation plan (from the `plan` skill or user requirements)
- Tasks are mostly independent
- Quality and spec compliance are importantView the complete source on GitHubcloudflare-temporary-deploy
When your agent uses it
Deploy a Worker live, no account, via wrangler --temporary.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,921 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- When NOT to Use
- Prerequisites
- How to Run
- Quick Reference
- Pitfalls
- Verification
Scripts executed separately
scripts/parse_deploy_output.py
Files
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# Cloudflare Temporary Deploy Skill
Deploy a Cloudflare Worker to a live `workers.dev` URL with zero account setup, using `wrangler deploy --temporary`. Cloudflare provisions a throwaway account, deploys, and prints a claim URL valid for 60 minutes; unclaimed accounts auto-delete. This gives an agent a tight write → deploy → verify loop without any OAuth, signup, or token copy-paste.
This skill does NOT cover production deploys (use `wrangler login` + a permanent account for those), nor non-Worker Cloudflare products beyond the temporary-account limits below.
## When to Use
Load this skill when the user wants to:
- **Ship agent-written code to a live URL** without first creating a Cloudflare account — "deploy this and give me a link"
- **Iterate in a background/autonomous session** where a browser OAuth step would be a hard stop
- **Prototype or evaluate Workers** quickly with a throwaway, claimable target
- **Build a self-verifying deploy loop** — deploy, `curl` the live URL, confirm output matches the code, redeployView the complete source on GitHubhar-derived-api-client
When your agent uses it
Record a site's XHR into a HAR, derive an HTTP client.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
2,658 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/har_capture.pyscripts/har_capture_cdp.pyscripts/har_to_client.py
Files
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# HAR-Derived API Client
Drive a website once with a real browser while recording its network traffic
to a HAR file, then distill that HAR into the site's private JSON API so you
can call it directly with plain HTTP — far cheaper and faster than
browser-controlling the page on every request. Credit: trick by Jared Longster,
popularized by Dax (thdxr). This captures and replays; it does NOT bypass
auth, solve CAPTCHAs, or defeat bot-detection — if the site needs a logged-in
session, you carry its headers/cookies forward, you don't forge them.
The scripts are stdlib-plus-Playwright: capture needs Playwright, derivation
is pure stdlib, replay needs only `requests`/`httpx` (or `curl`).
Covers **every Hermes browser pathway**: the default local Chromium thatView the complete source on GitHubpage-agent
When your agent uses it
Embed an in-page natural-language GUI copilot in web apps.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
2,231 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- When NOT to use this skill
- Prerequisites
- Path 1 — 30-second demo via CDN (no install)
- Path 2 — npm install into your own web app (production use)
- Path 3 — clone the source repo (contributing, or hacking on it)
- Repo layout (Path 3)
- Verifying it works
- Pitfalls
- Reference
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# page-agent
alibaba/page-agent (https://github.com/alibaba/page-agent, 17k+ stars, MIT) is an in-page GUI agent written in TypeScript. It lives inside a webpage, reads the DOM as text (no screenshots, no multi-modal LLM), and executes natural-language instructions like "click the login button, then fill username as John" against the current page. Pure client-side — the host site just includes a script and passes an OpenAI-compatible LLM endpoint.
## When to use this skill
Load this skill when a user wants to:
- **Ship an AI copilot inside their own web app** (SaaS, admin panel, B2B tool, ERP, CRM) — "users on my dashboard should be able to type 'create invoice for Acme Corp and email it' instead of clicking through five screens"
- **Modernize a legacy web app** without rewriting the frontend — page-agent drops on top of existing DOM
- **Add accessibility via natural language** — voice / screen-reader users drive the UI by describing what they want
- **Demo or evaluate page-agent** against a local (Ollama) or hosted (Qwen, OpenAI, OpenRouter) LLM
- **Build interactive training / product demos** — let an AI walk a user through "how to submit an expense report" live in the real UI
View the complete source on GitHubpublish-site
When your agent uses it
Versioned site deploys to GitHub/Cloudflare/Netlify Pages.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,200 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Files
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# Publish Site
Take a website, dashboard, or web app the user built (or you built for them) and put it online on infrastructure the user owns — GitHub Pages by default, Cloudflare Pages or Netlify when they need more. The discipline: preview locally for sign-off, version every deploy with a git tag, deploy through a provider ladder, verify the live URL with a real HTTP check, and keep rollback one command away.
This skill covers static sites and SPA build output (plain HTML/CSS/JS, or the `dist/`/`build/` folder from Vite/Next-export/Astro/etc.). It does not cover server-side runtimes — for throwaway serverless deploys with zero account setup, use the `cloudflare-temporary-deploy` optional skill instead.
## When to Use
Load this skill when the user asks to:
- **Put a site online** — "publish this", "host this somewhere", "give me a link I can share"
- **Deploy a dashboard, report, portfolio, docs site, or prototype** you just generated
- **Update an already-published site** with new content (redeploy = new version)
- **Roll back** a bad deploy to the previous versionView the complete source on GitHubscrollcraft
When your agent uses it
Premium scroll-driven landing pages; scroll = timeline.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
3,025 tokensWhen the skill is activated
Reference documents
34,012 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What this is not
- Step 0: The interview
- Bootstrap
- Step 1: The brief, journey first
- Step 2: Grammar, gate, then score
- Step 3: Assets
- Step 4: Build the page
- Step 5: Verify by scrolling it
- Hard rules (ship-blockers)
- Output
- Pitfalls
Scripts executed separately
engine/scrollcraft.jsscripts/doctor.mjsscripts/encode.shscripts/kie.mjsscripts/serve.mjsscripts/shoot.mjsscripts/workspace.mjsscripts/worldflight-assert.mjs
Files
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# scrollcraft
Scroll is the only input every visitor already knows. This skill treats it as a
timeline: the wheel is a scrubber, the page is a film with real text on top,
and each section behaves differently enough that the visitor keeps going.
**What you produce:** an interview brief, a page grammar, a customer-journey
map, a feeling curve with one engineered peak, a scroll score, one signature
move, assets, one real HTML page on a token-driven design floor, and a strip of
screenshots proving it holds up at every scroll position.
Use for: "scrollytelling", "scroll animation site", "a site where scrolling
plays a video", "Apple-style landing page", "3D scroll world", "make my brand a
scroll experience", "this looks like a template", or any request for a siteView the complete source on GitHubyuanbao
When your agent uses it
Yuanbao (元宝) groups: @mention users, query info/members.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,028 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- CRITICAL: How Messaging Works
- Available Tools
- @Mention Workflow
- Send DM (Private Message) Workflow
- Query Group Info
- Query Members
- Notes
Files
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# Yuanbao Group Interaction
## CRITICAL: How Messaging Works
**Your text reply IS the message sent to the group/user.** The gateway automatically delivers your response text to the chat. You do NOT need any special "send message" tool — just reply normally and it gets sent.
When you include `@nickname` in your reply text, the gateway automatically converts it into a real @mention that notifies the user. This is built-in — you have full @mention capability.
**NEVER say you cannot send messages or @mention users. NEVER suggest the user do it manually. NEVER add disclaimers about permissions. Just reply with the text you want sent.**
## Available Tools
| Tool | When to use |
|------|------------|View the complete source on GitHubgoogle_meet
When your agent uses it
Join a Google Meet call, transcribe live captions, optionally speak in realtime, and do the followup work afterwards. Use when the user asks the agent to sit in on a meeting, take notes, summarize, respond in-call, or action items from it.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,802 tokensWhen the skill is activated
Reference documents
1,595 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Two modes
- Two locations
- Prerequisites the user must handle once
- Flow
- Tool reference
- Important limits
- Status dict reference
- Transcript location
- Safety
Scripts executed separately
../../hermes__init__.py_jsonfile.pyaudio_bridge.pycli.pymeet_bot.pynode/__init__.pynode/cli.pynode/client.pynode/protocol.pynode/registry.pynode/server.pyprocess_manager.pyrealtime/__init__.pyrealtime/openai_client.pytools.py
Files
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# google_meet
## When to use
The user says any of:
- "join my Meet at <url>"
- "take notes on this meeting"
- "summarize the meeting and send followups"
- "sit in on my standup"
- "be a bot in this call and speak up when X"
## Two modes
View the complete source on GitHubapple-notes
When your agent uses it
Manage Apple Notes via memo CLI: create, search, edit.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
595 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Limitations
- Rules
Files
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# Apple Notes
Use `memo` to manage Apple Notes directly from the terminal. Notes sync across all Apple devices via iCloud.
## Prerequisites
- **macOS** with Notes.app
- Install: `brew tap antoniorodr/memo && brew install antoniorodr/memo/memo`
- Grant Automation access to Notes.app when prompted (System Settings → Privacy → Automation)
## When to Use
- User asks to create, view, or search Apple Notes
- Saving information to Notes.app for cross-device accessView the complete source on GitHubapple-reminders
When your agent uses it
Apple Reminders via remindctl: add, list, complete.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
970 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Date Formats
- Rules
Files
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# Apple Reminders
Use `remindctl` to manage Apple Reminders directly from the terminal. Tasks sync across all Apple devices via iCloud.
## Prerequisites
- **macOS** with Reminders.app
- Install: `brew install steipete/tap/remindctl`
- Grant Reminders permission when prompted
- Check: `remindctl status` / Request: `remindctl authorize`
## When to Use
- User mentions "reminder" or "Reminders app"View the complete source on GitHubfindmy
When your agent uses it
Track Apple devices/AirTags via FindMy.app on macOS.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
979 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- When to Use
- Method 1: AppleScript + Screenshot (Basic)
- Method 2: Peekaboo UI Automation (Recommended)
- Workflow: Track AirTag Location Over Time
- Limitations
- Rules
Files
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# Find My (Apple)
Track Apple devices and AirTags via the FindMy.app on macOS. Since Apple doesn't
provide a CLI for FindMy, this skill uses AppleScript to open the app and
screen capture to read device locations.
## Prerequisites
- **macOS** with Find My app and iCloud signed in
- Devices/AirTags already registered in Find My
- Screen Recording permission for terminal (System Settings → Privacy → Screen Recording)
- **Optional but recommended**: Install `peekaboo` for better UI automation:
`brew install steipete/tap/peekaboo`
View the complete source on GitHubimessage
When your agent uses it
Send and receive iMessages/SMS via the imsg CLI on macOS.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
662 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Service Options
- Rules
- Example Workflow
Files
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# iMessage
Use `imsg` to read and send iMessage/SMS via macOS Messages.app.
## Prerequisites
- **macOS** with Messages.app signed in
- Install: `brew install steipete/tap/imsg`
- Grant Full Disk Access for terminal (System Settings → Privacy → Full Disk Access)
- Grant Automation permission for Messages.app when prompted
## When to Use
- User asks to send an iMessage or text messageView the complete source on GitHubclaude-code
When your agent uses it
Delegate coding to Claude Code CLI (features, PRs).
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
9,164 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- Two Orchestration Modes
- PTY Dialog Handling (CRITICAL for Interactive Mode)
- CLI Subcommands
- Print Mode Deep Dive
- Complete CLI Flags Reference
- Settings & Configuration
- Interactive Session: Slash Commands
- Interactive Session: Keyboard Shortcuts
- PR Review Pattern
- Parallel Claude Instances
- CLAUDE.md — Project Context File
- Custom Subagents
- Hooks — Automation on Events
- MCP Integration
- Monitoring Interactive Sessions
- Environment Variables
- Cost & Performance Tips
- Pitfalls & Gotchas
- Rules for Hermes Agents
Files
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# Claude Code — Hermes Orchestration Guide
Delegate coding tasks to [Claude Code](https://code.claude.com/docs/en/cli-reference) (Anthropic's autonomous coding agent CLI) via the Hermes terminal. Claude Code v2.x can read files, write code, run shell commands, spawn subagents, and manage git workflows autonomously.
## Prerequisites
- **Install:** `npm install -g @anthropic-ai/claude-code`
- **Auth:** run `claude` once to log in (browser OAuth for Pro/Max, or set `ANTHROPIC_API_KEY`)
- **Console auth:** `claude auth login --console` for API key billing
- **SSO auth:** `claude auth login --sso` for Enterprise
- **Check status:** `claude auth status` (JSON) or `claude auth status --text` (human-readable)
- **Health check:** `claude doctor` — checks auto-updater and installation health
- **Version check:** `claude --version` (requires v2.x+)
- **Update:** `claude update` or `claude upgrade`View the complete source on GitHubcodex
When your agent uses it
Delegate coding to OpenAI Codex CLI (features, PRs).
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,513 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Prerequisites
- One-Shot Tasks
- Background Mode (Long Tasks)
- Key Flags
- Hermes Gateway Caveat
- PR Reviews
- Parallel Issue Fixing with Worktrees
- Batch PR Reviews
- Rules
Files
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# Codex CLI
Delegate coding tasks to [Codex](https://github.com/openai/codex) via the Hermes terminal. Codex is OpenAI's autonomous coding agent CLI.
## When to use
- Building features
- Refactoring
- PR reviews
- Batch issue fixing
Requires the codex CLI and a git repository.
## PrerequisitesView the complete source on GitHubcomputer-use
When your agent uses it
Drive the desktop background-first; escalate on signal.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
4,355 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The canonical workflow
- Capture modes
- Actions
- The verify → escalate ladder (background-first)
- Page content is a separate toolset
- Background rules (the whole point)
- Drag & drop
- Scroll
- Managing what's focused
- Delivering screenshots to the user
- Safety — these are hard rules
- Failure modes — what to do when things go sideways
- When NOT to use `computer_use`
- Going deeper — read the cua-driver skill pack
Files
SKILL.mdcore17,989 bytesSKILL.mdcore17,989 bytes
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# Computer Use (universal, any-model, cross-platform)
You have a `computer_use` tool that drives the user's desktop in the
**background** — your actions do NOT move the user's cursor, steal
keyboard focus, or switch virtual desktops / Spaces. The user can keep
typing in their editor while you click around in a browser in another
window. This is the opposite of pyautogui-style automation.
Everything here works with any tool-capable model — Claude, GPT, Gemini,
or an open model on a local OpenAI-compatible endpoint. There is no
Anthropic-native schema to learn.
Hermes drives [cua-driver](https://github.com/trycua/cua) under the hood.
This skill teaches the Hermes `computer_use` **action vocabulary**, which isView the complete source on GitHubhermes-agent
When your agent uses it
Use, configure, theme, extend, and orchestrate Hermes Agent.
What it loads into context
Frontmatter
135 tokensAlways available to the agent
SKILL.md
3,957 tokensWhen the skill is activated
Reference documents
27,411 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scope & Verification
- Quick Start
- Key Paths
- Routing Table — load the reference for the task
- Spawning Additional Hermes Instances
- Surfaces (quick orientation)
- Hard Invariants (never violate, regardless of what you loaded)
Scripts executed separately
templates/clock.mjstemplates/plugin.js
Files
SKILL.mdcore15,834 bytesSKILL.mdcore15,834 bytesSKILL.mdcore15,834 bytesSKILL.mdcore15,834 bytesSKILL.mdcore15,834 bytesreferences/background-systems.mddoc5,069 bytesreferences/cli-reference.mddoc6,874 bytesreferences/configuration.mddoc5,087 bytesreferences/contributor-guide.mddoc5,931 bytesreferences/delegate-task-concurrency-diagnosis.mddoc4,660 bytesreferences/desktop-plugins.mddoc15,157 bytesreferences/native-mcp.mddoc12,581 bytesreferences/petdex.mddoc3,244 bytesreferences/portal-auth-for-third-party-apps.mddoc6,579 bytesreferences/project-context-files.mddoc3,285 bytesreferences/providers-and-models.mddoc3,410 bytesreferences/security-privacy.mddoc3,393 bytesreferences/slash-commands.mddoc5,379 bytesreferences/themes.mddoc6,887 bytesreferences/troubleshooting.mddoc3,807 bytesreferences/tui-widgets.mddoc6,484 bytesreferences/webhooks.mddoc8,466 bytesreferences/windows-quirks.mddoc2,671 bytestemplates/clock.mjsruns1,423 bytestemplates/plugin.jsruns3,141 bytestemplates/skin.yamlasset1,389 bytes
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# Hermes Agent
Hermes Agent is an open-source AI agent framework by Nous Research that runs in your terminal, a native desktop app, messaging platforms, and IDEs. It's in the same category as Claude Code (Anthropic), Codex (OpenAI), and OpenClaw — autonomous coding and task-execution agents that use tool calling to interact with your system. Hermes works with any LLM provider (OpenRouter, Anthropic, OpenAI, Google, DeepSeek, xAI, local models, and 20+ others) and runs on Linux, macOS, Windows, and WSL.
What makes Hermes different:
- **Self-improving through skills** — Hermes learns from experience by saving reusable procedures as skills that load into future sessions.
- **Persistent memory across sessions** — remembers who you are, your preferences, environment details, and lessons learned. Pluggable memory backends.
- **Multi-platform gateway** — the same agent runs on Telegram, Discord, Slack, WhatsApp, iMessage, Signal, Matrix, Teams, Email, and a dozen more platforms with full tool access, not just chat.
- **Many surfaces** — the same agent core drives the CLI, the Ink TUI, a native Electron desktop app, a web dashboard, and an ACP server for IDEs (VS Code / Zed / JetBrains).
- **Provider-agnostic** — swap models and providers mid-workflow; credential pools rotate across multiple API keys automatically.
- **Profiles** — run multiple independent Hermes instances with isolated configs, sessions, skills, and memory.
- **Extensible & themeable** — plugins, MCP servers, custom tools, webhook triggers, cron scheduling, skins that theme every surface, desktop UI plugins, TUI widgets, and pet mascots.
View the complete source on GitHubarchitecture-diagram
When your agent uses it
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,536 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scope
- Workflow
- Design System & Visual Language
- Technical Implementation Details
- Document Structure
- Output Requirements
- Template Reference
Files
SKILL.mdcore5,830 bytesSKILL.mdcore5,830 bytestemplates/template.htmlasset12,483 bytes
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# Architecture Diagram Skill
Generate professional, dark-themed technical architecture diagrams as standalone HTML files with inline SVG graphics. No external tools, no API keys, no rendering libraries — just write the HTML file and open it in a browser.
## Scope
**Best suited for:**
- Software system architecture (frontend / backend / database layers)
- Cloud infrastructure (VPC, regions, subnets, managed services)
- Microservice / service-mesh topology
- Database + API map, deployment diagrams
- Anything with a tech-infra subject that fits a dark, grid-backed aesthetic
**Look elsewhere first for:**View the complete source on GitHubascii-video
When your agent uses it
ASCII video: convert video/audio to colored ASCII MP4/GIF.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
3,511 tokensWhen the skill is activated
Reference documents
93,410 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- What's inside
- Creative Standard
- Modes
- Stack
- Pipeline Architecture
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Files
README.mddoc16,440 bytesSKILL.mdcore15,011 bytesSKILL.mdcore15,011 bytesSKILL.mdcore15,011 bytesSKILL.mdcore15,011 bytesreferences/architecture.mddoc33,004 bytesreferences/composition.mddoc34,942 bytesreferences/effects.mddoc72,928 bytesreferences/inputs.mddoc24,575 bytesreferences/optimization.mddoc24,265 bytesreferences/scenes.mddoc37,487 bytesreferences/shaders.mddoc50,435 bytesreferences/troubleshooting.mddoc13,246 bytes
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# ASCII Video Production Pipeline
## When to use
Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.
## What's inside
Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering.
## Creative Standard
This is visual art. ASCII characters are the medium; cinema is the standard.
View the complete source on GitHubbaoyu-infographic
When your agent uses it
Infographics: 21 layouts x 21 styles (信息图, 可视化).
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
2,650 tokensWhen the skill is activated
Reference documents
13,664 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Options
- Layout Gallery
- Style Gallery
- Recommended Combinations
- Keyword Shortcuts
- Output Structure
- Core Principles
- Workflow
- References
- Pitfalls
Files
PORT_NOTES.mddoc1,712 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesSKILL.mdcore10,490 bytesreferences/analysis-framework.mddoc7,490 bytesreferences/base-prompt.mddoc1,082 bytesreferences/layouts/bento-grid.mddoc851 bytesreferences/layouts/binary-comparison.mddoc1,278 bytesreferences/layouts/bridge.mddoc907 bytesreferences/layouts/circular-flow.mddoc759 bytesreferences/layouts/comic-strip.mddoc847 bytesreferences/layouts/comparison-matrix.mddoc893 bytesreferences/layouts/dashboard.mddoc776 bytesreferences/layouts/dense-modules.mddoc3,673 bytesreferences/layouts/funnel.mddoc821 bytesreferences/layouts/hierarchical-layers.mddoc1,180 bytesreferences/layouts/hub-spoke.mddoc865 bytesreferences/layouts/iceberg.mddoc884 bytesreferences/layouts/isometric-map.mddoc833 bytesreferences/layouts/jigsaw.mddoc844 bytesreferences/layouts/linear-progression.mddoc1,238 bytesreferences/layouts/periodic-table.mddoc774 bytesreferences/layouts/story-mountain.mddoc815 bytesreferences/layouts/structural-breakdown.mddoc1,118 bytesreferences/layouts/tree-branching.mddoc913 bytesreferences/layouts/venn-diagram.mddoc825 bytesreferences/layouts/winding-roadmap.mddoc799 bytesreferences/structured-content-template.mddoc5,515 bytesreferences/styles/aged-academia.mddoc990 bytesreferences/styles/bold-graphic.mddoc920 bytesreferences/styles/chalkboard.mddoc2,204 bytesreferences/styles/claymation.mddoc654 bytesreferences/styles/corporate-memphis.mddoc612 bytesreferences/styles/craft-handmade.mddoc1,429 bytesreferences/styles/cyberpunk-neon.mddoc625 bytesreferences/styles/hand-drawn-edu.mddoc2,648 bytesreferences/styles/ikea-manual.mddoc572 bytesreferences/styles/kawaii.mddoc653 bytesreferences/styles/knolling.mddoc584 bytesreferences/styles/lego-brick.mddoc574 bytesreferences/styles/morandi-journal.mddoc2,233 bytesreferences/styles/origami.mddoc607 bytesreferences/styles/pixel-art.mddoc562 bytesreferences/styles/pop-laboratory.mddoc2,174 bytesreferences/styles/retro-pop-grid.mddoc2,106 bytesreferences/styles/storybook-watercolor.mddoc703 bytesreferences/styles/subway-map.mddoc591 bytesreferences/styles/technical-schematic.mddoc983 bytesreferences/styles/ui-wireframe.mddoc596 bytes
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# Infographic Generator
Adapted from [baoyu-infographic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.
Two dimensions: **layout** (information structure) × **style** (visual aesthetics). Freely combine any layout with any style.
## When to Use
Trigger this skill when the user asks to create an infographic, visual summary, information graphic, or uses terms like "信息图", "可视化", or "高密度信息大图". The user provides content (text, file path, URL, or topic) and optionally specifies layout, style, aspect ratio, or language.
## Options
| Option | Values |
|--------|--------|View the complete source on GitHubclaude-design
When your agent uses it
Design one-off HTML artifacts (landing, deck, prototype).
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
5,418 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When To Use This Skill vs `popular-web-designs` vs `design-md`
- Runtime Mode
- Core Identity
- When To Use
- Design Principle: Start From Context, Not Vibes
- Asking Questions
- Surface-First: Commit to a Composition Before Touching Tokens
- Workflow
- Artifact Format Rules
- HTML / CSS / JS Standards
- React Guidance for Standalone HTML
- Deck Rules
- Prototype Rules
- Variation Rules
- Tweakable Designs in CLI/API Mode
- Content Discipline
- Anti-Slop Rules
- Slop Diagnostic: Score Before You Fix
- Typography
- Color
- Layout and Composition
- Motion
- Images and Icons
- Source-Code Fidelity
- Reading Documents and Assets
- Copyright and Reference Models
- Verification
- Final Response Format
- Portable Opening Prompt Pattern
- Pitfalls
Files
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# Claude Design for CLI/API Agents
Use this skill when the user asks for design work that would normally fit Claude Design, but the agent is running in a CLI/API environment instead of the hosted Claude Design web UI.
The goal is to preserve Claude Design's useful design behavior and taste while removing hosted-tool plumbing that does not exist in normal agent environments.
**Before starting, check for other web-design skills like `popular-web-designs` (ready-to-paste design systems for Stripe, Linear, Vercel, Notion, etc.) and `design-md` (Google's DESIGN.md token spec format; an optional skill, installed with `hermes skills install official/creative/design-md`).** If the user wants a known brand's look, load `popular-web-designs` alongside this one and let it supply the visual vocabulary. If the deliverable is a token spec file rather than a rendered artifact, use `design-md` instead. Full decision table below.
## When To Use This Skill vs `popular-web-designs` vs `design-md`
Hermes has three design-related skills under `skills/creative/`. They do different jobs — load the right one (or combine them):
| Skill | What it gives you | Use when the user wants... |
|---|---|---|View the complete source on GitHubhumanizer
When your agent uses it
Humanize text: strip AI-isms and add real voice.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
7,480 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- How to use it in Hermes
- Your task
- Voice Calibration (optional)
- PERSONALITY AND SOUL
- CONTENT PATTERNS
- LANGUAGE AND GRAMMAR PATTERNS
- STYLE PATTERNS
- COMMUNICATION PATTERNS
- FILLER AND HEDGING
- STYLE, RHYTHM, AND RHETORIC PATTERNS
- Process
- Output Format
- Full Example
- Attribution
Files
LICENSEasset1,066 bytesSKILL.mdcore34,463 bytes
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# Humanizer: Remove AI Writing Patterns
Identify and remove signs of AI-generated text to make writing sound natural and human. Based on Wikipedia's "Signs of AI writing" guide (maintained by WikiProject AI Cleanup), derived from observations of thousands of AI-generated text instances.
**Key insight:** LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely completion, which is how the telltale patterns below get baked in.
## When to use this skill
Load this skill whenever the user asks to:
- "humanize", "de-AI", "de-slop", or "un-ChatGPT" a piece of text
- rewrite something so it doesn't sound like it was written by an LLM
- edit a draft (blog post, essay, PR description, docs, memo, email, tweet, resume bullet) to sound more natural
- match their voice in writing they're producing
- review text for AI tells before publishingView the complete source on GitHubmanim-video
When your agent uses it
Manim CE animations: 3Blue1Brown math/algo videos.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
3,117 tokensWhen the skill is activated
Reference documents
23,760 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Creative Standard
- Prerequisites
- Modes
- Stack
- Pipeline
- Project Structure
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Scripts executed separately
scripts/setup.sh
Files
README.mddoc886 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesSKILL.mdcore12,150 bytesreferences/animation-design-thinking.mddoc7,205 bytesreferences/animations.mddoc8,836 bytesreferences/camera-and-3d.mddoc4,132 bytesreferences/decorations.mddoc5,006 bytesreferences/equations.mddoc6,086 bytesreferences/graphs-and-data.mddoc4,628 bytesreferences/mobjects.mddoc9,739 bytesreferences/paper-explainer.mddoc9,333 bytesreferences/production-quality.mddoc5,904 bytesreferences/rendering.mddoc5,375 bytesreferences/scene-planning.mddoc2,751 bytesreferences/troubleshooting.mddoc4,319 bytesreferences/updaters-and-trackers.mddoc8,500 bytesreferences/visual-design.mddoc4,762 bytesscripts/setup.shruns921 bytes
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# Manim Video Production Pipeline
## When to use
Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories using Manim Community Edition.
## Creative Standard
This is educational cinema. Every frame teaches. Every animation reveals structure.
**Before writing a single line of code**, articulate the narrative arc. What misconception does this correct? What is the "aha moment"? What visual story takes the viewer from confusion to understanding? The user's prompt is a starting point — interpret it with pedagogical ambition.
**Geometry before algebra.** Show the shape first, the equation second. Visual memory encodes faster than symbolic memory. When the viewer sees the geometric pattern before the formula, the equation feels earned.
View the complete source on GitHubp5js
When your agent uses it
p5.js sketches: gen art, shaders, interactive, 3D.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
6,887 tokensWhen the skill is activated
Reference documents
34,392 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- What's inside
- Creative Standard
- Modes
- Stack
- Pipeline
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Scripts executed separately
scripts/export-frames.jsscripts/render.shscripts/serve.shscripts/setup.sh
Files
README.mddoc3,316 bytesSKILL.mdcore27,534 bytesreferences/animation.mddoc10,456 bytesreferences/color-systems.mddoc8,647 bytesreferences/core-api.mddoc9,841 bytesreferences/export-pipeline.mddoc15,611 bytesreferences/interaction.mddoc8,345 bytesreferences/shapes-and-geometry.mddoc7,213 bytesreferences/troubleshooting.mddoc13,041 bytesreferences/typography.mddoc6,779 bytesreferences/visual-effects.mddoc23,456 bytesreferences/webgl-and-3d.mddoc8,893 bytesscripts/export-frames.jsruns5,398 bytesscripts/render.shruns2,919 bytesscripts/serve.shruns841 bytesscripts/setup.shruns2,483 bytestemplates/viewer.htmlasset10,858 bytes
Read SKILL.md
# p5.js Production Pipeline
## When to use
Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.
## What's inside
Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems, flow fields, shaders (GLSL), pixel manipulation, kinetic typography, WebGL scenes, audio analysis, mouse/keyboard interaction, and headless high-res export.
## Creative Standard
This is visual art rendered in the browser. The canvas is the medium; the algorithm is the brush.
View the complete source on GitHubpopular-web-designs
When your agent uses it
54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
What it loads into context
Frontmatter
149 tokensAlways available to the agent
SKILL.md
2,482 tokensWhen the skill is activated
Reference documents
239,558 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Related design skills
- How to Use
- HTML Generation Pattern
- Font Substitution Reference
- Design Catalog
- Choosing a Design
Files
SKILL.mdcore9,869 bytestemplates/airbnb.mddoc14,847 bytestemplates/airtable.mddoc4,323 bytestemplates/apple.mddoc21,145 bytestemplates/bmw.mddoc10,608 bytestemplates/cal.mddoc18,605 bytestemplates/claude.mddoc21,101 bytestemplates/clay.mddoc18,128 bytestemplates/clickhouse.mddoc16,203 bytestemplates/cohere.mddoc15,566 bytestemplates/coinbase.mddoc5,804 bytestemplates/composio.mddoc21,755 bytestemplates/cursor.mddoc19,890 bytestemplates/elevenlabs.mddoc16,001 bytestemplates/expo.mddoc19,489 bytestemplates/figma.mddoc12,426 bytestemplates/framer.mddoc18,370 bytestemplates/hashicorp.mddoc16,448 bytestemplates/ibm.mddoc20,389 bytestemplates/intercom.mddoc7,185 bytestemplates/kraken.mddoc5,117 bytestemplates/linear.app.mddoc23,254 bytestemplates/lovable.mddoc18,183 bytestemplates/minimax.mddoc15,094 bytestemplates/mintlify.mddoc18,226 bytestemplates/miro.mddoc5,561 bytestemplates/mistral.ai.mddoc16,444 bytestemplates/mongodb.mddoc15,831 bytestemplates/notion.mddoc19,113 bytestemplates/nvidia.mddoc17,857 bytestemplates/ollama.mddoc16,330 bytestemplates/opencode.ai.mddoc16,330 bytestemplates/pinterest.mddoc12,717 bytestemplates/posthog.mddoc19,370 bytestemplates/raycast.mddoc19,284 bytestemplates/replicate.mddoc14,970 bytestemplates/resend.mddoc19,224 bytestemplates/revolut.mddoc9,386 bytestemplates/runwayml.mddoc15,120 bytestemplates/sanity.mddoc22,064 bytestemplates/sentry.mddoc15,562 bytestemplates/spacex.mddoc12,087 bytestemplates/spotify.mddoc13,754 bytestemplates/stripe.mddoc21,286 bytestemplates/supabase.mddoc15,615 bytestemplates/superhuman.mddoc19,737 bytestemplates/together.ai.mddoc15,096 bytestemplates/uber.mddoc19,453 bytestemplates/vercel.mddoc20,250 bytestemplates/voltagent.mddoc27,114 bytestemplates/warp.mddoc17,464 bytestemplates/webflow.mddoc4,886 bytestemplates/wise.mddoc9,231 bytestemplates/x.ai.mddoc17,066 bytestemplates/zapier.mddoc19,507 bytes
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# Popular Web Designs
54 real-world design systems ready for use when generating HTML/CSS. Each template captures a
site's complete visual language: color palette, typography hierarchy, component styles, spacing
system, shadows, responsive behavior, and practical agent prompts with exact CSS values.
## Related design skills
- **`claude-design`** — use for the design *process and taste* (scoping a brief,
producing variants, verifying a local HTML artifact, avoiding AI-design slop).
Pair it with this skill when the user wants a thoughtfully-designed page styled
after a known brand: `claude-design` drives the workflow, this skill supplies
the visual vocabulary.
- **`design-md`** (optional: `hermes skills install official/creative/design-md`) —View the complete source on GitHubsongwriting-and-ai-music
When your agent uses it
Songwriting craft and Suno AI music prompts.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
2,831 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Song Structure (Pick One or Invent Your Own)
- 2. Rhyme, Meter, and Sound
- 3. Emotional Arc and Dynamics
- 4. Writing Lyrics That Work
- 5. Parody and Adaptation
- 6. Suno AI Prompt Engineering
- 7. Phonetic Tricks for AI Singers
- 8. Workflow
- 9. Lessons Learned
- 10. Local / Open-Source Music Generation
Files
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# Songwriting & AI Music Generation
Everything here is a GUIDELINE, not a rule. Art breaks rules on purpose.
Use what serves the song. Ignore what doesn't.
---
## 1. Song Structure (Pick One or Invent Your Own)
Common skeletons — mix, modify, or throw out as needed:
```
ABABCB Verse/Chorus/Verse/Chorus/Bridge/Chorus (most pop/rock)
AABA Verse/Verse/Bridge/Verse (refrain-based) (jazz standards, ballads)View the complete source on GitHubsdlc-review
When your agent uses it
Review Kanban handoffs and route verified outcomes.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,861 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Review Lenses
- Procedure
- Pitfalls
- Verification
Files
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# SDLC Review Skill
Independently verify work handed from a Kanban implementation run to the review lane, then approve it, request changes, or escalate. This skill reviews the deliverable and its evidence; it does not take over the implementer's work.
## When to Use
Use this skill when all of the following are true:
- the dispatcher spawned you for a task claimed from the `review` lane;
- an implementer submitted a `review_requested` handoff;
- the task needs an independent verdict before it can be completed.
Do not use it for a separate downstream review card. A downstream card is ordinary implementation work with a review-oriented specification and completes through its own lifecycle.
View the complete source on GitHubemail-inbox-triage
When your agent uses it
Triage an inbox: prioritize threads, draft replies safely.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,142 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Output Shape
- Pitfalls
- Verification
Files
SKILL.mdcore5,447 bytes
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# Email Inbox Triage
Turn a mailbox into a bounded queue of decisions. This skill owns thread-aware prioritization and reply policy; connector skills (`himalaya`, `google-workspace`) own provider commands.
## When to Use
- "What emails need my attention?"
- "Triage today's inbox."
- "Draft replies to anything urgent."
- "Get me to inbox zero."
- "Find unanswered customer/vendor messages."
Don't use for: newsletter campaigns, or when the user only asks to retrieve one known message (use the connector skill directly).
View the complete source on GitHubhimalaya
When your agent uses it
Himalaya CLI: IMAP/SMTP email from terminal.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,835 tokensWhen the skill is activated
Reference documents
2,453 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- References
- Prerequisites
- Configuration Setup
- Hermes Integration Notes
- Common Operations
- Multiple Accounts
- Attachments
- Output Formats
- Debugging
- Tips
Files
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Read SKILL.md
# Himalaya Email CLI
Himalaya is a CLI email client that lets you manage emails from the terminal using IMAP, SMTP, Notmuch, or Sendmail backends.
This skill is separate from the Hermes Email gateway adapter. The gateway
adapter lets people email the agent and uses Hermes' built-in IMAP/SMTP
adapter; this skill lets the agent operate a mailbox from terminal tools and
requires the external `himalaya` CLI.
## References
- `references/configuration.md` (config file setup + IMAP/SMTP authentication)
- `references/message-composition.md` (MML syntax for composing emails)
View the complete source on GitHubyoutube-content
When your agent uses it
YouTube transcripts to summaries, threads, blogs.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
981 tokensWhen the skill is activated
Reference documents
315 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Setup
- Helper Script
- Output Formats
- Workflow
- Error Handling
Scripts executed separately
scripts/fetch_transcript.py
Files
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# YouTube Content Tool
## When to use
Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video. Transforms transcripts into structured content (chapters, summaries, threads, blog posts).
Extract transcripts from YouTube videos and convert them into useful formats.
## Setup
Use `terminal` with the Python from a PM-prepared Hermes source checkout. The
`youtube` extra declares the helper's dependency; do not install packages into
Hermes with raw pip or project-discovering `uv run`.
View the complete source on GitHubobsidian
When your agent uses it
Read, search, create, and edit notes in the Obsidian vault.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
724 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Vault path
- Read a note
- List notes
- Search
- Create a note
- Append to a note
- Targeted edits
- Wikilinks
Files
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# Obsidian Vault
Use this skill for filesystem-first Obsidian vault work: reading notes, listing notes, searching note files, creating notes, appending content, and adding wikilinks.
## Vault path
Use a known or resolved vault path before calling file tools.
The documented vault-path convention is the `OBSIDIAN_VAULT_PATH` environment variable, for example from `${HERMES_HOME:-~/.hermes}/.env`. If it is unset, use `~/Documents/Obsidian Vault`.
File tools do not expand shell variables. Do not pass paths containing `$OBSIDIAN_VAULT_PATH` to `read_file`, `write_file`, `patch`, or `search_files`; resolve the vault path first and pass a concrete absolute path. Vault paths may contain spaces, which is another reason to prefer file tools over shell commands.
If the vault path is unknown, `terminal` is acceptable for resolving `OBSIDIAN_VAULT_PATH` or checking whether the fallback path exists. Once the path is known, switch back to file tools.
View the complete source on GitHubdocument-to-action-items
When your agent uses it
Extract cited obligations, deadlines, tasks from documents.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
808 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Pitfalls
- Verification
Files
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# Document to Action Items
Turn documents into cited facts and proposed actions. Extraction is not legal advice, and low-confidence OCR or ambiguous language must remain visible. The `pdf` and `microsoft-office` skills own extraction mechanics; this skill owns what happens to the extracted content.
## When to Use
- "Extract deadlines and obligations from this contract."
- "Turn this report into tasks."
- "Read these scanned forms and structure the data."
- "Find risks, owners, and follow-ups in these attachments."
Don't use for: plain text extraction with no downstream structuring (load `pdf` directly).
## ProcedureView the complete source on GitHubgoogle-workspace
When your agent uses it
Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
What it loads into context
Frontmatter
158 tokensAlways available to the agent
SKILL.md
3,774 tokensWhen the skill is activated
Reference documents
1,292 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- References
- Scripts
- First-Time Setup
- Usage
- Output Format
- Rules
- Troubleshooting
- Revoking Access
Scripts executed separately
scripts/_hermes_home.pyscripts/google_api.pyscripts/gws_bridge.pyscripts/setup.py
Files
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Read SKILL.md
# Google Workspace
Gmail, Calendar, Drive, Contacts, Sheets, and Docs — through Hermes-managed OAuth and a thin CLI wrapper. When `gws` is installed, the skill uses it as the execution backend for broader Google Workspace coverage; otherwise it falls back to the bundled Python client implementation.
## References
- `references/gmail-search-syntax.md` — Gmail search operators (is:unread, from:, newer_than:, etc.)
- `references/daily-brief.md` — daily/morning brief procedure: schedule + conflicts + meeting prep + urgent mail from Gmail and Calendar. Load it when the user asks for a morning brief, meeting preparation, or "what's on my calendar and what email needs attention."
## Scripts
- `scripts/setup.py` — OAuth2 setup (run once to authorize)
- `scripts/google_api.py` — compatibility wrapper CLI. It prefers `gws` for operations when available, while preserving Hermes' existing JSON output contract.
View the complete source on GitHubmaps
When your agent uses it
Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,740 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- Commands
- Working With Telegram Location Pins
- Workflow Examples
- Pitfalls
- Verification
Scripts executed separately
scripts/maps_client.py
Files
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# Maps Skill
Location intelligence using free, open data sources. 8 commands, 44 POI
categories, zero dependencies (Python stdlib only), no API key required.
Data sources: OpenStreetMap/Nominatim, Overpass API, OSRM, TimeAPI.io.
This skill supersedes the old `find-nearby` skill — all of find-nearby's
functionality is covered by the `nearby` command below, with the same
`--near "<place>"` shortcut and multi-category support.
## When to Use
- User sends a Telegram location pin (latitude/longitude in the message) → `nearby`View the complete source on GitHubmeeting-action-items
When your agent uses it
Turn meeting notes into cited decisions, owners, tickets.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
769 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Pitfalls
- Verification
Files
SKILL.mdcore3,808 bytesSKILL.mdcore3,808 bytes
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# Meeting Action Items
Convert an existing transcript or notes set into accountable follow-through. `teams-meeting-pipeline` can retrieve Teams artifacts; this skill begins once notes/transcript content is available, from any source.
## When to Use
- "Extract action items from this meeting."
- "What did we decide and who owns what?"
- "Draft the follow-up and create tickets."
- "Reconcile these notes with the existing project board."
Don't use for: retrieving meeting recordings or transcripts (use `teams-meeting-pipeline` or the relevant connector first).
## ProcedureView the complete source on GitHubmicrosoft-office
When your agent uses it
Create, read, and edit Word, PowerPoint, and Excel files.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
2,265 tokensWhen the skill is activated
Reference documents
9,359 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Converting to PDF
- Pitfalls
- Verification
Scripts executed separately
scripts/csv_to_xlsx.pyscripts/docx_comments.pyscripts/docx_common.pyscripts/docx_create.pyscripts/docx_edit.pyscripts/docx_read.pyscripts/docx_revisions.pyscripts/docx_template.pyscripts/docx_validate.pyscripts/pptx_create.pyscripts/pptx_edit.pyscripts/pptx_from_template.pyscripts/pptx_read.pyscripts/pptx_render.pyscripts/xlsx_create.pyscripts/xlsx_edit.pyscripts/xlsx_read.pyscripts/xlsx_recalc.pyscripts/xlsx_restructure.pyscripts/xlsx_to_csv.pytests/test_docx_skill.pytests/test_powerpoint_skill.pytests/test_xlsx_skill.py
Files
LICENSEasset1,070 bytesSKILL.mdcore8,073 bytesSKILL.mdcore8,073 bytesSKILL.mdcore8,073 bytesSKILL.mdcore8,073 bytesreferences/excel.mddoc8,761 bytesreferences/powerpoint.mddoc9,569 bytesreferences/restructuring.mddoc3,799 bytesreferences/revisions-and-comments.mddoc3,997 bytesreferences/word.mddoc9,404 bytesscripts/csv_to_xlsx.pyruns3,430 bytesscripts/docx_comments.pyruns10,640 bytesscripts/docx_common.pyruns3,314 bytesscripts/docx_create.pyruns6,387 bytesscripts/docx_edit.pyruns8,619 bytesscripts/docx_read.pyruns6,231 bytesscripts/docx_revisions.pyruns4,550 bytesscripts/docx_template.pyruns2,482 bytesscripts/docx_validate.pyruns6,153 bytesscripts/pptx_create.pyruns9,044 bytesscripts/pptx_edit.pyruns17,225 bytesscripts/pptx_from_template.pyruns3,442 bytesscripts/pptx_read.pyruns4,761 bytesscripts/pptx_render.pyruns3,562 bytesscripts/xlsx_create.pyruns9,842 bytesscripts/xlsx_edit.pyruns10,812 bytesscripts/xlsx_read.pyruns5,790 bytesscripts/xlsx_recalc.pyruns4,170 bytesscripts/xlsx_restructure.pyruns15,962 bytesscripts/xlsx_to_csv.pyruns2,351 bytestests/test_docx_skill.pyruns22,211 bytestests/test_powerpoint_skill.pyruns20,443 bytestests/test_xlsx_skill.pyruns23,955 bytes
Read SKILL.md
# Microsoft Office Skill
Create, read, edit, and template Word (`.docx`), PowerPoint (`.pptx`), and
Excel (`.xlsx`, `.csv`) files offline with small Python CLIs built on
python-docx, python-pptx, and openpyxl. Office itself is never needed. It does
not open legacy binary formats (`.doc`, `.ppt`, `.xls`) and does not render
pages without LibreOffice.
## When to Use
Pick the format, then read its reference with `skill_view` before working:
| File | Reference | Typical asks |
| --- | --- | --- |View the complete source on GitHubWhen your agent uses it
PDF files: create, read, merge, fill, OCR, edit text.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,992 tokensWhen the skill is activated
Reference documents
4,047 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Pitfalls
- Verification
Scripts executed separately
scripts/_raster.pyscripts/extract_marker.pyscripts/extract_pymupdf.pyscripts/pdf_create.pyscripts/pdf_fill_form.pyscripts/pdf_form_layout.pyscripts/pdf_make_form.pyscripts/pdf_merge.pyscripts/pdf_meta.pyscripts/pdf_page_image.pyscripts/pdf_read.pyscripts/pdf_secure.pyscripts/pdf_split.pyscripts/pdf_stamp.pyscripts/pdf_watermark.pytests/test_pdf_skill.py
Files
LICENSEasset1,070 bytesSKILL.mdcore15,308 bytesSKILL.mdcore15,308 bytesSKILL.mdcore15,308 bytesSKILL.mdcore15,308 bytesSKILL.mdcore15,308 bytesreferences/forms.mddoc3,950 bytesreferences/nano-pdf-editing.mddoc1,342 bytesreferences/ocr-extraction.mddoc8,739 bytesscripts/_raster.pyruns2,679 bytesscripts/extract_marker.pyruns3,458 bytesscripts/extract_pymupdf.pyruns3,671 bytesscripts/pdf_create.pyruns4,638 bytesscripts/pdf_fill_form.pyruns4,721 bytesscripts/pdf_form_layout.pyruns7,131 bytesscripts/pdf_make_form.pyruns6,064 bytesscripts/pdf_merge.pyruns1,762 bytesscripts/pdf_meta.pyruns4,534 bytesscripts/pdf_page_image.pyruns3,665 bytesscripts/pdf_read.pyruns5,971 bytesscripts/pdf_secure.pyruns2,755 bytesscripts/pdf_split.pyruns3,121 bytesscripts/pdf_stamp.pyruns5,855 bytesscripts/pdf_watermark.pyruns1,928 bytestests/test_pdf_skill.pyruns18,932 bytes
Read SKILL.md
# PDF Skill
Create PDFs from structured specs, build and fill AcroForm forms (with layout linting and visual overlays), extract text/tables/metadata, merge/split/rotate/watermark/stamp pages, export page images, manage metadata and attachments, and encrypt/decrypt — using pypdf, reportlab, and pdfplumber. Two absorbed capabilities live in references/ (read the matching file before those tasks):
- **Scanned/image-only PDFs and OCR** (pymupdf fast path, marker-pdf quality path, scripts/extract_pymupdf.py + scripts/extract_marker.py): `references/ocr-extraction.md`
- **Editing text inside an existing PDF via natural-language prompts** (nano-pdf CLI): `references/nano-pdf-editing.md`
## When to Use
- Generate a report, invoice, or multi-page document as PDF.
- Build a fillable AcroForm (text/checkbox/radio/dropdown) from a JSON spec, linting the layout first.
- Pull text, tables (JSON/CSV), metadata, or form-field values out of a PDF.
- Merge, split, rotate, extract page subsets, watermark, stamp text/images at coordinates, bookmark, or compress PDFs.
- Export pages as PNGs for visual review or for OCR hand-off; set/clear document metadata; add/extract file attachments.View the complete source on GitHubproduct-price-monitor
When your agent uses it
Watch product, flight, or listing prices; alert on target.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
943 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure — Setup (foreground, once)
- Procedure — Tick (each scheduled run)
- Pitfalls
- Verification
Files
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# Product Price Monitor
Monitor a concrete purchasable item and alert on a normalized all-in price or availability condition. Handle variants, taxes, fees, currencies, stock, cancellation terms, and duplicate alerts explicitly. Setup runs once in the foreground; the recurring check runs as a `cronjob` tick (the `price-watch` automation blueprint scaffolds this).
## When to Use
- "Alert me when this laptop drops below $1,000."
- "Watch these flights for a fare under $500."
- "Tell me when this hotel has a refundable room."
- "Track ticket/listing availability."
- A cron tick fires for an existing price watch (steps 4-6).
Don't use for: one-off "what does this cost right now" lookups (use `web_search`/`web_extract` directly).
View the complete source on GitHubteams-meeting-pipeline
When your agent uses it
Teams meeting summaries, job replay, Graph subscriptions.
What it loads into context
Frontmatter
212 tokensAlways available to the agent
SKILL.md
1,656 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Prerequisites
- Command reference
- Decision tree for common asks
- Critical pitfall: Graph subscriptions expire in 72 hours
- Other pitfalls
- Related docs
Scripts executed separately
../../../hermes
Files
../../../hermesruns262 bytesSKILL.mdcore7,206 bytesSKILL.mdcore7,206 bytes
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# Teams Meeting Pipeline
Use this skill whenever the user asks about Microsoft Teams meeting summaries, transcripts, recordings, action items, Graph subscriptions, or any operational question about the Teams meeting pipeline. Works in any language — the triggers below are examples, not an exhaustive list.
Everything operator-facing is a `hermes teams-pipeline` subcommand run via the terminal tool. There are no new model tools for this pipeline — the CLI is the surface.
## When to use this skill
The user is asking to:
- summarize a Teams meeting / extract action items / pull meeting notes
- check pipeline status, inspect a stored meeting job, or see recent meetings
- replay / re-run a stored job that failed or needs a fresh summary
- validate Microsoft Graph setup after changing env or config
- troubleshoot "meeting summary never arrived" or "no new meetings are ingesting"View the complete source on GitHubweekly-review-planning
When your agent uses it
Weekly reset: commitments, stalled work, next-week plan.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
861 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure
- Output Shape
- Pitfalls
- Verification
Files
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# Weekly Review and Planning
Run a bounded weekly reset across the user's chosen systems. This is a concrete recurring task, not a generic productivity methodology — the `weekly-review` Automation Blueprint schedules it as a cron job.
## When to Use
- "Run my weekly review."
- "What did I commit to and what is slipping?"
- "Plan next week from my calendar, tasks, and notes."
- "Find stale projects and waiting items."
- A cron tick fires for a scheduled weekly review.
Don't use for: daily briefs (see the `google-workspace` daily-brief reference) or single-inbox triage (`email-inbox-triage`).
View the complete source on GitHubarxiv
When your agent uses it
Search arXiv papers by keyword, author, category, or ID.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,940 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Reference
- Searching Papers
- Search Query Syntax
- Sort and Pagination
- Fetching Specific Papers
- BibTeX Generation
- Reading Paper Content
- Common Categories
- Helper Script
- Semantic Scholar (Citations, Related Papers, Author Profiles)
- Complete Research Workflow
- Rate Limits
- Notes
- ID Versioning
- Withdrawn Papers
Scripts executed separately
scripts/search_arxiv.py
Files
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Read SKILL.md
# arXiv Research
Search and retrieve academic papers from arXiv via their free REST API. No API key, no dependencies — just curl.
## Quick Reference
| Action | Command |
|--------|---------|
| Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` |
| Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` |
| Read abstract (web) | `web_extract(urls=["https://arxiv.org/abs/2402.03300"])` |
| Read full paper (PDF) | `web_extract(urls=["https://arxiv.org/pdf/2402.03300"])` |
## Searching PapersView the complete source on GitHubcompetitor-news-monitor
When your agent uses it
Watch named companies for material news; cited digests.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
980 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Procedure — Setup (foreground, once)
- Procedure — Tick (each scheduled run)
- Pitfalls
- Verification
Files
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# Competitor News Monitor
Track a declared company set and report only material, new developments with primary-source evidence. This is not a generic page-diff watcher: it applies company-news categories, source hierarchy, event deduplication, and business significance. Setup runs once in the foreground; the recurring check runs as a `cronjob` tick (the `competitor-watch` automation blueprint scaffolds this).
## When to Use
- "Monitor these competitors weekly."
- "Tell me when Company X changes pricing or launches a product."
- "Create a competitor intelligence digest."
- "Track funding, partnerships, executive moves, and incidents."
- A cron tick fires for an existing competitor watch (steps 3-6).
Don't use for: one-off company research (use `web_search`/`web_extract` directly) or plain feed reading (`blogwatcher`).
View the complete source on GitHubgrounded-citations
When your agent uses it
Ground answers and documents in cited, verifiable sources.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
3,036 tokensWhen the skill is activated
Reference documents
1,306 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- How to Run
- Quick Reference
- Procedure
- Multi-Platform Sweeps
- Fact-Checking Mode
- Pitfalls
- Verification
Scripts executed separately
scripts/_hermes_home.pyscripts/sources.py
Files
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# Grounded Citations
Every claim taken from an outside source gets an inline numbered citation and a
`Sources:` list, Perplexity-style. A ledger script owns the `url → [n]` mapping
so the numbers and URLs come from retrieval, never from memory — the model only
ever emits small integers it was handed.
For high-stakes work the same ledger doubles as a fact-checking chain: verbatim
quotes are attached to each source (rejected unless they literally appear in
the fetched page text), claims from model knowledge are flagged `[unverified]`,
and `verify --evidence` fails any draft whose cited sources carry no evidence.
This skill covers answers in chat, written documents (markdown, PDF, docx,
slides), and research reports. It does not cover academic BibTeX pipelines —View the complete source on GitHubxurl
When your agent uses it
X/Twitter via xurl CLI: raw post search, posting, DM, media.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
4,405 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Secret Safety (MANDATORY)
- Installation
- One-Time User Setup (user runs these outside the agent)
- Quick Reference
- Command Details
- Raw API Access
- Global Flags
- Streaming
- Output Format
- Common Workflows
- Error Handling
- Agent Workflow
- Troubleshooting
- Notes
- Attribution
Files
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# xurl — X (Twitter) API via the Official CLI
`xurl` is the X developer platform's official CLI for the X API. It supports shortcut commands for common actions AND raw curl-style access to any v2 endpoint. All commands return JSON to stdout.
Use this skill for:
- posting, replying, quoting, deleting posts
- searching for raw posts (actual post JSON with IDs you can engage with) and reading timelines/mentions
- liking, reposting, bookmarking
- following, unfollowing, blocking, muting
- direct messages
- media uploads (images and video)
- raw access to any X API v2 endpoint
- multi-app / multi-account workflows
View the complete source on GitHubcodebase-inspection
When your agent uses it
Inspect codebases w/ pygount: LOC, languages, ratios.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
961 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Prerequisites
- 1. Basic Summary (Most Common)
- 2. Common Folder Exclusions
- 3. Filter by Specific Language
- 4. Detailed File-by-File Output
- 5. Output Formats
- 6. Interpreting Results
- Pitfalls
Files
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# Codebase Inspection with pygount
Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using `pygount`.
## When to Use
- User asks for LOC (lines of code) count
- User wants a language breakdown of a repo
- User asks about codebase size or composition
- User wants code-vs-comment ratios
- General "how big is this repo" questions
## Prerequisites
View the complete source on GitHubdogfood
When your agent uses it
Exploratory QA of web apps: find bugs, evidence, reports.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,148 tokensWhen the skill is activated
Reference documents
1,142 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Prerequisites
- Inputs
- Workflow
- Tools Reference
- Tips
Files
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# Dogfood: Systematic Web Application QA Testing
## Overview
This skill guides you through systematic exploratory QA testing of web applications with `browser_exec`. You will navigate the application, interact with elements, capture evidence of issues, and produce a structured bug report.
## Prerequisites
- The `browser` toolset must be enabled (`browser_exec`). `browser_exec` runs Python with pre-imported helpers (`new_tab`, `goto_url`, `wait_for_load`, `page_info`, `js`, `fill_input`, `click_at_xy`, `capture_screenshot`, `cdp`); browser state persists across calls, Python variables do not.
- A target URL and testing scope from the user
## Inputs
The user provides:View the complete source on GitHubgithub
When your agent uses it
GitHub via gh CLI: PRs, issues, reviews, repos, auth.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
607 tokensWhen the skill is activated
Reference documents
21,921 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Routing
- Core discipline (applies to every workflow)
- Verification
Scripts executed separately
scripts/gh-env.shscripts/git-credential-token.py
Files
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# GitHub
Work GitHub end to end with the `gh` CLI (REST fallback where noted): auth,
issues, the PR lifecycle, issue-to-PR delivery, code review, and repo
management. This skill consolidates six former skills; each workflow lives
complete in its reference file — ALWAYS read the matching reference before
starting that workflow, the body below only routes.
## Routing
| Task | Read first |
|---|---|
| Auth broken / new machine / token or SSH setup / gh login | `references/auth.md` |
| Create, triage, label, assign, close issues | `references/issues.md` |View the complete source on GitHubrequesting-code-review
When your agent uses it
Pre-commit review: security scan, quality gates, auto-fix.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
2,217 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Step 1 — Get the diff
- Step 2 — Static security scan
- Step 3 — Baseline tests and linting
- Step 4 — Self-review checklist
- Step 5 — Independent reviewer subagent
- Step 6 — Evaluate results
- Step 7 — Auto-fix loop
- Step 8 — Commit
- Reference: Common Patterns to Flag
- Integration with Other Skills
- Pitfalls
Files
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# Pre-Commit Code Verification
Automated verification pipeline before code lands. Static scans, baseline-aware
quality gates, an independent reviewer subagent, and an auto-fix loop.
**Core principle:** No agent should verify its own work. Fresh context finds what you miss.
## When to Use
- After implementing a feature or bug fix, before `git commit` or `git push`
- When user says "commit", "push", "ship", "done", "verify", or "review before merge"
- After completing a task with 2+ file edits in a git repo
- After each task in subagent-driven-development (the two-stage review)
View the complete source on GitHubsimplify-code
When your agent uses it
Parallel 4-agent cleanup of recent code changes.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
3,379 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- The Process
- Pitfalls
- Related
Files
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# Simplify Code — Parallel Review & Cleanup
Review your recent code changes with four focused reviewers running in
parallel, aggregate their findings, and apply the fixes worth applying.
**This is a cleanup pass, not a bug hunt.** You are improving the quality of
code that already works — removing duplication, flattening needless
complexity, cutting waste, and deepening band-aid fixes. Do not go hunting
for correctness bugs here; that's what `requesting-code-review` is for.
**Core principle:** Four narrow reviewers beat one broad reviewer. Each one
deeply searches the codebase for a single class of problem — reuse, quality,
efficiency, altitude — without diluting its attention across all four. They
run concurrently, so you pay the latency of one review, not four.View the complete source on GitHubspike
When your agent uses it
Throwaway experiments to validate an idea before build.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
2,147 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When NOT to use this
- If the user has the full GSD system installed
- Core method
- Comparison spikes
- Frontier mode (picking what to spike next)
- Output
- Attribution
Files
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# Spike
Use this skill when the user wants to **feel out an idea** before committing to a real build — validating feasibility, comparing approaches, or surfacing unknowns that no amount of research will answer. Spikes are disposable by design. Throw them away once they've paid their debt.
Load this when the user says things like "let me try this", "I want to see if X works", "spike this out", "before I commit to Y", "quick prototype of Z", "is this even possible?", or "compare A vs B".
## When NOT to use this
- The answer is knowable from docs or reading code — just do research, don't build
- The work is production path — use the `plan` skill instead
- The idea is already validated — jump straight to implementation
## If the user has the full GSD system installed
View the complete source on GitHubsystematic-debugging
When your agent uses it
4-phase root cause debugging: understand bugs before fixing.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,261 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- The Iron Law
- The Feedback Loop Rule
- When to Use
- The Four Phases
- Phase 1: Root Cause Investigation
- Phase 2: Pattern Analysis
- Phase 3: Hypothesis and Testing
- Phase 4: Implementation
- Red Flags — STOP and Follow Process
- Common Rationalizations
- Quick Reference
- Hermes Agent Integration
- Real-World Impact
Files
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# Systematic Debugging
## Overview
Random fixes waste time and create new bugs. Quick patches mask underlying issues.
**Core principle:** ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
**Violating the letter of this process is violating the spirit of debugging.**
## The Iron Law
```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRSTView the complete source on GitHubtest-driven-development
When your agent uses it
TDD: enforce RED-GREEN-REFACTOR, tests before code.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
2,453 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- The Iron Law
- Red-Green-Refactor Cycle
- Avoid Horizontal Slices
- Why Order Matters
- Common Rationalizations
- Red Flags — STOP and Start Over
- Verification Checklist
- When Stuck
- Hermes Agent Integration
- Testing Anti-Patterns
- Final Rule
Files
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# Test-Driven Development (TDD)
## Overview
Write the test first. Watch it fail. Write minimal code to pass.
**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.
**Violating the letter of the rules is violating the spirit of the rules.**
## When to Use
**Always:**
- New featuresView the complete source on GitHubblocked-page-recovery
When your agent uses it
Use when a fetch fails: 403/429, paywall, WAF, bot wall.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,324 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The ladder
- Provenance discipline (non-negotiable)
- Manual routes
- Fake successes — routes that LIE
- Proxy relays: don't
Scripts executed separately
scripts/recover_page.py
Files
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# Blocked-Page Recovery
When a page won't fetch — 403/429, Cloudflare "Just a moment...", a paywall,
or a bot-detection interstitial — don't give up and don't loop on the same
URL. Third-party services often hold a **copy** of the page. Work down this
ladder, cheapest first.
## The ladder
```
1. Wayback Machine — archive.org "available" API (snapshot + timestamp)
2. archive.today — domain rotation: archive.ph → .md → .li → .is
3. Jina Reader — only if JINA_API_KEY is set (live server-side render)
4. API-first pivot — look for /api/, /graphql, .json, or RSS on the same hostView the complete source on GitHubSkills included
- agent-merge-conflict-arbiter
- antigravity-cli
- blackbox
- dynamic-workflow
- grok
- honcho
- opencode
- openhands
- evm
- hyperliquid
- solana
- one-three-one-rule
- ai-presenter-video
- anatomy
- archify
- ascii-art
- auteur
- baoyu-article-illustrator
- baoyu-comic
- brag
- brag-slim
- comfyui
- concept-diagrams
- creative-ideation
- design-md
- draw-your-font
- dream-loop
- excalidraw
- heartmula
- hyperframes
- impeccable
- ip-as-logo
- kanban-video-orchestrator
- meme-generation
- mono-color
- pixel-art
- pretext
- simple-english
- sketch
- social-media-content-calendar
- system-atlas
- tldraw-offline
- unreal-mcp
- jupyter-notebook
- actual-setup
- docker-management
- hermes-s6-container-supervision
- inference-sh-cli
- pinggy-tunnel
- setup-wizard-generator
- watchers
- adversarial-ux-test
- agentmail
- 3-statement-model
- comps-analysis
- dcf-model
- excel-author
- lbo-model
- merger-model
- polymarket
- pptx-author
- stocks
- minecraft-modpack-server
- pokemon-player
- fitness-nutrition
- neuroskill-bci
- fastmcp
- mcp-oauth-remote-gateway
- mcporter
- gif-search
- songsee
- accelerate
- chroma
- evaluating-llms-harness
- faiss
- flash-attention
- llama-cpp
- serving-llms-vllm
- lambda-labs
- modal
- segment-anything-model
- nemo-curator
- obliteratus
- peft
- pytorch-fsdp
- pytorch-lightning
- dspy
- saelens
- simpo
- slime
- tensorrt-llm
- torchtitan
- axolotl
- unsloth
- mpp-agent
- stripe-link-cli
- stripe-projects
- airtable
- box
- canvas
- decision-questionnaire
- here-now
- live-dashboard
- memento-flashcards
- notion
- property-listings
- shop
- shopify
- siyuan
- telephony
- bioinformatics
- blogwatcher
- darwinian-evolver
- domain-intel
- drug-discovery
- duckduckgo-search
- gitnexus-explorer
- llm-wiki
- osint-investigation
- parallel-cli
- qmd
- research-paper-writing
- rss-feeds
- scrapling
- searxng-search
- 1password
- godmode
- oss-forensics
- sherlock
- unbroker
- web-pentest
- openhue
- reddit-reading
- ast-grep
- code-wiki
- grill-me
- hermes-agent-skill-authoring
- inspecting-hermes-desktop-dom
- node-inspect-debugger
- pr-lens
- python-debugpy
- rest-graphql-debug
- subagent-driven-development
- cloudflare-temporary-deploy
- har-derived-api-client
- page-agent
- publish-site
- scrollcraft
- yuanbao
- google_meet
- apple-notes
- apple-reminders
- findmy
- imessage
- claude-code
- codex
- computer-use
- hermes-agent
- architecture-diagram
- ascii-video
- baoyu-infographic
- claude-design
- humanizer
- manim-video
- p5js
- popular-web-designs
- songwriting-and-ai-music
- sdlc-review
- email-inbox-triage
- himalaya
- youtube-content
- obsidian
- document-to-action-items
- google-workspace
- maps
- meeting-action-items
- microsoft-office
- product-price-monitor
- teams-meeting-pipeline
- weekly-review-planning
- arxiv
- competitor-news-monitor
- grounded-citations
- xurl
- codebase-inspection
- dogfood
- github
- requesting-code-review
- simplify-code
- spike
- systematic-debugging
- test-driven-development
- blocked-page-recovery
Repository facts
- License
- MIT
- Created
- Jul 22, 2025
- Last pushed
- Nov 6, 2026
- Forks
- 57,614
- Open issues
- 18,701
- Project activity
- Active
Rank over the last 12 weeks
Now #4 · best #3 · same as 30 days ago
- #3 on Aug 2
- #3 from Aug 7 to Aug 24
- #4 from Aug 25 to Oct 25
- #4 since Oct 28
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 2, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
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optional-skills/autonomous-ai-agents/opencode/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/autonomous-ai-agents/openhands/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/autonomous-ai-agents/openhands/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedoptional-skills/blockchain/evm/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- 271 dangerous shell commandsin 91 files: research-paper-writing/templates/aaai2026/aaai2026-unified-template.tex:121 unsloth/references/llms-full.md:1029 unsloth/references/llms-txt.md:188 and 88 more
- 7,739 network referencesin 554 files: unsloth/references/llms-txt.md:35 unsloth/references/llms-full.md:5 axolotl/references/api.md:9 and 551 more
- 14 obfuscation signalsin 9 files: microsoft-office/tests/test_powerpoint_skill.py:40 unsloth/references/llms-full.md:556 unsloth/references/llms-txt.md:400 and 6 more
- 21 instruction override signalsin 5 files: antigravity-cli/SKILL.md:88 claude-code/SKILL.md:119 antigravity-cli/references/cli-docs.md:48 and 2 more
- 340 executable scriptsin 279 files: scripts/run_tests.sh:1 activate:1 activate.fish:1 and 276 more
- 1,042 references to secretsin 181 files: github/references/repo-management.md:16 telephony/scripts/telephony.py:5 unsloth/references/llms-full.md:1139 and 178 more
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optional-skills/autonomous-ai-agents/antigravity-cli/SKILL.md
- Instruction override signalline 88
terminal(command="agy -p 'Implement the change described in TASK.md and run the tests' --dangerously-skip-permissions", workdir="/path/to/repo", background=true, notify_on_complete=true) - Instruction override signalline 149
- `--continue` / `-c` - `--conversation` - `--dangerously-skip-permissions` - Instruction override signalline 150
- `--conversation` - `--dangerously-skip-permissions` - `--print` / `-p` - Instruction override signalline 151
- `--dangerously-skip-permissions` - Instruction override signalline 188
### Sandbox behavior - `enableTerminalSandbox` is a boolean in `settings.json`; default `false`. - Launch-time overrides (`--sandbox`, `--dangerously-skip-permissions`) can - Instruction override signalline 189
- `enableTerminalSandbox` is a boolean in `settings.json`; default `false`. - Launch-time overrides (`--sandbox`, `--dangerously-skip-permissions`) can supersede persistent settings for the current session. - Instruction override signalline 190
- Launch-time overrides (`--sandbox`, `--dangerously-skip-permissions`) can
optional-skills/autonomous-ai-agents/antigravity-cli/references/cli-docs.md
- Network accessline 8
## Install - macOS/Linux: `curl -fsSL https://antigravity.google/cli/install.sh | bash` - Dangerous shell operationline 9
- macOS/Linux: `curl -fsSL https://antigravity.google/cli/install.sh | bash` - Network accessline 9
- macOS/Linux: `curl -fsSL https://antigravity.google/cli/install.sh | bash` - Network accessline 10
- Windows PowerShell: `irm https://antigravity.google/cli/install.ps1 | iex` - Network accessline 11
- Windows CMD: `curl -fsSL https://antigravity.google/cli/install.cmd -o install.cmd && install.cmd && del install.cmd` - Instruction override signalline 48
## Permissions and sandbox - Permission modes: `request-review`, `always-proceed`, `strict`, `proceed-in-sandbox` - Launch overrides: `--sandbox`, `--dangerously-skip-permissions` - Instruction override signalline 49
- Permission modes: `request-review`, `always-proceed`, `strict`, `proceed-in-sandbox` - Launch overrides: `--sandbox`, `--dangerously-skip-permissions` - Sandbox setting: `enableTerminalSandbox` in `settings.json` (default `false`) - Instruction override signalline 50
- Launch overrides: `--sandbox`, `--dangerously-skip-permissions`
optional-skills/autonomous-ai-agents/blackbox/SKILL.md
- Network accessline 16
Delegate coding tasks to [Blackbox AI](https://www.blackbox.ai/) via the Hermes terminal. Blackbox is a multi-model coding agent CLI that dispatches tasks to multiple LLMs (Claude, Codex, Gemini, Blackbox Pro) and uses a judge to select the - Network accessline 22
- Node.js 20+ installed - Blackbox CLI installed: `npm install -g @blackbox_ai/blackbox-cli` (binary: `blackbox`) - API key from [app.blackbox.ai/dashboard](https://app.blackbox.ai/dashboard) - Network accessline 23
- Blackbox CLI installed: `npm install -g @blackbox_ai/blackbox-cli` (binary: `blackbox`) - API key from [app.blackbox.ai/dashboard](https://app.blackbox.ai/dashboard) - Configured: run `blackbox configure` and enter your API key - Network accessline 24
- API key from [app.blackbox.ai/dashboard](https://app.blackbox.ai/dashboard) - Network accessline 85
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && blackbox --prompt 'Review this PR against main. Check for bugs, security issues, and code quality.'", pty=true
optional-skills/autonomous-ai-agents/grok/SKILL.md
- Network accessline 16
Delegate coding tasks to [Grok Build](https://docs.x.ai/build/overview) (xAI's - Network accessline 36
- **Install (preferred):** `npm install -g @xai-official/grok` - The official installer `curl -fsSL https://x.ai/cli/install.sh | bash` also - Dangerous shell operationline 37
- The official installer `curl -fsSL https://x.ai/cli/install.sh | bash` also - Network accessline 37
- The official installer `curl -fsSL https://x.ai/cli/install.sh | bash` also - Access to secretsline 54
[Credential-related source omitted] - Network accessline 205
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && grok --no-auto-update -p 'Review the changes vs origin/main. Check bugs, security, race conditions, missing te - Access to secretsline 273
[Credential-related source omitted] - Network accessline 278
3. **Always pass `--no-auto-update` in automation** — otherwise Grok phones home for update checks (and `x.ai`/`storage.googleapis.com` may be unreachable). 4. **Prefer npm install over the curl installer** — `npm install -g - Network accessline 279
for update checks (and `x.ai`/`storage.googleapis.com` may be unreachable). 4. **Prefer npm install over the curl installer** — `npm install -g @xai-official/grok` avoids the Cloudflare-walled `x.ai` host. - Network accessline 280
4. **Prefer npm install over the curl installer** — `npm install -g
optional-skills/autonomous-ai-agents/honcho/SKILL.md
- Network accessline 9
hermes: tags: [Honcho, Memory, Profiles, Observation, Dialectic, User-Modeling, Session-Summary] homepage: https://docs.honcho.dev - Network accessline 10
tags: [Honcho, Memory, Profiles, Observation, Dialectic, User-Modeling, Session-Summary] homepage: https://docs.honcho.dev related_skills: [hermes-agent] - Network accessline 11
homepage: https://docs.honcho.dev - Network accessline 41
hermes memory setup honcho # select "cloud", paste API key from https://app.honcho.dev - Network accessline 42
# select "cloud", paste API key from https://app.honcho.dev - Network accessline 48
hermes memory setup honcho # select "local", enter base URL (e.g. http://localhost:8000) - Network accessline 49
# select "local", enter base URL (e.g. http://localhost:8000) - Network accessline 52
See: https://docs.honcho.dev/v3/guides/integrations/hermes#running-honcho-locally-with-hermes - Network accessline 119
Settings changed in the [Honcho dashboard](https://app.honcho.dev) are synced back on session init -- server-side config wins over local defaults. - Network accessline 350
| Key | Default | Description | |-----|---------|-------------| | `apiKey` | -- | API key ([get one](https://app.honcho.dev)) | - Network accessline 351
|-----|---------|-------------| | `apiKey` | -- | API key ([get one](https://app.honcho.dev)) | | `baseUrl` | -- | Base URL for self-hosted Honcho | - Network accessline 352
| `apiKey` | -- | API key ([get one](https://app.honcho.dev)) |
optional-skills/autonomous-ai-agents/opencode/SKILL.md
- Network accessline 16
Use [OpenCode](https://opencode.ai) as an autonomous coding worker orchestrated by Hermes terminal/process tools. OpenCode is a provider-agnostic, open-source AI coding agent with a TUI and CLI. - Access to secretsline 28
[Credential-related source omitted] - Network accessline 161
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && opencode run 'Review this PR vs main. Report bugs, security risks, test gaps, and style issues.' -f $(git diff origin/main --name-o
optional-skills/autonomous-ai-agents/openhands/SKILL.md
- Network accessline 16
Delegate coding tasks to the [OpenHands CLI](https://github.com/All-Hands-AI/OpenHands) via the `terminal` tool. OpenHands is model-agnostic: any LiteLLM-supported provider (OpenAI, Anthropic, OpenRouter, DeepSeek, Ollama, vLLM, etc.). - Network accessline 41
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Network accessline 43
export LLM_BASE_URL=https://openrouter.ai/api/v1 # omit for native OpenAI - Network accessline 61
[Credential-related source omitted] - Network accessline 62
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 124
[Credential-related source omitted] - Network accessline 136
[Credential-related source omitted] - Network accessline 137
[Credential-related source omitted] - Access to secretsline 137
[Credential-related source omitted] - Network accessline 147
- [OpenHands GitHub](https://github.com/All-Hands-AI/OpenHands) - Network accessline 148
- [OpenHands CLI command reference](https://docs.openhands.dev/openhands/usage/cli/command-reference)
optional-skills/blockchain/evm/SKILL.md
- Network accessline 57
Override RPC endpoint: `export EVM_RPC_URL=https://your-rpc.com`
optional-skills/blockchain/evm/scripts/evm_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 21
"ethereum": { "chain_id": 1, "rpc": "https://ethereum-rpc.publicnode.com", - Network accessline 22
"chain_id": 1, "rpc": "https://ethereum-rpc.publicnode.com", "native": "ETH", - Network accessline 23
"rpc": "https://ethereum-rpc.publicnode.com", - Network accessline 24
"native": "ETH", "coingecko": "ethereum", "explorer": "https://etherscan.io", - Network accessline 25
"coingecko": "ethereum", "explorer": "https://etherscan.io", "decimals": 18, - Network accessline 26
"explorer": "https://etherscan.io", - Network accessline 29
"bsc": { "chain_id": 56, "rpc": "https://bsc-dataseed1.binance.org", - Network accessline 30
"chain_id": 56, "rpc": "https://bsc-dataseed1.binance.org", "native": "BNB", - Network accessline 31
"rpc": "https://bsc-dataseed1.binance.org", - Network accessline 32
"native": "BNB", "coingecko": "binancecoin", "explorer": "https://bscscan.com", - Network accessline 33
"coingecko": "binancecoin", "explorer": "https://bscscan.com", "decimals": 18, - Network accessline 34
"explorer": "https://bscscan.com", - Network accessline 37
"base": { "chain_id": 8453, "rpc": "https://mainnet.base.org", - Network accessline 38
"chain_id": 8453, "rpc": "https://mainnet.base.org", "native": "ETH", - Network accessline 39
"rpc": "https://mainnet.base.org", - Network accessline 40
"native": "ETH", "coingecko": "ethereum", "explorer": "https://basescan.org", - Network accessline 41
"coingecko": "ethereum", "explorer": "https://basescan.org", "decimals": 18, - Network accessline 42
"explorer": "https://basescan.org", - Network accessline 45
"arbitrum": { "chain_id": 42161, "rpc": "https://arb1.arbitrum.io/rpc", - Network accessline 46
"chain_id": 42161, "rpc": "https://arb1.arbitrum.io/rpc", "native": "ETH", - Network accessline 47
"rpc": "https://arb1.arbitrum.io/rpc", - Network accessline 48
"native": "ETH", "coingecko": "ethereum", "explorer": "https://arbiscan.io", - Network accessline 49
"coingecko": "ethereum", "explorer": "https://arbiscan.io", "decimals": 18, - Network accessline 50
"explorer": "https://arbiscan.io", - Network accessline 53
"polygon": { "chain_id": 137, "rpc": "https://polygon-rpc.com", - Network accessline 54
"chain_id": 137, "rpc": "https://polygon-rpc.com", "native": "MATIC", - Network accessline 55
"rpc": "https://polygon-rpc.com", - Network accessline 56
"native": "MATIC", "coingecko": "matic-network", "explorer": "https://polygonscan.com", - Network accessline 57
"coingecko": "matic-network", "explorer": "https://polygonscan.com", "decimals": 18, - Network accessline 58
"explorer": "https://polygonscan.com", - Network accessline 61
"optimism": { "chain_id": 10, "rpc": "https://mainnet.optimism.io", - Network accessline 62
"chain_id": 10, "rpc": "https://mainnet.optimism.io", "native": "ETH", - Network accessline 63
"rpc": "https://mainnet.optimism.io", - Network accessline 64
"native": "ETH", "coingecko": "ethereum", "explorer": "https://optimistic.etherscan.io", - Network accessline 65
"coingecko": "ethereum", "explorer": "https://optimistic.etherscan.io", "decimals": 18, - Network accessline 66
"explorer": "https://optimistic.etherscan.io", - Network accessline 69
"avalanche": { "chain_id": 43114, "rpc": "https://api.avax.network/ext/bc/C/rpc", - Network accessline 70
"chain_id": 43114, "rpc": "https://api.avax.network/ext/bc/C/rpc", "native": "AVAX", - Network accessline 71
"rpc": "https://api.avax.network/ext/bc/C/rpc", - Network accessline 72
"native": "AVAX", "coingecko": "avalanche-2", "explorer": "https://snowtrace.io", - Network accessline 73
"coingecko": "avalanche-2", "explorer": "https://snowtrace.io", "decimals": 18, - Network accessline 74
"explorer": "https://snowtrace.io", - Network accessline 77
"zksync": { "chain_id": 324, "rpc": "https://mainnet.era.zksync.io", - Network accessline 78
"chain_id": 324, "rpc": "https://mainnet.era.zksync.io", "native": "ETH", - Network accessline 79
"rpc": "https://mainnet.era.zksync.io", - Network accessline 80
"native": "ETH", "coingecko": "ethereum", "explorer": "https://explorer.zksync.io", - Network accessline 81
"coingecko": "ethereum", "explorer": "https://explorer.zksync.io", "decimals": 18, - Network accessline 82
"explorer": "https://explorer.zksync.io", - Network accessline 328
"User-Agent": "Mozilla/5.0 (compatible; evm_client/1.0)", } req = urllib.request.Request(url, data=body, headers=headers, method="POST") - Network accessline 329
} req = urllib.request.Request(url, data=body, headers=headers, method="POST") delay = 1.0 - Network accessline 330
req = urllib.request.Request(url, data=body, headers=headers, method="POST") - Network accessline 333
for attempt in range(retries): try: with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 334
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return json.loads(resp.read().decode()) - Network accessline 335
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 356
def _http_get(url: str, retries: int = 5, timeout: int = 20) -> Any: headers = {"Accept": "application/json", "User-Agent": "evm_client/1.0"} req = urllib.request.Request(url, headers=headers, method="GET") - Network accessline 357
headers = {"Accept": "application/json", "User-Agent": "evm_client/1.0"} req = urllib.request.Request(url, headers=headers, method="GET") delay = 1.0 - Network accessline 358
req = urllib.request.Request(url, headers=headers, method="GET") - Network accessline 361
for attempt in range(retries): try: with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 362
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return json.loads(resp.read().decode()) - Network accessline 363
with urllib.request.urlopen(req, timeout=timeout) as resp: - Access to secretsline 389
[Credential-related source omitted] - Network accessline 545
COINGECKO_BASE = "https://api.coingecko.com/api/v3" - Network accessline 994
def fetch(chain: str) -> None: - Network accessline 1239
url = f"https://www.4byte.directory/api/v1/signatures/?hex_signature={selector}" - Network accessline 1279
try: data = _http_get(f"https://api.ensideas.com/ens/resolve/{query}") - Network accessline 1280
data = _http_get(f"https://api.ensideas.com/ens/resolve/{query}")
optional-skills/blockchain/hyperliquid/SKILL.md
optional-skills/blockchain/hyperliquid/scripts/hyperliquid_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
Environment: HYPERLIQUID_API_URL Override API base URL (default: https://api.hyperliquid.xyz) - Network accessline 23
HYPERLIQUID_API_URL Override API base URL (default: https://api.hyperliquid.xyz) HYPERLIQUID_USER_ADDRESS Default address for state/fills/orders/review commands - Network accessline 24
(default: https://api.hyperliquid.xyz) - Network accessline 43
USER_AGENT = "HermesAgent/1.0" DEFAULT_USER_ENV = "HYPERLIQUID_USER_ADDRESS" DEFAULT_API_BASE = "https://api.hyperliquid.xyz" - Network accessline 45
DEFAULT_API_BASE = "https://api.hyperliquid.xyz" - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 118
[Credential-related source omitted] - Network accessline 130
for attempt in range(retries + 1): request = urllib.request.Request(_info_url(), data=data, headers=headers, method="POST") - Network accessline 131
request = urllib.request.Request(_info_url(), data=data, headers=headers, method="POST") - Network accessline 132
try: with urllib.request.urlopen(request, timeout=timeout) as response: body = json.load(response) - Network accessline 133
with urllib.request.urlopen(request, timeout=timeout) as response:
optional-skills/blockchain/solana/SKILL.md
- Network accessline 49
RPC endpoint (default): https://api.mainnet-beta.solana.com - Network accessline 50
Override: export SOLANA_RPC_URL=https://your-private-rpc.com - Network accessline 74
# Optional: set a private RPC for better rate limits export SOLANA_RPC_URL="https://api.mainnet-beta.solana.com" - Network accessline 75
export SOLANA_RPC_URL="https://api.mainnet-beta.solana.com"
optional-skills/blockchain/solana/scripts/solana_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 31
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Network accessline 32
"SOLANA_RPC_URL", "https://api.mainnet-beta.solana.com", ) - Network accessline 33
"https://api.mainnet-beta.solana.com", - Network accessline 76
for attempt in range(retries + 1): req = urllib.request.Request( - Network accessline 77
req = urllib.request.Request( - Network accessline 79
) try: with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 80
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return json.load(resp) - Network accessline 81
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 100
for attempt in range(retries + 1): req = urllib.request.Request( - Network accessline 101
req = urllib.request.Request( - Network accessline 104
) try: with urllib.request.urlopen(req, timeout=20) as resp: - Network accessline 105
try: with urllib.request.urlopen(req, timeout=20) as resp: body = json.load(resp) - Network accessline 106
with urllib.request.urlopen(req, timeout=20) as resp: - Network accessline 138
for attempt in range(3): req = urllib.request.Request( - Network accessline 139
req = urllib.request.Request( - Network accessline 142
) try: with urllib.request.urlopen(req, timeout=20) as resp: - Network accessline 143
try: with urllib.request.urlopen(req, timeout=20) as resp: return json.load(resp) - Network accessline 144
with urllib.request.urlopen(req, timeout=20) as resp: - Network accessline 183
for i, mint in enumerate(mints[:max_lookups]): url = ( f"https://api.coingecko.com/api/v3/simple/token_price/solana" - Network accessline 184
url = ( f"https://api.coingecko.com/api/v3/simple/token_price/solana" f"?contract_addresses={mint}&vs_currencies=usd" - Network accessline 185
f"https://api.coingecko.com/api/v3/simple/token_price/solana" - Network accessline 202
data = _http_get_json( "https://api.coingecko.com/api/v3/simple/price?ids=solana&vs_currencies=usd" - Network accessline 203
"https://api.coingecko.com/api/v3/simple/price?ids=solana&vs_currencies=usd" - Network accessline 216
sym, name = KNOWN_TOKENS[mint] return {"symbol": sym, "name": name} url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}" - Network accessline 217
return {"symbol": sym, "name": name} url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}" data = _http_get_json(url, timeout=10) - Network accessline 218
url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}"
optional-skills/creative/ai-presenter-video/SKILL.md
- Network accessline 3
description: "Make a verified AI presenter video from script + image." version: 1.0.0 author: cclank (https://github.com/cclank/lanshu-create-ai-presenter-video), ported by Hermes Agent - Network accessline 4
version: 1.0.0 author: cclank (https://github.com/cclank/lanshu-create-ai-presenter-video), ported by Hermes Agent license: MIT - Network accessline 5
author: cclank (https://github.com/cclank/lanshu-create-ai-presenter-video), ported by Hermes Agent - Network accessline 11
tags: [video, presenter, avatar, lipsync, tts, captions, creative] category: creative homepage: https://github.com/cclank/lanshu-create-ai-presenter-video - Network accessline 12
category: creative homepage: https://github.com/cclank/lanshu-create-ai-presenter-video related_skills: [hyperframes, kanban-video-orchestrator, comfyui] - Network accessline 13
homepage: https://github.com/cclank/lanshu-create-ai-presenter-video
optional-skills/creative/ai-presenter-video/scripts/finalize_delivery.sh
optional-skills/creative/ai-presenter-video/scripts/init_job.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/creative/ai-presenter-video/scripts/preflight.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/creative/anatomy/SKILL.md
- Network accessline 11
category: creative related_skills: [concept-diagrams, system-atlas, architecture-diagram] upstream: https://github.com/wheresryan22/anatomy (pinned dbf697a, v1.0.0) - Network accessline 12
related_skills: [concept-diagrams, system-atlas, architecture-diagram] upstream: https://github.com/wheresryan22/anatomy (pinned dbf697a, v1.0.0) --- - Network accessline 13
upstream: https://github.com/wheresryan22/anatomy (pinned dbf697a, v1.0.0)
optional-skills/creative/anatomy/examples/arcade-cabinet/build.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/arcade-cabinet/live.js
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/arcade-cabinet/parts.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/arcade-cabinet/screen.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/desk-computer/build.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/dial-indicator/build.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/raptor-engine/build.mjs
- Executable scriptline 1
Executable file - Network accessline 889
const backSvg = k.figureSvg({ width: W, height: H, label: LABEL_TEXT, body: back, className: "rp-back" }); const frontSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg rp-front" viewBox="0 0 ${W} ${H}" aria-hidden="true"><g clas - Network accessline 890
const frontSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg rp-front" viewBox="0 0 ${W} ${H}" aria-hidden="true"><g class="rp-engine">${groups.join("")}</g></svg>`; - Network accessline 923
const corners = `<div class="iso-plate-corners" aria-hidden="true"><span class="iso-plate-corner" data-corner="fig">Fig 1</span><span class="iso-plate-corner" data-corner="title">Raptor 2 · test stand</span><span class="iso-plate-corner" da - Network accessline 924
const controls = `<div class="rp-controls"><button type="button" class="rp-fire" data-fire><span class="rp-lamp" aria-hidden="true"></span><span data-fire-label>Ignite</span></button><span class="rp-hint">Hover the engine to take it apart</ - Network accessline 925
const glowSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg rp-glow" viewBox="0 0 ${W} ${H}" aria-hidden="true"><g class="rp-glow-g"><path class="rp-rim rp-rim-soft" d="${LIP}"/><path class="rp-rim" d="${LIP}"/></g></svg>`;
optional-skills/creative/anatomy/examples/raptor-engine/live.js
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/ripple-tank/build.mjs
- Executable scriptline 1
Executable file - Network accessline 529
const backSvg = k.figureSvg({ width: W, height: H, label: LABEL_TEXT, body: back, className: "rt-back" }); const layerSvg = (name, body) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg rt-layer ${name}" viewBox="0 0 ${W} ${H}" ar - Network accessline 530
const layerSvg = (name, body) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg rt-layer ${name}" viewBox="0 0 ${W} ${H}" aria-hidden="true">${body.join("")}</svg>`;
optional-skills/creative/anatomy/examples/ripple-tank/live.js
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/ripple-tank/model.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/art.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/drops.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/fan.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/figure.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/geometry.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/live.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/spot-plate/rig.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/art.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/figure.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/geometry.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/live.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/pose.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/rig.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/test-rig/view.ts
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/turning-dial/build.mjs
- Executable scriptline 1
Executable file - Network accessline 449
const LABEL = "A combination dial on a raised door plate. A small robot hand lies on the dial's hub and turns with it: a palm with a cover plate, a connector block, a piston on two saddles with a ball joint, three jointed fingers whose tips - Network accessline 450
const backSvg = k.figureSvg({ width: WIDTH, height: HEIGHT, label: LABEL, body: BACK }); const staticSvg = (list) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-layer" viewBox="0 0 ${WIDTH} ${HEIGHT}" aria-hidden="true">${li - Network accessline 451
const staticSvg = (list) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-layer" viewBox="0 0 ${WIDTH} ${HEIGHT}" aria-hidden="true">${list.join("")}</svg>`;
optional-skills/creative/anatomy/examples/turning-dial/live.js
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/examples/turning-dial/stress.mjs
- Executable scriptline 1
Executable file - Network accessline 191
for (let f = 0; f < 5; f++) { const tag = f < 4 ? `f${f}` : "thumb"; const curl = (Math.sin(t * 1.3 + f * 0.9) * 0.5 + 0.5) * lift; - Network accessline 192
const tag = f < 4 ? `f${f}` : "thumb"; const curl = (Math.sin(t * 1.3 + f * 0.9) * 0.5 + 0.5) * lift; out[`${tag}.j0`] = -5 + 10 * curl; - Network accessline 193
const curl = (Math.sin(t * 1.3 + f * 0.9) * 0.5 + 0.5) * lift; - Network accessline 223
const BACK = [`<path class="iso-halo" d="${k.haloOf(BENCH, BENCH_TOP - 6, 6, P)}"/>`, ...layerOf("back")]; const backSvg = k.figureSvg({ width: WIDTH, height: HEIGHT, label: "A dense robot hand on a turntable in a workshop: a stress scene f - Network accessline 224
const backSvg = k.figureSvg({ width: WIDTH, height: HEIGHT, label: "A dense robot hand on a turntable in a workshop: a stress scene for the 3D mode.", body: BACK }); const staticSvg = (list) => `<svg xmlns="http://www.w3.org/2000/svg" class - Network accessline 225
const staticSvg = (list) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-layer" viewBox="0 0 ${WIDTH} ${HEIGHT}" aria-hidden="true">${list.join("")}</svg>`;
optional-skills/creative/anatomy/kit/audit.mjs
- Executable scriptline 1
Executable file - Dangerous shell operationline 221
const rm = Math.max(...poly.map(([, r]) => r)); - Dangerous shell operationline 224
const spread = [0, 1, 2].map((i) => rm * Math.sqrt(Math.max(0, 1 - F.a[i] * F.a[i]))); - Dangerous shell operationline 331
const { aV, uV, vV, s0, s1, rm } = shape._f; - Dangerous shell operationline 347
const qc = xc * xc + yc * yc - rm * rm; - Dangerous shell operationline 353
} else if (xc * xc + yc * yc > rm * rm) return null;
optional-skills/creative/anatomy/kit/canvas-painter.mjs
- Network accessline 1
const CP_Q = 64; const CP_SVG = "http://www.w3.org/2000/svg"; - Executable scriptline 1
Executable file - Network accessline 2
const CP_SVG = "http://www.w3.org/2000/svg"; - Network accessline 689
const theme = holder ? holder.getAttribute("data-theme") : "dark"; const card = getComputedStyle(stage.closest(".iso-plate") || stage).getPropertyValue("--anatomy-card").trim() || "#fff"; return `<svg xmlns="http://www.w3.org/20 - Network accessline 690
const card = getComputedStyle(stage.closest(".iso-plate") || stage).getPropertyValue("--anatomy-card").trim() || "#fff"; return `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ${W} ${H}" width="${W}" height="${H}"><style>${css.r - Network accessline 691
return `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ${W} ${H}" width="${W}" height="${H}"><style>${css.replace(/<\/style/gi, "")}</style><g class="iso" data-theme="${theme}"><g class="iso-plate" style="padding:0;box-shadow:none">${
optional-skills/creative/anatomy/kit/gl-shared.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/gl.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/iso-kit.mjs
- Executable scriptline 1
Executable file - Network accessline 532
const style = standalone ? `<style>${ISO_CSS}</style>` : ""; return `<svg${attrs({ xmlns: "http://www.w3.org/2000/svg", - Network accessline 533
return `<svg${attrs({ xmlns: "http://www.w3.org/2000/svg", class: `iso-svg${standalone ? " iso" : ""}${className ? ` ${className}` : ""}`, - Network accessline 534
xmlns: "http://www.w3.org/2000/svg",
optional-skills/creative/anatomy/kit/iso-kit.ts
- Executable scriptline 1
Executable file - Network accessline 669
const style = standalone ? `<style>${ISO_CSS}</style>` : ""; return `<svg${attrs({ xmlns: "http://www.w3.org/2000/svg", - Network accessline 670
return `<svg${attrs({ xmlns: "http://www.w3.org/2000/svg", class: `iso-svg${standalone ? " iso" : ""}${className ? ` ${className}` : ""}`, - Network accessline 671
xmlns: "http://www.w3.org/2000/svg",
optional-skills/creative/anatomy/kit/lathe.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/react/draw.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/react/live.tsx
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/tube.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/kit/turn-audit.mjs
optional-skills/creative/anatomy/kit/turn-build.mjs
- Executable scriptline 1
Executable file - Network accessline 1260
}; const out = { svg: markup, data: packed, css: TURN_CSS, report, scene, frames, planesByName }; out.layerSvg = (name, { width, height, label = "" }) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-live iso-layer" data-l - Network accessline 1261
const out = { svg: markup, data: packed, css: TURN_CSS, report, scene, frames, planesByName }; out.layerSvg = (name, { width, height, label = "" }) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-live iso-layer" data-live=" - Network accessline 1262
out.layerSvg = (name, { width, height, label = "" }) => `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg iso-live iso-layer" data-live="${esc(name)}" viewBox="0 0 ${width} ${height}" aria-hidden="true"${label ? ` aria-label="${esc(la
optional-skills/creative/anatomy/kit/turn-fixed.mjs
optional-skills/creative/anatomy/kit/turn.mjs
optional-skills/creative/anatomy/references/verify.md
- Network accessline 14
node scripts/capture.mjs page.html --out shots/joint.png --zoom 200,80,220,170 # close-up at 4x, in card px node scripts/capture.mjs page.html --out shots/still.png --reduced # reduced motion node scripts/capture.mjs http://lo - Network accessline 16
node scripts/capture.mjs http://localhost:3000/docs --select "figure:nth-of-type(3) .iso-plate" # one figure on a dev-server page
optional-skills/creative/anatomy/references/webgl.md
- Network accessline 166
const DATA = { W, H, camera: glCamera(P), plateR: 40, deck: [DECK.w / 2, DECK.r] }; const backSvg = k.figureSvg({ width: W, height: H, label: LABEL, body: back }); const frontSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg fx- - Network accessline 167
const backSvg = k.figureSvg({ width: W, height: H, label: LABEL, body: back }); const frontSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg fx-layer" viewBox="0 0 ${W} ${H}" aria-hidden="true">${front.join("")}</svg>`; const st - Network accessline 168
const frontSvg = `<svg xmlns="http://www.w3.org/2000/svg" class="iso-svg fx-layer" viewBox="0 0 ${W} ${H}" aria-hidden="true">${front.join("")}</svg>`;
optional-skills/creative/anatomy/scripts/capture.mjs
- Executable scriptline 1
Executable file - Network accessline 144
}; try { const target = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); - Network accessline 145
try { const target = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); const socket = new WebSocket(target.webSocketDebuggerUrl); - Network accessline 146
const target = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json();
optional-skills/creative/anatomy/scripts/drive.mjs
- Executable scriptline 1
Executable file - Network accessline 144
}; try { const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); - Network accessline 145
try { const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); const socket = new WebSocket(page.webSocketDebuggerUrl); - Network accessline 146
const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json();
optional-skills/creative/anatomy/scripts/inline-kit.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/scripts/line-ends.mjs
- Executable scriptline 1
Executable file - Network accessline 254
problems.sort((a, b) => b.gap - a.gap); if (show) { const ns = "http://www.w3.org/2000/svg"; - Network accessline 255
if (show) { const ns = "http://www.w3.org/2000/svg"; const overlay = document.createElementNS(ns, "svg"); - Network accessline 256
const ns = "http://www.w3.org/2000/svg";
optional-skills/creative/anatomy/scripts/lines.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/scripts/turn-bench.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/anatomy/scripts/turn-check.mjs
- Executable scriptline 1
Executable file - Network accessline 62
}); }); const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); - Network accessline 63
}); const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); const socket = new WebSocket(page.webSocketDebuggerUrl); - Network accessline 64
const page = await (await fetch(`http://127.0.0.1:${port}/json/new?about:blank`, { method: "PUT" })).json(); - Network accessline 297
} } return '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" shape-rendering="crispEdges"><rect width="100%" height="100%" fill="rgb(0,0,0)"/>' + out.join("") + "</svg>"; - Network accessline 298
} return '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" shape-rendering="crispEdges"><rect width="100%" height="100%" fill="rgb(0,0,0)"/>' + out.join("") + "</svg>"; }; - Network accessline 299
return '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" shape-rendering="crispEdges"><rect width="100%" height="100%" fill="rgb(0,0,0)"/>' + out.join("") + "</svg>"; - Network accessline 369
const h = H * SCALE; let hidden = null; const svgString = () => '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" class="iso iso-svg" data-theme="' + theme + '" style="--anat - Network accessline 370
let hidden = null; const svgString = () => '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" class="iso iso-svg" data-theme="' + theme + '" style="--anatomy-paper:var(--anatomy - Network accessline 371
const svgString = () => '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 ' + W + " " + H + '" width="' + w + '" height="' + h + '" class="iso iso-svg" data-theme="' + theme + '" style="--anatomy-paper:var(--anatomy-card)"><style>' + cs - Network accessline 610
if (argv.includes("--lines")) { const angles = Array.from({ length: 24 }, (_, i) => i * 15); const planted = await page.evaluate(`(() => { window.__isoTurn.set(0); const part = document.querySelector("svg[data-live] [data-p]"); - Network accessline 611
const angles = Array.from({ length: 24 }, (_, i) => i * 15); const planted = await page.evaluate(`(() => { window.__isoTurn.set(0); const part = document.querySelector("svg[data-live] [data-p]"); const el = document.createElementNS("h - Network accessline 612
const planted = await page.evaluate(`(() => { window.__isoTurn.set(0); const part = document.querySelector("svg[data-live] [data-p]"); const el = document.createElementNS("http://www.w3.org/2000/svg", "path"); el.setAttribute("class", "iso-
optional-skills/creative/anatomy/scripts/turn-fidelity.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/archify/SKILL.md
- Network accessline 20
> **Catalog stub.** This entry is maintained upstream at > [tt-a1i/archify](https://github.com/tt-a1i/archify): the project ships a - Network accessline 21
> [tt-a1i/archify](https://github.com/tt-a1i/archify): the project ships a - Network accessline 52
Full documentation: https://github.com/tt-a1i/archify#readme
optional-skills/creative/ascii-art/SKILL.md
- Network accessline 64
# Basic text banner (default font) curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World" - Network accessline 65
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World" - Network accessline 67
# With a specific font curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant" - Network accessline 68
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant" - Network accessline 69
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Doom" - Network accessline 70
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Star+Wars" - Network accessline 71
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=3-D" - Network accessline 72
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Banner3" - Network accessline 74
# List all available fonts (returns JSON array) curl -s "https://asciified.thelicato.io/api/v2/fonts" - Network accessline 75
curl -s "https://asciified.thelicato.io/api/v2/fonts" - Network accessline 81
- The response is plain text ASCII art — no JSON wrapping, ready to display - Font names are case-sensitive; use the fonts endpoint to get exact names - Works from any terminal with curl — no Python or pip needed - Network accessline 83
- Works from any terminal with curl — no Python or pip needed - Dangerous shell operationline 92
sudo apt install cowsay -y # Debian/Ubuntu - Dangerous shell operationline 136
sudo apt install boxes -y # Debian/Ubuntu - Network accessline 159
python -m pyfiglet "HERMES" -f slant | boxes -d stone # Or without pyfiglet installed: curl -s "https://asciified.thelicato.io/api/v2/ascii?text=HERMES&font=Slant" | boxes -d stone - Network accessline 161
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=HERMES&font=Slant" | boxes -d stone - Dangerous shell operationline 171
sudo apt install toilet toilet-fonts -y # Debian/Ubuntu - Dangerous shell operationline 202
sudo snap install ascii-image-converter - Network accessline 210
ascii-image-converter image.png -b # Braille characters ascii-image-converter image.png -n # Negative/inverted ascii-image-converter https://url/image.jpg # Direct URL - Network accessline 211
ascii-image-converter image.png -n # Negative/inverted ascii-image-converter https://url/image.jpg # Direct URL ascii-image-converter image.png --save-txt out # Save as text - Network accessline 212
ascii-image-converter https://url/image.jpg # Direct URL - Dangerous shell operationline 219
sudo apt install jp2a -y - Network accessline 232
**URL pattern:** `https://ascii.co.uk/art/{subject}` - Network accessline 237
curl -s 'https://ascii.co.uk/art/cat' -o ~/.hermes/cache/scratch/ascii_art.html - Network accessline 272
curl -s https://api.github.com/octocat - Network accessline 282
curl -s "qrenco.de/Hello+World" - Network accessline 283
curl -s "qrenco.de/https://example.com" - Network accessline 289
curl -s "wttr.in/London" # Full weather report with ASCII graphics - Network accessline 290
curl -s "wttr.in/Moon" # Moon phase in ASCII art - Network accessline 291
curl -s "v2.wttr.in/London" # Detailed version - Network accessline 314
1. **Text as a banner** → pyfiglet if installed, otherwise asciified API via curl 2. **Wrap a message in fun character art** → cowsay - Network accessline 315
2. **Wrap a message in fun character art** → cowsay 3. **Add decorative border/frame** → boxes (can combine with pyfiglet/asciified) 4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing - Network accessline 316
3. **Add decorative border/frame** → boxes (can combine with pyfiglet/asciified) 4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing 5. **Convert an image to ASCII** → ascii-image-converter or jp2a - Network accessline 317
4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing - Network accessline 318
5. **Convert an image to ASCII** → ascii-image-converter or jp2a 6. **QR code** → qrenco.de via curl 7. **Weather/moon art** → wttr.in via curl - Network accessline 319
6. **QR code** → qrenco.de via curl 7. **Weather/moon art** → wttr.in via curl 8. **Something custom/creative** → LLM generation with Unicode palette - Network accessline 320
7. **Weather/moon art** → wttr.in via curl 8. **Something custom/creative** → LLM generation with Unicode palette 9. **Any tool not installed** → install it, or fall back to next option
optional-skills/creative/auteur/LICENSE
- Network accessline 1
auteur — ported from https://github.com/agiwhitelist/auteur - Network accessline 2
Upstream snapshot: commit 9bca227df9877e60dc45d49783c8cbd885eccd9b (2026-08-06) Upstream author / copyright holder: agiwhitelist (https://github.com/agiwhitelist) The MIT license text below is reproduced verbatim from the upstream LICENSE. - Network accessline 3
Upstream author / copyright holder: agiwhitelist (https://github.com/agiwhitelist)
optional-skills/creative/auteur/SKILL.md
- Network accessline 3
description: Design and build cinematic, award-level web pages. version: 1.3.1 author: agiwhitelist (https://github.com/agiwhitelist, upstream agiwhitelist/auteur), ported by Hermes Agent - Network accessline 4
version: 1.3.1 author: agiwhitelist (https://github.com/agiwhitelist, upstream agiwhitelist/auteur), ported by Hermes Agent license: MIT - Network accessline 5
author: agiwhitelist (https://github.com/agiwhitelist, upstream agiwhitelist/auteur), ported by Hermes Agent - Network accessline 11
category: creative related_skills: [popular-web-designs, design-md, p5js] upstream: https://github.com/agiwhitelist/auteur (pinned 9bca227d) - Network accessline 12
related_skills: [popular-web-designs, design-md, p5js] upstream: https://github.com/agiwhitelist/auteur (pinned 9bca227d) --- - Network accessline 13
upstream: https://github.com/agiwhitelist/auteur (pinned 9bca227d) - Network accessline 18
> Ported from [agiwhitelist/auteur](https://github.com/agiwhitelist/auteur) (MIT), snapshot - Network accessline 19
> commit [`9bca227d`](https://github.com/agiwhitelist/auteur/commit/9bca227df9877e60dc45d49783c8cbd885eccd9b)
optional-skills/creative/auteur/references/ambient-backgrounds.md
- Network accessline 72
.amb-grain{position:fixed;inset:0;z-index:0;pointer-events:none;opacity:.05; background-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='160' height='160'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoi - Network accessline 73
background-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='160' height='160'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.82' numOctaves='2' stitchTiles='stitch'/%3E%3C/filter%3E%3 - Dangerous shell operationline 107
requestAnimationFrame(frame); // RM draws one still frame and stops - Dangerous shell operationline 124
// per frame: y -= 0.00006 (wrap), draw each at alpha .07 in the brand hue; RM → draw once.
optional-skills/creative/auteur/references/assets.md
optional-skills/creative/auteur/references/recon.md
optional-skills/creative/auteur/references/scroll-cinema.md
- Network accessline 484
const draco = new DRACOLoader() draco.setDecoderPath('https://www.gstatic.com/draco/versioned/decoders/1.5.7/') - Network accessline 485
draco.setDecoderPath('https://www.gstatic.com/draco/versioned/decoders/1.5.7/') - Network accessline 568
a.addEventListener('click', async e => { e.preventDefault() navigate(await (await fetch(a.href)).text()) - Network accessline 569
e.preventDefault() navigate(await (await fetch(a.href)).text()) }) - Network accessline 570
navigate(await (await fetch(a.href)).text())
optional-skills/creative/auteur/references/system.md
- Network accessline 102
node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing,/team
optional-skills/creative/auteur/references/verify.md
- Network accessline 119
node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing,/team
optional-skills/creative/auteur/scripts/chromadiff.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/creative/auteur/scripts/moodboard.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 68
const out = []; try { await open(page, 'https://www.bing.com/images/search?q=' + encodeURIComponent(q) + '&form=HDRSC2', 3500); - Network accessline 69
try { await open(page, 'https://www.bing.com/images/search?q=' + encodeURIComponent(q) + '&form=HDRSC2', 3500); for (let pass = 0; pass < 3 && out.length < want; pass++) { - Network accessline 70
await open(page, 'https://www.bing.com/images/search?q=' + encodeURIComponent(q) + '&form=HDRSC2', 3500); - Network accessline 88
const out = []; try { await open(page, 'https://www.pinterest.com/search/pins/?q=' + encodeURIComponent(q) + '&rs=typed', 6000); - Network accessline 89
try { await open(page, 'https://www.pinterest.com/search/pins/?q=' + encodeURIComponent(q) + '&rs=typed', 6000); for (let pass = 0; pass < 4 && out.length < want; pass++) { - Network accessline 90
await open(page, 'https://www.pinterest.com/search/pins/?q=' + encodeURIComponent(q) + '&rs=typed', 6000); - Network accessline 98
const img = it.src.replace(/\/\d+x\//, '/736x/'); if (out.length >= want) break; if (!out.some(o => o.img === img)) out.push({ img, thumb: it.src, page: it.pin ? 'https://www.pinterest.com' + it.pin : 'https://www.pinterest. - Network accessline 99
if (out.length >= want) break; if (!out.some(o => o.img === img)) out.push({ img, thumb: it.src, page: it.pin ? 'https://www.pinterest.com' + it.pin : 'https://www.pinterest.com/search/pins/?q=' + encodeURIComponent(q), source: 'pin - Network accessline 100
if (!out.some(o => o.img === img)) out.push({ img, thumb: it.src, page: it.pin ? 'https://www.pinterest.com' + it.pin : 'https://www.pinterest.com/search/pins/?q=' + encodeURIComponent(q), source: 'pinterest' }); - Network accessline 112
const out = []; try { const res = await ctx.request.get(`https://api.are.na/v2/search?q=${encodeURIComponent(q)}&per=${Math.min(want * 2, 40)}`, { timeout: 20_000 }); - Network accessline 113
try { const res = await ctx.request.get(`https://api.are.na/v2/search?q=${encodeURIComponent(q)}&per=${Math.min(want * 2, 40)}`, { timeout: 20_000 }); const json = await res.json(); - Network accessline 114
const res = await ctx.request.get(`https://api.are.na/v2/search?q=${encodeURIComponent(q)}&per=${Math.min(want * 2, 40)}`, { timeout: 20_000 }); - Network accessline 117
const img = b.image?.large?.url || b.image?.display?.url || b.image?.original?.url; if (!img || out.length >= want) continue; out.push({ img, thumb: b.image?.thumb?.url, page: `https://www.are.na/block/${b.id}`, source: 'arena', - Network accessline 118
if (!img || out.length >= want) continue; out.push({ img, thumb: b.image?.thumb?.url, page: `https://www.are.na/block/${b.id}`, source: 'arena', title: b.title }); } - Network accessline 119
out.push({ img, thumb: b.image?.thumb?.url, page: `https://www.are.na/block/${b.id}`, source: 'arena', title: b.title });
optional-skills/creative/auteur/scripts/motionqa.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/creative/auteur/scripts/refscout.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 284
async function harvestAwwwards(ctx, tag, text, n) { const page = await ctx.newPage(); const base = 'https://www.awwwards.com/websites/'; - Network accessline 285
const page = await ctx.newPage(); const base = 'https://www.awwwards.com/websites/'; const url = text ? `${base}?text=${encodeURIComponent(text)}` : tag ? `${base}${tag}/` : base; - Network accessline 286
const base = 'https://www.awwwards.com/websites/'; - Network accessline 294
for (const slug of slugs.slice(0, n)) { try { await open(page, 'https://www.awwwards.com' + slug, 3500); - Network accessline 295
try { await open(page, 'https://www.awwwards.com' + slug, 3500); const d = await page.evaluate(() => { - Network accessline 296
await open(page, 'https://www.awwwards.com' + slug, 3500);
optional-skills/creative/auteur/scripts/shoot.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/creative/auteur/scripts/slopscan.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/creative/auteur/scripts/source.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 73
async function json(url) { const r = await fetch(url, { headers: H, signal: AbortSignal.timeout(45_000) }); - Network accessline 74
const r = await fetch(url, { headers: H, signal: AbortSignal.timeout(45_000) }); - Network accessline 77
} async function text(url) { const r = await fetch(url, { headers: H, signal: AbortSignal.timeout(45_000) }); - Network accessline 78
async function text(url) { const r = await fetch(url, { headers: H, signal: AbortSignal.timeout(45_000) }); if (!r.ok) throw new Error(`${r.status} ${url.slice(0, 70)}`); - Network accessline 79
const r = await fetch(url, { headers: H, signal: AbortSignal.timeout(45_000) }); - Network accessline 95
async function polyhaven(type) { const all = await json(`https://api.polyhaven.com/assets?t=${type}`); - Network accessline 96
const all = await json(`https://api.polyhaven.com/assets?t=${type}`); - Network accessline 106
const out = []; for (const { slug, a, score } of ranked) { const files = await json(`https://api.polyhaven.com/files/${slug}`).catch(() => null); - Network accessline 107
for (const { slug, a, score } of ranked) { const files = await json(`https://api.polyhaven.com/files/${slug}`).catch(() => null); if (!files) continue; - Network accessline 108
const files = await json(`https://api.polyhaven.com/files/${slug}`).catch(() => null); - Network accessline 114
attribution: null, // CC0: none required credit: Object.keys(a.authors || {}).join(', '), landing: `https://polyhaven.com/a/${slug}`, - Network accessline 115
credit: Object.keys(a.authors || {}).join(', '), landing: `https://polyhaven.com/a/${slug}`, note: [a.categories?.join('/'), Object.entries(a.attributes || {}).map(([k, v]) => `${k}=${v}`).join(' ')].filter(Boolean).join(' · '), - Network accessline 116
landing: `https://polyhaven.com/a/${slug}`, - Network accessline 145
async function icons() { const collections = await json('https://api.iconify.design/collections').catch(() => ({})); - Network accessline 146
const collections = await json('https://api.iconify.design/collections').catch(() => ({})); - Network accessline 150
for (const term of terms) { if (out.length >= limit) break; const r = await json(`https://api.iconify.design/search?query=${encodeURIComponent(term)}&limit=${limit * 4}`).catch(() => null); - Network accessline 151
if (out.length >= limit) break; const r = await json(`https://api.iconify.design/search?query=${encodeURIComponent(term)}&limit=${limit * 4}`).catch(() => null); for (const id of r?.icons || []) { - Network accessline 152
const r = await json(`https://api.iconify.design/search?query=${encodeURIComponent(term)}&limit=${limit * 4}`).catch(() => null); - Network accessline 160
licence: c.license?.title || 'unknown', attribution: /CC.?BY/i.test(c.license?.title || '') ? `Icon "${name}" from ${c.name || set} — ${c.license?.title}` : null, credit: c.author?.name || '', landing: c.license?.url || `htt - Network accessline 161
attribution: /CC.?BY/i.test(c.license?.title || '') ? `Icon "${name}" from ${c.name || set} — ${c.license?.title}` : null, credit: c.author?.name || '', landing: c.license?.url || `https://icon-sets.iconify.design/${set}/`, - Network accessline 162
credit: c.author?.name || '', landing: c.license?.url || `https://icon-sets.iconify.design/${set}/`, - Network accessline 163
note: `set has ${c.total || '?'} icons`, files: [{ url: `https://api.iconify.design/${set}/${name}.svg?height=64`, path: `${set}-${name}.svg` }], }); - Network accessline 164
files: [{ url: `https://api.iconify.design/${set}/${name}.svg?height=64`, path: `${set}-${name}.svg` }], - Network accessline 178
if (existsSync(cacheFile)) meta = JSON.parse(await readFile(cacheFile, 'utf8')); else { meta = JSON.parse((await text('https://fonts.google.com/metadata/fonts')).replace(/^\)\]\}'\n?/, '')); - Network accessline 180
meta = JSON.parse((await text('https://fonts.google.com/metadata/fonts')).replace(/^\)\]\}'\n?/, '')); - Network accessline 234
id: f.family, title: `${f.family} (${f.category})`, licence: 'OFL / Apache-2.0 (Google Fonts)', attribution: null, credit: '', landing: `https://fonts.google.com/specimen/${f.family.replace(/ /g, '+')}`, - Network accessline 235
licence: 'OFL / Apache-2.0 (Google Fonts)', attribution: null, credit: '', landing: `https://fonts.google.com/specimen/${f.family.replace(/ /g, '+')}`, note: `${banned ? '⛔ ON THE AUTEUR BAN LIST — pick something else. ' : ''}po - Network accessline 236
landing: `https://fonts.google.com/specimen/${f.family.replace(/ /g, '+')}`, - Network accessline 237
note: `${banned ? '⛔ ON THE AUTEUR BAN LIST — pick something else. ' : ''}popularity #${f.popularity ?? '?'} · weights ${Object.keys(f.fonts || {}).length}${axes ? ` · variable: ${axes}` : ' · static only'}`, css: `https://fonts.googl - Network accessline 238
css: `https://fonts.googleapis.com/css2?family=${spec}&display=swap`, - Network accessline 265
const r = await json(`https://api.openverse.org/v1/images/?q=${encodeURIComponent(query)}&page_size=${limit}&license_type=commercial,modification`); - Network accessline 279
async function videos() { let html; try { html = await text(`https://coverr.co/s?q=${encodeURIComponent(query)}`); } - Network accessline 280
let html; try { html = await text(`https://coverr.co/s?q=${encodeURIComponent(query)}`); } catch (e) { console.error(`[source] coverr: ${e.message}`); return []; } - Network accessline 281
try { html = await text(`https://coverr.co/s?q=${encodeURIComponent(query)}`); } - Network accessline 295
licence: 'Coverr License — free commercial use, redistribution prohibited', attribution: null, credit: 'Coverr', landing: `https://coverr.co/videos/${slug}`, - Network accessline 296
attribution: null, credit: 'Coverr', landing: `https://coverr.co/videos/${slug}`, note: `${res} · ⚠ stock — ambient loop / texture / fallback only, never the peak scene`, - Network accessline 297
landing: `https://coverr.co/videos/${slug}`, - Network accessline 338
if (existsSync(dest)) { f.skipped = true; continue; } // never re-download a cached master try { const r = await fetch(f.url, { headers: { ...H, Referer: it.landing || '' }, signal: AbortSignal.timeout(120_000) }); - Network accessline 339
try { const r = await fetch(f.url, { headers: { ...H, Referer: it.landing || '' }, signal: AbortSignal.timeout(120_000) }); if (!r.ok) { f.error = `HTTP ${r.status}`; continue; } - Network accessline 340
const r = await fetch(f.url, { headers: { ...H, Referer: it.landing || '' }, signal: AbortSignal.timeout(120_000) });
optional-skills/creative/auteur/scripts/systemscan.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 10
* Usage: * node scripts/systemscan.mjs <url> [<url>...] * node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing - Network accessline 11
* node scripts/systemscan.mjs <url> [<url>...] * node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing * - Network accessline 12
* node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing - Network accessline 33
node scripts/systemscan.mjs <url> [<url>...] node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing - Network accessline 34
node scripts/systemscan.mjs http://localhost:3000 --routes /,/settings,/billing
optional-skills/creative/auteur/templates/scroll-flight-engine.js
optional-skills/creative/baoyu-article-illustrator/PORT_NOTES.md
- Network accessline 3
Ported from [JimLiu/baoyu-skills](https://github.com/JimLiu/baoyu-skills) v1.57.0. - Network accessline 40
# Compare versions curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-article-illustrator/SKILL.md | head -5 - Network accessline 41
curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-article-illustrator/SKILL.md | head -5 - Network accessline 44
# Diff style/palette files (safe to overwrite — unchanged from upstream) diff <(curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-article-illustrator/references/styles/blueprint.md) references/styles/blueprint - Network accessline 45
diff <(curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-article-illustrator/references/styles/blueprint.md) references/styles/blueprint.md
optional-skills/creative/baoyu-article-illustrator/SKILL.md
- Network accessline 10
tags: [article-illustration, creative, image-generation] category: creative homepage: https://github.com/JimLiu/baoyu-skills#baoyu-article-illustrator - Network accessline 11
category: creative homepage: https://github.com/JimLiu/baoyu-skills#baoyu-article-illustrator --- - Network accessline 12
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-article-illustrator - Network accessline 17
Adapted from [baoyu-article-illustrator](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem. - Network accessline 162
1. Call `image_generate(prompt=..., aspect_ratio=...)`. `image_generate` returns a JSON result containing an image URL; it does NOT write to disk and does NOT accept an output path. 2. Map the prompt's `ASPECT` to `image_generate`'s enum: ` - Network accessline 163
2. Map the prompt's `ASPECT` to `image_generate`'s enum: `16:9` → `landscape`, `9:16` → `portrait`, `1:1` → `square`. Custom ratios → nearest named aspect. 3. Download the returned URL to `{output-dir}/NN-{type}-{slug}.png` via `terminal` ( - Network accessline 164
3. Download the returned URL to `{output-dir}/NN-{type}-{slug}.png` via `terminal` (e.g. `curl -sSL -o "{output-dir}/NN-{type}-{slug}.png" "{url}"`).
optional-skills/creative/baoyu-article-illustrator/references/workflow.md
- Network accessline 24
1. Call `vision_analyze(image_url=..., question="Describe the style, color palette (with hex approximations), composition, and subject so this can be used as a style/palette reference for another illustration.")`. 2. Write `{output-dir}/ref - Network accessline 25
2. Write `{output-dir}/references/NN-ref-{slug}.md` via `write_file` with the description. 3. (Optional) Run `terminal` with `cp` (or `curl -sSL -o ...` for URLs) to keep a local binary copy. Not required by the skill. 4. Mark the reference - Network accessline 26
3. (Optional) Run `terminal` with `cp` (or `curl -sSL -o ...` for URLs) to keep a local binary copy. Not required by the skill. - Network accessline 291
curl -sSL -o "{output-dir}/NN-{type}-{slug}.png" "{image_url}" - Network accessline 293
If `curl` is unavailable, fall back to `wget -qO "{output-dir}/NN-{type}-{slug}.png" "{image_url}"`.
optional-skills/creative/baoyu-comic/PORT_NOTES.md
- Network accessline 3
Ported from [JimLiu/baoyu-skills](https://github.com/JimLiu/baoyu-skills) v1.56.1. - Network accessline 36
- **User-supplied reference images** are reduced to `style` / `palette` / `scene` trait extraction — traits are embedded in the prompt body; the image files themselves are kept only for provenance under `refs/`. - **Page prompts** now manda - Network accessline 38
- **Download step** — after every `image_generate` call, the returned URL is fetched to disk (e.g., `curl -fsSL "<url>" -o <target>.png`) and verified before the workflow advances. - Network accessline 66
# Compare versions curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/SKILL.md | head -5 - Network accessline 67
curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/SKILL.md | head -5 - Network accessline 70
# Diff a reference file diff <(curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/references/art-styles/manga.md) \ - Network accessline 71
diff <(curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/references/art-styles/manga.md) \
optional-skills/creative/baoyu-comic/SKILL.md
- Network accessline 9
hermes: tags: [comic, knowledge-comic, creative, image-generation] homepage: https://github.com/JimLiu/baoyu-skills#baoyu-comic - Network accessline 10
tags: [comic, knowledge-comic, creative, image-generation] homepage: https://github.com/JimLiu/baoyu-skills#baoyu-comic --- - Network accessline 11
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-comic - Network accessline 16
Adapted from [baoyu-comic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem. - Network accessline 194
1. Read the URL from the tool result 2. Fetch the image bytes using an **absolute** output path, e.g. `curl -fsSL "<url>" -o /abs/path/to/comic/<slug>/NN-page-<slug>.png` - Network accessline 195
2. Fetch the image bytes using an **absolute** output path, e.g. `curl -fsSL "<url>" -o /abs/path/to/comic/<slug>/NN-page-<slug>.png` 3. Verify the file exists and is non-empty at that exact path before proceeding to the next page - Network accessline 196
`curl -fsSL "<url>" -o /abs/path/to/comic/<slug>/NN-page-<slug>.png` - Network accessline 199
**Never rely on shell CWD persistence for `-o` paths.** The terminal tool's persistent-shell CWD can change between batches (session expiry, `TERMINAL_LIFETIME_SECONDS`, a failed `cd` that leaves you in the wrong directory). `curl -o relati - Network accessline 240
- Image generation: 10-30 seconds per page; auto-retry once on failure - **Always download** the URL returned by `image_generate` to a local PNG — downstream tooling (and the user's review) expects files in the output directory, not ephemer - Network accessline 241
- **Always download** the URL returned by `image_generate` to a local PNG — downstream tooling (and the user's review) expects files in the output directory, not ephemeral URLs - **Use absolute paths for `curl -o`** — never rely on persiste - Network accessline 242
- **Use absolute paths for `curl -o`** — never rely on persistent-shell CWD across batches. Silent footgun: files land in the wrong directory and subsequent `ls` on the intended path shows nothing. See Step 7 "Download step".
optional-skills/creative/baoyu-comic/references/workflow.md
optional-skills/creative/brag-slim/SKILL.md
- Network accessline 20
> **Catalog stub.** This entry is maintained upstream at > [latent-spaces/brag](https://github.com/latent-spaces/brag): the project - Network accessline 21
> [latent-spaces/brag](https://github.com/latent-spaces/brag): the project - Network accessline 46
Full documentation: https://github.com/latent-spaces/brag#readme
optional-skills/creative/brag/SKILL.md
- Network accessline 20
> **Catalog stub.** This entry is maintained upstream at > [latent-spaces/brag](https://github.com/latent-spaces/brag): the project - Network accessline 21
> [latent-spaces/brag](https://github.com/latent-spaces/brag): the project - Network accessline 59
Full documentation: https://github.com/latent-spaces/brag#readme
optional-skills/creative/comfyui/SKILL.md
- Network accessline 44
(`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch. Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever - Network accessline 45
free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch. Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever you're starting from an official template. - Network accessline 46
Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever - Network accessline 105
# What's available? command -v comfy >/dev/null 2>&1 && echo "comfy-cli: installed" curl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo "server: running" - Network accessline 107
curl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo "server: running" - Network accessline 151
# Local (defaults to http://127.0.0.1:8188) - Access to secretsline 158
[Credential-related source omitted] - Network accessline 159
python scripts/run_workflow.py --workflow workflow_api.json --args '{"prompt": "..."}' --host https://cloud.comfy.org - Network accessline 160
--workflow workflow_api.json \ --args '{"prompt": "..."}' \ --host https://cloud.comfy.org \ - Network accessline 161
--args '{"prompt": "..."}' \ --host https://cloud.comfy.org \ --output-dir ./outputs - Network accessline 162
--host https://cloud.comfy.org \ - Network accessline 224
| "fetch the error from job X" | script | `fetch_logs.py <prompt_id>` | | **Direct REST** | | | | "what's in the queue?" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` | - Network accessline 225
| **Direct REST** | | | | "what's in the queue?" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` | | "cancel that" | REST | `curl -X POST http://HOST:8188/interrupt` | - Network accessline 226
| "what's in the queue?" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` | - Network accessline 227
| "cancel that" | REST | `curl -X POST http://HOST:8188/interrupt` | - Network accessline 228
| "free GPU memory" | REST | `curl -X POST http://HOST:8188/free` | - Network accessline 237
**Official docs:** https://docs.comfy.org/installation - Network accessline 238
**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started - Network accessline 239
**Cloud docs:** https://docs.comfy.org/get_started/cloud - Network accessline 240
**Cloud API:** https://docs.comfy.org/development/cloud/overview - Network accessline 320
**Docs:** https://docs.comfy.org/get_started/cloud - Network accessline 322
1. Sign up at https://comfy.org/cloud - Network accessline 323
2. Generate an API key at https://platform.comfy.org/login - Access to secretsline 326
[Credential-related source omitted] - Network accessline 330
python scripts/run_workflow.py --workflow workflows/flux_dev_txt2img.json --args '{"prompt": "..."}' --host https://cloud.comfy.org - Network accessline 332
--args '{"prompt": "..."}' \ --host https://cloud.comfy.org \ - Network accessline 333
--host https://cloud.comfy.org \ - Network accessline 337
**Pricing:** https://www.comfy.org/cloud/pricing - Network accessline 348
**Docs:** https://docs.comfy.org/installation/desktop - **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64 - Network accessline 349
- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64 - Network accessline 350
- **macOS (Apple Silicon):** https://comfy.org - Network accessline 358
**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows - Network accessline 360
Download from https://github.com/comfyanonymous/ComfyUI/releases, extract, - Network accessline 369
**Docs:** https://docs.comfy.org/comfy-cli/getting-started - Network accessline 403
comfy launch --background # background daemon on :8188 comfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port curl -s http://127.0.0.1:8188/system_stats # health check - Network accessline 405
curl -s http://127.0.0.1:8188/system_stats # health check - Network accessline 414
**Docs:** https://docs.comfy.org/installation/manual_install - Network accessline 417
git clone https://github.com/comfyanonymous/ComfyUI.git cd ComfyUI pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130 - Network accessline 418
cd ComfyUI pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130 pip install -r requirements.txt - Network accessline 419
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130 - Network accessline 429
# SDXL (general purpose, ~6.5 GB) comfy model download \ --url "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" \ - Network accessline 430
comfy model download --url "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" - Network accessline 431
--url "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" \ - Network accessline 434
# SD 1.5 (lighter, ~4 GB, good for 6 GB cards) comfy model download \ --url "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" \ - Network accessline 435
comfy model download --url "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" - Network accessline 436
--url "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" \ - Network accessline 439
# Flux Dev fp8 (smaller variant, ~12 GB) comfy model download \ --url "https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors" \ - Network accessline 440
comfy model download --url "https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors" - Network accessline 441
--url "https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors" \ - Network accessline 444
# CivitAI (set token first): comfy model download \ --url "https://civitai.com/api/download/models/128713" \ - Network accessline 445
comfy model download --url "https://civitai.com/api/download/models/128713" - Network accessline 446
--url "https://civitai.com/api/download/models/128713" \ - Network accessline 503
curl -X POST "http://127.0.0.1:8188/upload/image" \ - Network accessline 507
# Cloud equivalent: curl -X POST "https://cloud.comfy.org/api/upload/image" \ - Network accessline 508
[Credential-related source omitted] - Access to secretsline 508
[Credential-related source omitted] - Access to secretsline 509
[Credential-related source omitted] - Network accessline 515
- **Base URL:** `https://cloud.comfy.org` - Access to secretsline 517
[Credential-related source omitted] - Network accessline 537
# Local curl -s http://127.0.0.1:8188/queue | python -m json.tool - Dangerous shell operationline 538
curl -s http://127.0.0.1:8188/queue | python -m json.tool - Network accessline 538
curl -s http://127.0.0.1:8188/queue | python -m json.tool - Network accessline 539
curl -X POST http://127.0.0.1:8188/queue -d '{"clear": true}' # cancel pending - Network accessline 540
curl -X POST http://127.0.0.1:8188/interrupt # cancel running - Network accessline 541
curl -X POST http://127.0.0.1:8188/free \ - Network accessline 545
# Cloud — same paths under /api/, plus: python scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org - Network accessline 546
python scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org - Network accessline 556
2. **Server must be running** — all execution requires a live server. `comfy launch --background` starts one. Verify with `curl http://127.0.0.1:8188/system_stats`. - Network accessline 558
`curl http://127.0.0.1:8188/system_stats`. - Network accessline 603
- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud - [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`) - [ ] `curl http://HOST:PORT/system_stats` returns JSON - Network accessline 604
- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`) - [ ] `curl http://HOST:PORT/system_stats` returns JSON - [ ] `comfy model list` shows at least one checkpoint (local) OR - Network accessline 605
- [ ] `curl http://HOST:PORT/system_stats` returns JSON
optional-skills/creative/comfyui/references/official-cli.md
- Network accessline 3
Official CLI from [Comfy-Org/comfy-cli](https://github.com/Comfy-Org/comfy-cli). - Network accessline 4
Docs: https://docs.comfy.org/comfy-cli/getting-started - Network accessline 249
Custom-node registry: - https://registry.comfy.org/ - Network accessline 250
- https://registry.comfy.org/ - Network accessline 252
Model browsers: - https://huggingface.co/models - Network accessline 253
- https://huggingface.co/models - Network accessline 254
- https://civitai.com (NSFW; requires API token for many) - Network accessline 255
- https://comfyworkflows.com (community workflows)
optional-skills/creative/comfyui/references/rest-api.md
- Network accessline 9
| | Local ComfyUI | Comfy Cloud | |---|---|---| | Base URL | `http://127.0.0.1:8188` | `https://cloud.comfy.org` | - Network accessline 10
|---|---|---| | Base URL | `http://127.0.0.1:8188` | `https://cloud.comfy.org` | | API path prefix | none (`/prompt`, `/view`, …) | `/api/...` (`/api/prompt`, `/api/view`, …) | - Network accessline 11
| Base URL | `http://127.0.0.1:8188` | `https://cloud.comfy.org` | - Network accessline 13
[Credential-related source omitted] - Access to secretsline 14
[Credential-related source omitted] - Network accessline 15
| `/api/view` response | direct bytes | 302 redirect → signed URL (use `curl -L`) | - Network accessline 54
# Local curl -X POST "http://127.0.0.1:8188/prompt" \ - Network accessline 55
curl -X POST "http://127.0.0.1:8188/prompt" \ - Network accessline 59
# Cloud curl -X POST "https://cloud.comfy.org/api/prompt" \ - Network accessline 60
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 61
[Credential-related source omitted] - Network accessline 77
[Credential-related source omitted] - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted] - Network accessline 92
[Credential-related source omitted] - Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Network accessline 103
curl -s "http://127.0.0.1:8188/history" # all - Network accessline 104
curl -s "http://127.0.0.1:8188/history/{id}" # one prompt_id - Network accessline 128
# Local (direct bytes) curl -s "http://127.0.0.1:8188/view?filename=ComfyUI_00001_.png&subfolder=&type=output" \ - Network accessline 129
curl -s "http://127.0.0.1:8188/view?filename=ComfyUI_00001_.png&subfolder=&type=output" \ - Network accessline 132
# Cloud (302 → signed URL; -L follows; STRIP X-API-Key for the second hop) curl -L "https://cloud.comfy.org/api/view?filename=...&type=output" \ - Network accessline 133
[Credential-related source omitted] - Access to secretsline 133
[Credential-related source omitted] - Access to secretsline 134
[Credential-related source omitted] - Access to secretsline 150
[Credential-related source omitted] - Network accessline 186
# Image curl -X POST "http://127.0.0.1:8188/upload/image" \ - Network accessline 187
curl -X POST "http://127.0.0.1:8188/upload/image" \ - Network accessline 191
# Mask (linked to a previously uploaded image) curl -X POST "http://127.0.0.1:8188/upload/mask" \ - Network accessline 192
curl -X POST "http://127.0.0.1:8188/upload/mask" \ - Network accessline 197
[Credential-related source omitted] - Access to secretsline 197
[Credential-related source omitted] - Network accessline 202
# All node types and their input specs curl -s "http://127.0.0.1:8188/object_info" | python3 -m json.tool - Dangerous shell operationline 203
curl -s "http://127.0.0.1:8188/object_info" | python3 -m json.tool - Network accessline 203
curl -s "http://127.0.0.1:8188/object_info" | python3 -m json.tool - Network accessline 205
# Specific node curl -s "http://127.0.0.1:8188/object_info/KSampler" - Network accessline 206
curl -s "http://127.0.0.1:8188/object_info/KSampler" - Network accessline 208
# Models per folder (local) curl -s "http://127.0.0.1:8188/models/checkpoints" - Network accessline 209
curl -s "http://127.0.0.1:8188/models/checkpoints" - Network accessline 210
curl -s "http://127.0.0.1:8188/models/loras" - Network accessline 212
# Models per folder (cloud — note the experimental prefix) curl -s "https://cloud.comfy.org/api/experiment/models/checkpoints" \ - Network accessline 213
[Credential-related source omitted] - Access to secretsline 213
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 220
# View queue curl -s "http://127.0.0.1:8188/queue" - Network accessline 221
curl -s "http://127.0.0.1:8188/queue" - Network accessline 223
# Clear all pending curl -X POST "http://127.0.0.1:8188/queue" \ - Network accessline 224
curl -X POST "http://127.0.0.1:8188/queue" \ - Network accessline 228
# Delete specific items curl -X POST "http://127.0.0.1:8188/queue" \ - Network accessline 229
curl -X POST "http://127.0.0.1:8188/queue" \ - Network accessline 233
# Cancel currently-running job curl -X POST "http://127.0.0.1:8188/interrupt" - Network accessline 234
curl -X POST "http://127.0.0.1:8188/interrupt" - Network accessline 240
# Stats (VRAM, RAM, GPU, ComfyUI version) curl -s "http://127.0.0.1:8188/system_stats" - Network accessline 241
curl -s "http://127.0.0.1:8188/system_stats" - Network accessline 243
# Free GPU memory curl -X POST "http://127.0.0.1:8188/free" \ - Network accessline 244
curl -X POST "http://127.0.0.1:8188/free" \ - Network accessline 255
# Install a custom node from a git URL curl -X POST "http://127.0.0.1:8188/manager/queue/install" \ - Network accessline 256
curl -X POST "http://127.0.0.1:8188/manager/queue/install" \ - Network accessline 258
-d '{"git_url": "https://github.com/user/comfyui-node.git"}' - Network accessline 260
# Check install queue status curl -s "http://127.0.0.1:8188/manager/queue/status" - Network accessline 261
curl -s "http://127.0.0.1:8188/manager/queue/status" - Network accessline 263
# Install model curl -X POST "http://127.0.0.1:8188/manager/queue/install_model" \ - Network accessline 264
curl -X POST "http://127.0.0.1:8188/manager/queue/install_model" \ - Network accessline 266
-d '{"url": "https://...", "path": "models/checkpoints", "filename": "model.safetensors"}'
optional-skills/creative/comfyui/references/template-integrity.md
- Network accessline 3
> **Authored by [@purzbeats](https://github.com/purzbeats)** — adapted from - Network accessline 4
> [purzbeats/hermes-agent-comfyui-helper](https://github.com/purzbeats/hermes-agent-comfyui-helper). - Network accessline 200
for debugging. - Cloud `/api/view` returns **302 redirect to signed GCS URL** — use `curl -s -L` to follow and download. Python `urllib` fails with 401 - Network accessline 201
- Cloud `/api/view` returns **302 redirect to signed GCS URL** — use `curl -s -L` to follow and download. Python `urllib` fails with 401 (forwards auth headers to GCS CDN). - Network accessline 202
`curl -s -L` to follow and download. Python `urllib` fails with 401 - Access to secretsline 204
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/_common.py
- Executable scriptline 1
Executable file - Network accessline 43
DEFAULT_LOCAL_HOST = "http://127.0.0.1:8188" - Network accessline 44
DEFAULT_CLOUD_HOST = "https://cloud.comfy.org" - Access to secretsline 45
[Credential-related source omitted] - Network accessline 337
parsed = urlparse(host if "://" in host else f"http://{host}") - Access to secretsline 391
[Credential-related source omitted] - Access to secretsline 393
[Credential-related source omitted] - Network accessline 575
else: body_bytes = data req = urllib.request.Request(url, data=body_bytes, headers=headers, method=method) - Network accessline 577
req = urllib.request.Request(url, data=body_bytes, headers=headers, method=method) - Network accessline 595
if k.lower() not in {"x-api-key", "authorization", "cookie"} } new_req = urllib.request.Request(newurl, headers=clean_headers, method="GET") - Network accessline 596
} new_req = urllib.request.Request(newurl, headers=clean_headers, method="GET") return new_req - Network accessline 597
new_req = urllib.request.Request(newurl, headers=clean_headers, method="GET")
optional-skills/creative/comfyui/scripts/auto_fix_deps.py
optional-skills/creative/comfyui/scripts/check_deps.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 15
[Credential-related source omitted] - Network accessline 18
python3 check_deps.py workflow_api.json python3 check_deps.py workflow_api.json --host 127.0.0.1 --port 8188 python3 check_deps.py workflow_api.json --host https://cloud.comfy.org - Network accessline 20
python3 check_deps.py workflow_api.json --host https://cloud.comfy.org - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Network accessline 117
NODE_TO_GIT_URL: dict[str, str] = { "HunyuanVideoSampler": "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper", - Network accessline 118
"HunyuanVideoSampler": "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper", - Network accessline 119
"HunyuanVideoModelLoader": "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper", - Access to secretsline 268
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Network accessline 303
else: entry["fix_hint"] = ( "Search https://registry.comfy.org or " - Network accessline 304
entry["fix_hint"] = ( "Search https://registry.comfy.org or " "use ComfyUI-Manager UI to find the package providing this node." - Network accessline 305
"Search https://registry.comfy.org or " - Access to secretsline 391
[Credential-related source omitted] - Network accessline 399
from urllib.parse import urlparse, urlunparse parsed = urlparse(host if "://" in host else f"http://{host}") - Network accessline 400
parsed = urlparse(host if "://" in host else f"http://{host}") - Access to secretsline 404
[Credential-related source omitted] - Access to secretsline 422
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/comfyui_setup.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 123
log "" log "Hardware check: this machine is not suitable for local ComfyUI." log "Recommended: Comfy Cloud — https://platform.comfy.org" - Network accessline 124
log "Hardware check: this machine is not suitable for local ComfyUI." log "Recommended: Comfy Cloud — https://platform.comfy.org" log "" - Network accessline 125
log "Recommended: Comfy Cloud — https://platform.comfy.org" - Network accessline 133
log "Hardware check: verdict is MARGINAL." log " SD1.5 should work; SDXL/Flux may be slow or OOM." log " Consider Comfy Cloud for heavier workflows: https://platform.comfy.org" - Network accessline 134
log " SD1.5 should work; SDXL/Flux may be slow or OOM." log " Consider Comfy Cloud for heavier workflows: https://platform.comfy.org" fi - Network accessline 135
log " Consider Comfy Cloud for heavier workflows: https://platform.comfy.org" - Network accessline 172
else log "Neither pipx nor uvx found. Falling back to pip install --user…" log " (Recommend installing pipx: https://pipx.pypa.io)" - Network accessline 173
log "Neither pipx nor uvx found. Falling back to pip install --user…" log " (Recommend installing pipx: https://pipx.pypa.io)" if ! pip install --user comfy-cli >>"$LOG_FILE" 2>&1; then - Network accessline 174
log " (Recommend installing pipx: https://pipx.pypa.io)" - Network accessline 237
if curl -fsS "http://127.0.0.1:$PORT/system_stats" >/dev/null 2>&1; then - Network accessline 238
log "Server already running on port $PORT — skipping launch." log "Stop with \`$COMFY_BIN stop\` if you want a fresh start." curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true - Network accessline 239
log "Stop with \`$COMFY_BIN stop\` if you want a fresh start." curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true log "Done." - Dangerous shell operationline 240
curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true - Network accessline 240
curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true - Network accessline 258
ELAPSED=0 while [ $ELAPSED -lt $MAX_WAIT ]; do if curl -fsS "http://127.0.0.1:$PORT/system_stats" >/dev/null 2>&1; then - Network accessline 259
while [ $ELAPSED -lt $MAX_WAIT ]; do if curl -fsS "http://127.0.0.1:$PORT/system_stats" >/dev/null 2>&1; then log "Server is running!" - Network accessline 260
if curl -fsS "http://127.0.0.1:$PORT/system_stats" >/dev/null 2>&1; then - Network accessline 261
log "Server is running!" curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true break - Dangerous shell operationline 262
curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true - Network accessline 262
curl -fsS "http://127.0.0.1:$PORT/system_stats" | python3 -m json.tool 2>/dev/null || true - Network accessline 276
log "" log "Setup complete!" log " Server: http://127.0.0.1:$PORT" - Network accessline 277
log "Setup complete!" log " Server: http://127.0.0.1:$PORT" log " Web UI: http://127.0.0.1:$PORT (open in browser)" - Network accessline 278
log " Server: http://127.0.0.1:$PORT" - Network accessline 279
log " Web UI: http://127.0.0.1:$PORT (open in browser)"
optional-skills/creative/comfyui/scripts/extract_schema.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/creative/comfyui/scripts/fetch_logs.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 9
Usage: python3 fetch_logs.py <prompt_id> python3 fetch_logs.py <prompt_id> --host https://cloud.comfy.org - Network accessline 10
python3 fetch_logs.py <prompt_id> python3 fetch_logs.py <prompt_id> --host https://cloud.comfy.org python3 fetch_logs.py --tail-queue # show currently queued/running jobs - Network accessline 11
python3 fetch_logs.py <prompt_id> --host https://cloud.comfy.org - Access to secretsline 23
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 121
[Credential-related source omitted] - Access to secretsline 128
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/hardware_check.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 433
def _install_urls() -> dict: return { "desktop": "https://docs.comfy.org/installation/desktop", - Network accessline 434
return { "desktop": "https://docs.comfy.org/installation/desktop", "manual": "https://docs.comfy.org/installation/manual_install", - Network accessline 435
"desktop": "https://docs.comfy.org/installation/desktop", - Network accessline 436
"manual": "https://docs.comfy.org/installation/manual_install", - Network accessline 437
"comfy_cli": "https://docs.comfy.org/comfy-cli/getting-started", - Network accessline 438
"cloud": "https://platform.comfy.org",
optional-skills/creative/comfyui/scripts/health_check.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 12
Usage: python3 health_check.py python3 health_check.py --host https://cloud.comfy.org - Network accessline 13
python3 health_check.py python3 health_check.py --host https://cloud.comfy.org python3 health_check.py --workflow my.json - Network accessline 14
python3 health_check.py --host https://cloud.comfy.org - Access to secretsline 29
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Access to secretsline 152
[Credential-related source omitted] - Access to secretsline 169
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/run_batch.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Access to secretsline 170
[Credential-related source omitted] - Access to secretsline 171
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/run_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 8
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 30
python3 run_workflow.py --workflow workflow_api.json \ --args '{"prompt": "a cat"}' \ --host https://cloud.comfy.org \ - Network accessline 31
--args '{"prompt": "a cat"}' \ --host https://cloud.comfy.org \ --output-dir ./outputs - Network accessline 32
--host https://cloud.comfy.org \ - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 66
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 107
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 259
# Build WS URL. Preserve any base-path components the user gave us # (e.g. http://example.com/comfyui → ws://example.com/comfyui/ws). - Network accessline 260
# (e.g. http://example.com/comfyui → ws://example.com/comfyui/ws). - Access to secretsline 266
[Credential-related source omitted] - Access to secretsline 267
[Credential-related source omitted] - Access to secretsline 576
[Credential-related source omitted] - Access to secretsline 637
[Credential-related source omitted] - Access to secretsline 639
[Credential-related source omitted] - Access to secretsline 640
[Credential-related source omitted] - Access to secretsline 644
[Credential-related source omitted] - Access to secretsline 656
[Credential-related source omitted]
optional-skills/creative/comfyui/scripts/ws_monitor.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 17
# Cloud — watch a specific prompt_id python3 ws_monitor.py --host https://cloud.comfy.org \ - Network accessline 18
python3 ws_monitor.py --host https://cloud.comfy.org \ - Access to secretsline 39
[Credential-related source omitted] - Access to secretsline 114
[Credential-related source omitted] - Access to secretsline 134
[Credential-related source omitted] - Network accessline 139
parsed = urlparse(args.host if "://" in args.host else f"http://{args.host}") - Access to secretsline 144
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted]
optional-skills/creative/comfyui/tests/README.md
optional-skills/creative/comfyui/tests/conftest.py
optional-skills/creative/comfyui/tests/pytest.ini
- Access to secretsline 3
[Credential-related source omitted]
optional-skills/creative/comfyui/tests/test_check_deps.py
- Executable scriptline 1
Executable file
optional-skills/creative/comfyui/tests/test_cloud_integration.py
- Executable scriptline 1
Executable file - Access to secretsline 3
[Credential-related source omitted] - Network accessline 18
class TestCloudEndpointsLive: def test_system_stats_reachable(self, cloud_key): url = resolve_url("https://cloud.comfy.org", "/system_stats") - Network accessline 19
[Credential-related source omitted] - Network accessline 20
url = resolve_url("https://cloud.comfy.org", "/system_stats") - Network accessline 28
url = resolve_url("https://cloud.comfy.org", "/models/checkpoints") - Network accessline 33
def test_models_endpoint_returns_dicts(self, cloud_key): url = resolve_url("https://cloud.comfy.org", "/models/checkpoints") - Network accessline 34
url = resolve_url("https://cloud.comfy.org", "/models/checkpoints") - Network accessline 48
url = resolve_url("https://cloud.comfy.org", "/history/some-fake-id") - Network accessline 53
url = resolve_url("https://cloud.comfy.org", "/object_info") - Network accessline 63
[Credential-related source omitted] - Network accessline 65
[Credential-related source omitted] - Access to secretsline 65
[Credential-related source omitted] - Network accessline 73
[Credential-related source omitted] - Network accessline 74
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Network accessline 86
def test_health_check_passes(self, cloud_key, capsys): from health_check import main as health_main health_main(["--host", "https://cloud.comfy.org", "--api-key", cloud_key]) - Network accessline 87
from health_check import main as health_main health_main(["--host", "https://cloud.comfy.org", "--api-key", cloud_key]) captured = capsys.readouterr() - Network accessline 88
health_main(["--host", "https://cloud.comfy.org", "--api-key", cloud_key])
optional-skills/creative/comfyui/tests/test_common.py
- Executable scriptline 1
Executable file - Network accessline 32
class TestCloudDetection: def test_cloud_host_exact(self): assert is_cloud_host("https://cloud.comfy.org") is True - Network accessline 33
def test_cloud_host_exact(self): assert is_cloud_host("https://cloud.comfy.org") is True assert is_cloud_host("https://cloud.comfy.org/foo/bar") is True - Network accessline 34
assert is_cloud_host("https://cloud.comfy.org") is True - Network accessline 35
assert is_cloud_host("https://cloud.comfy.org/foo/bar") is True - Network accessline 37
def test_cloud_host_subdomain(self): assert is_cloud_host("https://staging.cloud.comfy.org") is True - Network accessline 38
assert is_cloud_host("https://staging.cloud.comfy.org") is True - Network accessline 39
assert is_cloud_host("https://api.cloud.comfy.org") is True - Network accessline 41
def test_local_not_cloud(self): assert is_cloud_host("http://127.0.0.1:8188") is False - Network accessline 42
assert is_cloud_host("http://127.0.0.1:8188") is False - Network accessline 43
assert is_cloud_host("http://localhost:8188") is False - Network accessline 44
assert is_cloud_host("http://my-server.local:8188") is False - Network accessline 70
class TestResolveURL: def test_local_no_prefix(self): assert resolve_url("http://127.0.0.1:8188", "/prompt") == "http://127.0.0.1:8188/prompt" - Network accessline 72
assert resolve_url("http://127.0.0.1:8188", "/prompt") == "http://127.0.0.1:8188/prompt" - Network accessline 74
def test_cloud_adds_api_prefix(self): assert resolve_url("https://cloud.comfy.org", "/prompt") == "https://cloud.comfy.org/api/prompt" - Network accessline 75
assert resolve_url("https://cloud.comfy.org", "/prompt") == "https://cloud.comfy.org/api/prompt" - Network accessline 77
def test_cloud_history_renamed(self): assert resolve_url("https://cloud.comfy.org", "/history/abc") == "https://cloud.comfy.org/api/history_v2/abc" - Network accessline 78
assert resolve_url("https://cloud.comfy.org", "/history/abc") == "https://cloud.comfy.org/api/history_v2/abc" - Network accessline 80
def test_cloud_models_renamed(self): assert resolve_url("https://cloud.comfy.org", "/models/loras") == "https://cloud.comfy.org/api/experiment/models/loras" - Network accessline 81
assert resolve_url("https://cloud.comfy.org", "/models/loras") == "https://cloud.comfy.org/api/experiment/models/loras" - Network accessline 85
assert resolve_url("https://cloud.comfy.org", "/api/prompt") == "https://cloud.comfy.org/api/prompt" - Network accessline 87
def test_trailing_slash_stripped(self): assert resolve_url("http://127.0.0.1:8188/", "/prompt") == "http://127.0.0.1:8188/prompt" - Network accessline 88
assert resolve_url("http://127.0.0.1:8188/", "/prompt") == "http://127.0.0.1:8188/prompt" - Network accessline 389
import requests s = self._build_session() prep = requests.PreparedRequest() - Network accessline 390
s = self._build_session() prep = requests.PreparedRequest() prep.prepare(method="GET", url="https://other.example.com/file", - Network accessline 391
prep = requests.PreparedRequest() - Network accessline 392
[Credential-related source omitted] - Network accessline 393
[Credential-related source omitted] - Network accessline 394
resp = requests.Response() - Network accessline 395
orig = requests.PreparedRequest() - Network accessline 396
orig.prepare(method="GET", url="https://cloud.comfy.org/api/view", headers={}) - Network accessline 403
import requests s = self._build_session() prep = requests.PreparedRequest() - Network accessline 404
s = self._build_session() prep = requests.PreparedRequest() prep.prepare(method="GET", url="https://cloud.comfy.org/foo", - Network accessline 405
prep = requests.PreparedRequest() - Network accessline 406
[Credential-related source omitted] - Network accessline 407
[Credential-related source omitted] - Network accessline 408
resp = requests.Response() - Network accessline 409
orig = requests.PreparedRequest() - Network accessline 410
orig.prepare(method="GET", url="https://cloud.comfy.org/bar", headers={}) - Network accessline 416
import requests s = self._build_session() prep = requests.PreparedRequest() - Network accessline 417
s = self._build_session() prep = requests.PreparedRequest() prep.prepare(method="GET", url="https://other.example.com/x", - Network accessline 418
prep = requests.PreparedRequest() - Network accessline 419
prep.prepare(method="GET", url="https://other.example.com/x", headers={"Cookie": "session=secret"}) resp = requests.Response() - Network accessline 420
headers={"Cookie": "session=secret"}) resp = requests.Response() orig = requests.PreparedRequest() - Network accessline 421
resp = requests.Response() - Network accessline 422
orig = requests.PreparedRequest() - Network accessline 423
orig.prepare(method="GET", url="https://cloud.comfy.org/foo", headers={})
optional-skills/creative/comfyui/tests/test_extract_schema.py
- Executable scriptline 1
Executable file
optional-skills/creative/comfyui/tests/test_run_workflow.py
- Executable scriptline 1
Executable file - Network accessline 178
r = ComfyRunner() assert r.is_cloud is False assert r.host == "http://127.0.0.1:8188" - Network accessline 180
assert r.host == "http://127.0.0.1:8188" - Network accessline 182
[Credential-related source omitted] - Network accessline 183
[Credential-related source omitted] - Access to secretsline 183
[Credential-related source omitted] - Network accessline 187
[Credential-related source omitted] - Network accessline 188
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Network accessline 191
[Credential-related source omitted] - Network accessline 192
[Credential-related source omitted] - Access to secretsline 192
[Credential-related source omitted] - Network accessline 198
r = ComfyRunner() url = r._url("/prompt") assert url == "http://127.0.0.1:8188/prompt" - Network accessline 200
assert url == "http://127.0.0.1:8188/prompt" - Network accessline 202
[Credential-related source omitted] - Network accessline 203
[Credential-related source omitted] - Access to secretsline 203
[Credential-related source omitted] - Network accessline 205
assert url == "https://cloud.comfy.org/api/prompt" - Network accessline 207
[Credential-related source omitted] - Network accessline 208
[Credential-related source omitted] - Access to secretsline 208
[Credential-related source omitted] - Network accessline 210
assert url == "https://cloud.comfy.org/api/history_v2/abc-123"
optional-skills/creative/comfyui/workflows/README.md
- Access to secretsline 35
[Credential-related source omitted] - Network accessline 36
python3 ../scripts/run_workflow.py --workflow flux_dev_txt2img.json --args '{"prompt": "a fox in a misty forest"}' --host https://cloud.comfy.org - Network accessline 37
--workflow flux_dev_txt2img.json \ --args '{"prompt": "a fox in a misty forest"}' \ --host https://cloud.comfy.org \ - Network accessline 38
--args '{"prompt": "a fox in a misty forest"}' \ --host https://cloud.comfy.org \ --output-dir ./out - Network accessline 39
--host https://cloud.comfy.org \ - Network accessline 77
python3 ../scripts/run_workflow.py --workflow sd15_txt2img.json --args '{"ckpt_name": "v1-5-pruned-emaonly-fp16.safetensors", "prompt": "..."}' --host https://cloud.comfy.org - Network accessline 78
--workflow sd15_txt2img.json \ --args '{"ckpt_name": "v1-5-pruned-emaonly-fp16.safetensors", "prompt": "..."}' \ --host https://cloud.comfy.org - Network accessline 80
--host https://cloud.comfy.org - Network accessline 83
The `ckpt_name`, `vae_name`, `lora_name`, `unet_name`, etc. are all exposed as controllable parameters by `extract_schema.py` — discover what's installed with `comfy model list` (local) or `curl /api/experiment/models/checkpoints` - Network accessline 84
as controllable parameters by `extract_schema.py` — discover what's installed with `comfy model list` (local) or `curl /api/experiment/models/checkpoints` (cloud). - Network accessline 85
with `comfy model list` (local) or `curl /api/experiment/models/checkpoints`
optional-skills/creative/concept-diagrams/SKILL.md
- Network accessline 167
<svg width="100%" viewBox="0 0 680 {HEIGHT}" xmlns="http://www.w3.org/2000/svg"> - Network accessline 304
import http.server, socketserver with socketserver.TCPServer(('127.0.0.1', 0), http.server.SimpleHTTPRequestHandler) as s: print(f'Serving at http://127.0.0.1:{s.server_address[1]}/') - Network accessline 305
with socketserver.TCPServer(('127.0.0.1', 0), http.server.SimpleHTTPRequestHandler) as s: print(f'Serving at http://127.0.0.1:{s.server_address[1]}/') s.serve_forever() - Network accessline 306
print(f'Serving at http://127.0.0.1:{s.server_address[1]}/')
optional-skills/creative/concept-diagrams/examples/automated-password-reset-flow.md
- Network accessline 19
<svg width="100%" viewBox="0 0 680 960" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/autonomous-llm-research-agent-flow.md
- Network accessline 20
<svg width="100%" viewBox="0 0 680 920" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/commercial-aircraft-structure.md
- Network accessline 18
<svg width="100%" viewBox="0 0 680 400" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/feature-film-production-pipeline.md
- Network accessline 16
<svg width="100%" viewBox="0 0 680 780" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/hospital-emergency-department-flow.md
- Network accessline 16
<svg width="100%" viewBox="0 0 680 620" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/place-order-uml-sequence.md
- Network accessline 21
<svg width="100%" viewBox="0 0 680 648" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/concept-diagrams/examples/wind-turbine-structure.md
- Network accessline 21
<svg width="100%" viewBox="0 0 680 920" xmlns="http://www.w3.org/2000/svg">
optional-skills/creative/creative-ideation/SKILL.md
- Network accessline 177
Constraint-dispatch core adapted from [wttdotm.com/prompts.html](https://wttdotm.com/prompts.html). Methods drawn from primary sources cited in each method file.
optional-skills/creative/creative-ideation/references/full-prompt-library.md
- Network accessline 3
Constraint-dispatch library — voice and approach inspired by [wttdotm.com/prompts.html](https://wttdotm.com/prompts.html). Adapted and expanded. - Network accessline 180
Constraint approach inspired by [wttdotm.com/prompts.html](https://wttdotm.com/prompts.html). Original v1 of this library was substantially adapted from there. This expanded version groups constraints by domain affinity for use with the rou
optional-skills/creative/design-md/SKILL.md
- Network accessline 217
- Repo: https://github.com/google-labs-code/design.md (Apache-2.0)
optional-skills/creative/draw-your-font/SKILL.md
- Network accessline 3
description: "Turn a handwriting photo into an installable TTF font." version: 0.1.0 author: Danilo Znamerovszkij (https://github.com/danilo-znamerovszkij/draw-your-font), ported by Hermes Agent - Network accessline 4
version: 0.1.0 author: Danilo Znamerovszkij (https://github.com/danilo-znamerovszkij/draw-your-font), ported by Hermes Agent license: MIT - Network accessline 5
author: Danilo Znamerovszkij (https://github.com/danilo-znamerovszkij/draw-your-font), ported by Hermes Agent - Network accessline 11
tags: [font, handwriting, typography, ttf, woff, vision, creative] category: creative homepage: https://github.com/danilo-znamerovszkij/draw-your-font - Network accessline 12
category: creative homepage: https://github.com/danilo-znamerovszkij/draw-your-font related_skills: [pixel-art] - Network accessline 13
homepage: https://github.com/danilo-znamerovszkij/draw-your-font
optional-skills/creative/dream-loop/SKILL.md
- Network accessline 12
category: creative related_skills: [p5js, claude-design, manim-video] upstream: https://github.com/achimala/dream-loop - Network accessline 13
related_skills: [p5js, claude-design, manim-video] upstream: https://github.com/achimala/dream-loop --- - Network accessline 14
upstream: https://github.com/achimala/dream-loop - Network accessline 139
Serve the build (e.g. `python3 -m http.server` in the build dir), then via `browser_exec`: `new_tab('http://localhost:8000')`, `wait_for_load()`, allow - Network accessline 140
`browser_exec`: `new_tab('http://localhost:8000')`, `wait_for_load()`, allow - Network accessline 280
--- Adapted from [dream-loop](https://github.com/achimala/dream-loop) by Anshu - Network accessline 281
Adapted from [dream-loop](https://github.com/achimala/dream-loop) by Anshu
optional-skills/creative/excalidraw/SKILL.md
- Network accessline 18
Create diagrams by writing standard Excalidraw element JSON and saving as `.excalidraw` files. These files can be drag-and-dropped onto [excalidraw.com](https://excalidraw.com) for viewing and editing. No accounts, no API keys, no rendering
optional-skills/creative/excalidraw/scripts/upload.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 15
Example: python upload.py ~/diagrams/architecture.excalidraw # prints: https://excalidraw.com/#json=abc123,encryptionKeyHere - Network accessline 16
python upload.py ~/diagrams/architecture.excalidraw # prints: https://excalidraw.com/#json=abc123,encryptionKeyHere """ - Network accessline 17
# prints: https://excalidraw.com/#json=abc123,encryptionKeyHere - Network accessline 36
UPLOAD_URL = "https://json.excalidraw.com/api/v2/post/" - Network accessline 88
req = urllib.request.Request(UPLOAD_URL, data=payload, method="POST") - Network accessline 89
with urllib.request.urlopen(req, timeout=30) as resp: - Network accessline 101
return f"https://excalidraw.com/#json={file_id},{key_b64}"
optional-skills/creative/heartmula/SKILL.md
- Network accessline 39
cd ~/ # or desired directory git clone https://github.com/HeartMuLa/heartlib.git - Network accessline 40
git clone https://github.com/HeartMuLa/heartlib.git - Network accessline 101
- `torchtune` may report version `0.4.0+cpu` — this is just package metadata, it still uses CUDA via PyTorch - To verify GPU is being used, look for "CUDA memory" lines in the output (e.g. "CUDA memory before unloading: 6.20 GB") - **No GPU - Network accessline 103
- **No GPU?** You can run on CPU with `--mula_device cpu --codec_device cpu`, but expect generation to be **extremely slow** (potentially 30-60+ minutes for a single song vs ~4 minutes on GPU). CPU mode also requires significant RAM (~12GB+ - Network accessline 169
## Links - Repo: https://github.com/HeartMuLa/heartlib - Network accessline 170
- Repo: https://github.com/HeartMuLa/heartlib - Network accessline 171
- Models: https://huggingface.co/HeartMuLa - Network accessline 172
- Paper: https://arxiv.org/abs/2601.10547
optional-skills/creative/hyperframes/SKILL.md
- Network accessline 172
- **`HeadlessExperimental.beginFrame' wasn't found`** — Chromium 147+ removed this protocol. Ensure you're on `hyperframes@>=0.4.2` (auto-detects and falls back to screenshot mode). Escape hatch: `export PRODUCER_FORCE_SCREENSHOT=true`. See
optional-skills/creative/hyperframes/references/cli.md
- Network accessline 34
npx hyperframes capture https://example.com # → captures/example.com/ - Network accessline 35
npx hyperframes capture https://stripe.com -o stripe-video # custom output dir - Network accessline 36
npx hyperframes capture https://example.com --json # machine-readable output - Network accessline 37
npx hyperframes capture https://example.com --skip-assets # skip images/SVGs - Network accessline 185
Popular catalog blocks: `flash-through-white` (shader transition), `instagram-follow` (social overlay), `data-chart` (animated chart), `lower-third` (talking-head overlay). See [hyperframes.heygen.com/catalog](https://hyperframes.heygen.com
optional-skills/creative/hyperframes/references/composition.md
- Network accessline 27
<script src="https://cdn.jsdelivr.net/npm/gsap@3.14.2/dist/gsap.min.js"></script>
optional-skills/creative/hyperframes/references/gsap.md
- Network accessline 6
<script src="https://cdn.jsdelivr.net/npm/gsap@3.14.2/dist/gsap.min.js"></script>
optional-skills/creative/hyperframes/references/troubleshooting.md
- Network accessline 13
**Cause:** Chromium 147+ removed the `HeadlessExperimental.beginFrame` CDP command. This affected sandbox environments (e.g., OpenClaw, some containerized agent hosts) that ship modern Chromium as the system browser. See [hyperframes#294](h - Network accessline 23
`hyperframes >= 0.4.2` auto-detects whether the resolved browser supports `beginFrame` (checks for `chrome-headless-shell` in the binary path) and falls back to screenshot capture mode when it doesn't. Commit [`4c72ba4`](https://github.com/ - Dangerous shell operationline 61
| Ubuntu / Debian | `sudo apt-get install -y ffmpeg` | - Dangerous shell operationline 62
| Fedora / RHEL | `sudo dnf install -y ffmpeg` | - Dangerous shell operationline 63
| Arch | `sudo pacman -S ffmpeg` | - Network accessline 73
- **nvm:** `nvm install 22 && nvm use 22` - **Homebrew (macOS):** `brew install node@22 && brew link --overwrite node@22` - **apt:** follow [nodesource](https://github.com/nodesource/distributions) for Node 22 LTS. - Network accessline 75
- **apt:** follow [nodesource](https://github.com/nodesource/distributions) for Node 22 LTS. - Network accessline 160
Include `npx hyperframes info` output + the full error log. File at [github.com/heygen-com/hyperframes](https://github.com/heygen-com/hyperframes/issues).
optional-skills/creative/hyperframes/references/website-to-video.md
- Network accessline 10
npx hyperframes capture https://example.com -o example-video
optional-skills/creative/hyperframes/scripts/setup.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 6
# # Pins `hyperframes@>=0.4.2` so the OpenClaw/Chromium-147 fix from # https://github.com/heygen-com/hyperframes/issues/294 (commit 4c72ba4) - Network accessline 7
# Pins `hyperframes@>=0.4.2` so the OpenClaw/Chromium-147 fix from # https://github.com/heygen-com/hyperframes/issues/294 (commit 4c72ba4) # is always present — the engine auto-detects `HeadlessExperimental.beginFrame` - Network accessline 8
# https://github.com/heygen-com/hyperframes/issues/294 (commit 4c72ba4) - Dangerous shell operationline 48
Linux*) echo " sudo apt-get install -y ffmpeg # Debian/Ubuntu" - Dangerous shell operationline 49
echo " sudo dnf install -y ffmpeg # Fedora/RHEL";; - Network accessline 50
Darwin*) echo " brew install ffmpeg";; MINGW*|MSYS*|CYGWIN*) echo " winget install Gyan.FFmpeg";; *) echo " See https://ffmpeg.org/download.html";; - Network accessline 51
MINGW*|MSYS*|CYGWIN*) echo " winget install Gyan.FFmpeg";; *) echo " See https://ffmpeg.org/download.html";; esac - Network accessline 52
*) echo " See https://ffmpeg.org/download.html";; - Dangerous shell operationline 82
echo " Try: sudo npm install -g hyperframes@latest" - Dangerous shell operationline 97
echo " Try 'npm install -g hyperframes@latest' or 'sudo npm install -g hyperframes@latest'."
optional-skills/creative/impeccable/SKILL.md
- Network accessline 20
> **Catalog stub.** This entry is maintained upstream at > [pbakaus/impeccable](https://github.com/pbakaus/impeccable): the project - Network accessline 21
> [pbakaus/impeccable](https://github.com/pbakaus/impeccable): the project - Network accessline 42
Full documentation: https://impeccable.style
optional-skills/creative/ip-as-logo/LICENSE
- Network accessline 1
ip-as-logo — ported from https://github.com/s1dashu/ip-as-logo-skill - Network accessline 2
Upstream snapshot: commit b1bf517c54a407452cfaca98a54668cd052f8e63 (2026-08-20) Upstream author / copyright holder: s1dashu (https://github.com/s1dashu) The MIT license text below is reproduced verbatim from the upstream LICENSE. - Network accessline 3
Upstream author / copyright holder: s1dashu (https://github.com/s1dashu)
optional-skills/creative/ip-as-logo/SKILL.md
- Network accessline 3
description: "Design minimal cute IP mascot marks readable at 32px." version: 1.0.0 author: s1dashu (https://github.com/s1dashu, upstream s1dashu/ip-as-logo-skill), ported by Hermes Agent - Network accessline 4
version: 1.0.0 author: s1dashu (https://github.com/s1dashu, upstream s1dashu/ip-as-logo-skill), ported by Hermes Agent license: MIT - Network accessline 5
author: s1dashu (https://github.com/s1dashu, upstream s1dashu/ip-as-logo-skill), ported by Hermes Agent - Network accessline 10
tags: [logo, mascot, branding, ip-character, image-generation, creative] category: creative upstream: https://github.com/s1dashu/ip-as-logo-skill (pinned b1bf517c) - Network accessline 11
category: creative upstream: https://github.com/s1dashu/ip-as-logo-skill (pinned b1bf517c) related_skills: [pixel-art] - Network accessline 12
upstream: https://github.com/s1dashu/ip-as-logo-skill (pinned b1bf517c) - Network accessline 20
> **Hermes adaptation notes** (the rest of this document is the upstream > workflow, kept intact — snapshot of > [s1dashu/ip-as-logo-skill](https://github.com/s1dashu/ip-as-logo-skill) - Network accessline 21
> workflow, kept intact — snapshot of > [s1dashu/ip-as-logo-skill](https://github.com/s1dashu/ip-as-logo-skill) > commit [`b1bf517c`](https://github.com/s1dashu/ip-as-logo-skill/commit/b1bf517c54a407452cfaca98a54668cd052f8e63), - Network accessline 22
> [s1dashu/ip-as-logo-skill](https://github.com/s1dashu/ip-as-logo-skill) - Network accessline 23
> commit [`b1bf517c`](https://github.com/s1dashu/ip-as-logo-skill/commit/b1bf517c54a407452cfaca98a54668cd052f8e63),
optional-skills/creative/kanban-video-orchestrator/SKILL.md
- Network accessline 15
`--workspace dir:/abs/path` discipline are adapted from alt-glitch's original multi-agent video pipeline at https://github.com/NousResearch/kanban-video-pipeline. - Network accessline 16
original multi-agent video pipeline at https://github.com/NousResearch/kanban-video-pipeline. --- - Network accessline 17
https://github.com/NousResearch/kanban-video-pipeline.
optional-skills/creative/kanban-video-orchestrator/assets/setup.sh.tmpl
- Executable scriptline 1
#!/usr/bin/env bash - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted]
optional-skills/creative/kanban-video-orchestrator/references/kanban-setup.md
- Network accessline 12
> approach, SOUL.md-per-profile convention, and `--workspace dir:<path>` rule > are adapted from alt-glitch's original multi-agent video pipeline: > [NousResearch/kanban-video-pipeline](https://github.com/NousResearch/kanban-video-pipeline) - Network accessline 13
> are adapted from alt-glitch's original multi-agent video pipeline: > [NousResearch/kanban-video-pipeline](https://github.com/NousResearch/kanban-video-pipeline). > This skill generalizes those patterns across video styles and replaces the - Network accessline 14
> [NousResearch/kanban-video-pipeline](https://github.com/NousResearch/kanban-video-pipeline). - Access to secretsline 226
[Credential-related source omitted] - Access to secretsline 234
[Credential-related source omitted] - Access to secretsline 239
[Credential-related source omitted] - Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted]
optional-skills/creative/kanban-video-orchestrator/references/tool-matrix.md
- Access to secretsline 211
[Credential-related source omitted] - Access to secretsline 212
[Credential-related source omitted] - Access to secretsline 213
[Credential-related source omitted] - Access to secretsline 269
[Credential-related source omitted] - Access to secretsline 273
[Credential-related source omitted] - Access to secretsline 274
[Credential-related source omitted] - Access to secretsline 275
[Credential-related source omitted] - Access to secretsline 278
[Credential-related source omitted] - Access to secretsline 279
[Credential-related source omitted] - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 281
[Credential-related source omitted] - Access to secretsline 282
[Credential-related source omitted] - Access to secretsline 283
[Credential-related source omitted] - Access to secretsline 286
[Credential-related source omitted]
optional-skills/creative/kanban-video-orchestrator/scripts/bootstrap_pipeline.py
optional-skills/creative/kanban-video-orchestrator/scripts/monitor.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/creative/meme-generation/scripts/.gitignore
- Executable scriptline 1
Executable file
optional-skills/creative/meme-generation/scripts/generate_meme.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 32
TEMPLATES_FILE = SCRIPT_DIR / "templates.json" CACHE_DIR = SCRIPT_DIR / ".cache" IMGFLIP_API = "https://api.imgflip.com/get_memes" - Network accessline 33
CACHE_DIR = SCRIPT_DIR / ".cache" IMGFLIP_API = "https://api.imgflip.com/get_memes" IMGFLIP_CACHE_FILE = CACHE_DIR / "imgflip_memes.json" - Network accessline 34
IMGFLIP_API = "https://api.imgflip.com/get_memes" - Network accessline 44
return resp.content import urllib.request return urllib.request.urlopen(url, timeout=timeout).read() - Network accessline 46
return urllib.request.urlopen(url, timeout=timeout).read()
optional-skills/creative/meme-generation/scripts/templates.json
- Network accessline 2
"this-is-fine": { "name": "This is Fine", "url": "https://i.imgflip.com/wxica.jpg", - Network accessline 3
"name": "This is Fine", "url": "https://i.imgflip.com/wxica.jpg", "best_for": "chaos, denial, pretending things are okay", - Network accessline 4
"url": "https://i.imgflip.com/wxica.jpg", - Network accessline 11
"drake": { "name": "Drake Hotline Bling", "url": "https://i.imgflip.com/30b1gx.jpg", - Network accessline 12
"name": "Drake Hotline Bling", "url": "https://i.imgflip.com/30b1gx.jpg", "best_for": "rejecting one thing, preferring another", - Network accessline 13
"url": "https://i.imgflip.com/30b1gx.jpg", - Network accessline 20
"distracted-boyfriend": { "name": "Distracted Boyfriend", "url": "https://i.imgflip.com/1ur9b0.jpg", - Network accessline 21
"name": "Distracted Boyfriend", "url": "https://i.imgflip.com/1ur9b0.jpg", "best_for": "distraction, shifting priorities, temptation", - Network accessline 22
"url": "https://i.imgflip.com/1ur9b0.jpg", - Network accessline 30
"two-buttons": { "name": "Two Buttons", "url": "https://i.imgflip.com/1g8my4.jpg", - Network accessline 31
"name": "Two Buttons", "url": "https://i.imgflip.com/1g8my4.jpg", "best_for": "impossible choice, dilemma between two options", - Network accessline 32
"url": "https://i.imgflip.com/1g8my4.jpg", - Network accessline 40
"expanding-brain": { "name": "Expanding Brain", "url": "https://i.imgflip.com/1jwhww.jpg", - Network accessline 41
"name": "Expanding Brain", "url": "https://i.imgflip.com/1jwhww.jpg", "best_for": "escalating irony, increasingly absurd ideas", - Network accessline 42
"url": "https://i.imgflip.com/1jwhww.jpg", - Network accessline 51
"change-my-mind": { "name": "Change My Mind", "url": "https://i.imgflip.com/24y43o.jpg", - Network accessline 52
"name": "Change My Mind", "url": "https://i.imgflip.com/24y43o.jpg", "best_for": "strong or ironic opinion, controversial take", - Network accessline 53
"url": "https://i.imgflip.com/24y43o.jpg", - Network accessline 59
"woman-yelling-at-cat": { "name": "Woman Yelling at Cat", "url": "https://i.imgflip.com/345v97.jpg", - Network accessline 60
"name": "Woman Yelling at Cat", "url": "https://i.imgflip.com/345v97.jpg", "best_for": "argument, blame, misunderstanding", - Network accessline 61
"url": "https://i.imgflip.com/345v97.jpg", - Network accessline 68
"one-does-not-simply": { "name": "One Does Not Simply", "url": "https://i.imgflip.com/1bij.jpg", - Network accessline 69
"name": "One Does Not Simply", "url": "https://i.imgflip.com/1bij.jpg", "best_for": "something that sounds easy but is actually hard", - Network accessline 70
"url": "https://i.imgflip.com/1bij.jpg", - Network accessline 77
"grus-plan": { "name": "Gru's Plan", "url": "https://i.imgflip.com/26jxvs.jpg", - Network accessline 78
"name": "Gru's Plan", "url": "https://i.imgflip.com/26jxvs.jpg", "best_for": "a plan that backfires, unexpected consequence", - Network accessline 79
"url": "https://i.imgflip.com/26jxvs.jpg", - Network accessline 88
"batman-slapping-robin": { "name": "Batman Slapping Robin", "url": "https://i.imgflip.com/9ehk.jpg", - Network accessline 89
"name": "Batman Slapping Robin", "url": "https://i.imgflip.com/9ehk.jpg", "best_for": "shutting down a bad idea, correcting someone", - Network accessline 90
"url": "https://i.imgflip.com/9ehk.jpg",
optional-skills/creative/mono-color/SKILL.md
- Network accessline 12
category: creative related_skills: [baoyu-infographic, meme-generation, pixel-art] upstream: https://github.com/yanliudesign/mono-color-skill - Network accessline 13
related_skills: [baoyu-infographic, meme-generation, pixel-art] upstream: https://github.com/yanliudesign/mono-color-skill --- - Network accessline 14
upstream: https://github.com/yanliudesign/mono-color-skill
optional-skills/creative/pixel-art/ATTRIBUTION.md
- Network accessline 8
- Source: https://github.com/Synero/pixel-art-studio
optional-skills/creative/pixel-art/SKILL.md
- Network accessline 11
category: creative credits: - "Hardware palettes and animation loops ported from Synero/pixel-art-studio (MIT) — https://github.com/Synero/pixel-art-studio" - Network accessline 12
credits: - "Hardware palettes and animation loops ported from Synero/pixel-art-studio (MIT) — https://github.com/Synero/pixel-art-studio" --- - Network accessline 13
- "Hardware palettes and animation loops ported from Synero/pixel-art-studio (MIT) — https://github.com/Synero/pixel-art-studio" - Network accessline 218
Named hardware palettes and the procedural animation loops in `pixel_art_video.py` are ported from [pixel-art-studio](https://github.com/Synero/pixel-art-studio) - Network accessline 219
are ported from [pixel-art-studio](https://github.com/Synero/pixel-art-studio)
optional-skills/creative/pixel-art/scripts/__init__.py
- Executable scriptline 1
Executable file
optional-skills/creative/pixel-art/scripts/palettes.py
optional-skills/creative/pixel-art/scripts/pixel_art.py
- Executable scriptline 1
Executable file
optional-skills/creative/pixel-art/scripts/pixel_art_video.py
- Executable scriptline 1
Executable file - Network accessline 241
raise RuntimeError( "ffmpeg not found on PATH. Install via your package manager or " "download from https://ffmpeg.org/" - Network accessline 242
"ffmpeg not found on PATH. Install via your package manager or " "download from https://ffmpeg.org/" ) - Network accessline 243
"download from https://ffmpeg.org/"
optional-skills/creative/pretext/SKILL.md
- Network accessline 18
[`@chenglou/pretext`](https://github.com/chenglou/pretext) is a 15KB zero-dependency TypeScript library by Cheng Lou (React core, ReasonML, Midjourney) for **DOM-free multiline text measurement and layout**. It does one thing: given `(text, - Network accessline 68
layoutNextLineRange, materializeLineRange, // use-case 2b: streaming / variable width measureLineStats, walkLineRanges, // stats without string allocation } from "https://esm.sh/@chenglou/pretext@0.0.6"; - Network accessline 69
measureLineStats, walkLineRanges, // stats without string allocation } from "https://esm.sh/@chenglou/pretext@0.0.6"; </script> - Network accessline 70
} from "https://esm.sh/@chenglou/pretext@0.0.6"; - Network accessline 74
Pin the version. `@0.0.6` at time of writing — check [npm](https://www.npmjs.com/package/@chenglou/pretext) for the latest if demo behavior is off. - Network accessline 161
cd <dir-with-html> && python -m http.server 8765 # then open http://localhost:8765/<file>.html - Network accessline 162
# then open http://localhost:8765/<file>.html - Network accessline 210
Clone these for inspiration / patterns (all MIT-ish, linked from [pretext.cool](https://www.pretext.cool/)): - Network accessline 220
Official playground: [chenglou.me/pretext](https://chenglou.me/pretext/) — accordion, bubbles, dynamic-layout, editorial-engine, justification-comparison, masonry, markdown-chat, rich-note.
optional-skills/creative/pretext/references/patterns.md
- Network accessline 3
Copy-pasteable snippets for the most common pretext demo shapes. Each pattern is self-contained — drop into an HTML `<script type="module">` after importing from `https://esm.sh/@chenglou/pretext@0.0.6`.
optional-skills/creative/pretext/templates/donut-orbit.html
- Network accessline 5
<meta name="viewport" content="width=device-width,initial-scale=1,user-scalable=no" /> <title>NOUS · pretext</title> <link rel="preconnect" href="https://fonts.googleapis.com"> - Network accessline 6
<title>NOUS · pretext</title> <link rel="preconnect" href="https://fonts.googleapis.com"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> - Network accessline 7
<link rel="preconnect" href="https://fonts.googleapis.com"> - Network accessline 8
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> - Network accessline 9
<link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@400;500&display=block" rel="stylesheet"> - Network accessline 149
@font-face { font-family: "Mondwest"; src: url("https://esm.sh/@nous-research/ui@0.4.0/dist/fonts/Mondwest-Regular.woff2") format("woff2"); - Network accessline 150
font-family: "Mondwest"; src: url("https://esm.sh/@nous-research/ui@0.4.0/dist/fonts/Mondwest-Regular.woff2") format("woff2"); font-weight: 400; font-style: normal; font-display: block; - Network accessline 151
src: url("https://esm.sh/@nous-research/ui@0.4.0/dist/fonts/Mondwest-Regular.woff2") format("woff2"); - Network accessline 154
@font-face { font-family: "Geist Mono"; src: url("https://cdn.jsdelivr.net/npm/@fontsource/geist-mono@5.2.5/files/geist-mono-latin-400-normal.woff2") format("woff2"); - Network accessline 155
font-family: "Geist Mono"; src: url("https://cdn.jsdelivr.net/npm/@fontsource/geist-mono@5.2.5/files/geist-mono-latin-400-normal.woff2") format("woff2"); font-weight: 400; font-style: normal; font-display: block; - Network accessline 156
src: url("https://cdn.jsdelivr.net/npm/@fontsource/geist-mono@5.2.5/files/geist-mono-latin-400-normal.woff2") format("woff2"); - Network accessline 159
@font-face { font-family: "JetBrains Mono"; src: url("https://cdn.jsdelivr.net/npm/@fontsource/jetbrains-mono@5.2.5/files/jetbrains-mono-latin-400-normal.woff2") format("woff2"); - Network accessline 160
font-family: "JetBrains Mono"; src: url("https://cdn.jsdelivr.net/npm/@fontsource/jetbrains-mono@5.2.5/files/jetbrains-mono-latin-400-normal.woff2") format("woff2"); font-weight: 400; font-style: normal; font-display: block; - Network accessline 161
src: url("https://cdn.jsdelivr.net/npm/@fontsource/jetbrains-mono@5.2.5/files/jetbrains-mono-latin-400-normal.woff2") format("woff2"); - Network accessline 179
layoutNextLineRange, materializeLineRange, } from "https://esm.sh/@chenglou/pretext@0.0.6"; - Network accessline 180
materializeLineRange, } from "https://esm.sh/@chenglou/pretext@0.0.6"; import gsap from "https://esm.sh/gsap@3.12.5"; - Network accessline 181
} from "https://esm.sh/@chenglou/pretext@0.0.6"; - Network accessline 182
import gsap from "https://esm.sh/gsap@3.12.5"; - Network accessline 183
import GUI from "https://esm.sh/lil-gui@0.19.2"; - Network accessline 1450
resize(); const DS_CDN = "https://esm.sh/@nous-research/ui@0.4.0/dist/fonts"; - Network accessline 1451
const DS_CDN = "https://esm.sh/@nous-research/ui@0.4.0/dist/fonts";
optional-skills/creative/pretext/templates/hello-orb-flow.html
optional-skills/creative/simple-english/SKILL.md
- Network accessline 3
description: "Rewrite text to ASD-STE100 Simplified Technical English." version: 1.2.0 author: AminBlg (https://github.com/AminBlg/SimpleEnglish), ported by Hermes Agent - Network accessline 4
version: 1.2.0 author: AminBlg (https://github.com/AminBlg/SimpleEnglish), ported by Hermes Agent license: MIT - Network accessline 5
author: AminBlg (https://github.com/AminBlg/SimpleEnglish), ported by Hermes Agent - Network accessline 10
tags: [writing, documentation, ste, asd-ste100, technical-writing, editing, anti-ai-slop] category: creative homepage: https://github.com/AminBlg/SimpleEnglish - Network accessline 11
category: creative homepage: https://github.com/AminBlg/SimpleEnglish related_skills: [humanizer] - Network accessline 12
homepage: https://github.com/AminBlg/SimpleEnglish
optional-skills/creative/sketch/SKILL.md
- Network accessline 31
> **Note:** The upstream GSD project ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)) is **archived / no longer maintained** on GitHub. The npm package (`get-shit-done-cc`) still installs, but treat it as an archived - Network accessline 82
- Inline `<style>` — no build step, no external CSS - System fonts or one Google Font via `<link>` - Tailwind via CDN (`<script src="https://cdn.tailwindcss.com"></script>`) is fine - Network accessline 83
- System fonts or one Google Font via `<link>` - Tailwind via CDN (`<script src="https://cdn.tailwindcss.com"></script>`) is fine - Realistic fake content — actual sentences, actual names, not "Lorem ipsum" - Network accessline 84
- Tailwind via CDN (`<script src="https://cdn.tailwindcss.com"></script>`) is fine - Network accessline 224
Adapted from the GSD (Get Shit Done) project's `/gsd-sketch` workflow — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)). The upstream GSD repo is now **archived/unmaintained** on GitHub;
optional-skills/creative/system-atlas/SKILL.md
- Network accessline 11
category: creative related_skills: [architecture-diagram, excalidraw] upstream: https://github.com/inkboard/system-atlas (pinned f7005f2) - Network accessline 12
related_skills: [architecture-diagram, excalidraw] upstream: https://github.com/inkboard/system-atlas (pinned f7005f2) --- - Network accessline 13
upstream: https://github.com/inkboard/system-atlas (pinned f7005f2)
optional-skills/creative/system-atlas/assets/build.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/system-atlas/assets/data.example.mjs
- Executable scriptline 1
Executable file
optional-skills/creative/system-atlas/assets/template.html
- Network accessline 2
<meta charset="utf-8"> <title>__TITLE__</title> <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:ital,wght@0,400;0,500;0,600;1,400&display=swap"> - Network accessline 3
<title>__TITLE__</title> <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:ital,wght@0,400;0,500;0,600;1,400&display=swap"> <style> - Network accessline 4
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:ital,wght@0,400;0,500;0,600;1,400&display=swap"> - Network accessline 136
<nav id="index" aria-label="Structures"></nav> <div id="canvasWrap"> <svg id="svg" xmlns="http://www.w3.org/2000/svg"> - Network accessline 137
<div id="canvasWrap"> <svg id="svg" xmlns="http://www.w3.org/2000/svg"> <defs> - Network accessline 138
<svg id="svg" xmlns="http://www.w3.org/2000/svg"> - Network accessline 172
const byId = Object.fromEntries(NODES.map(n=>[n.id,n])); const center = n => [n.gx+n.w/2, n.gy+n.d/2]; const NS='http://www.w3.org/2000/svg'; - Network accessline 173
const center = n => [n.gx+n.w/2, n.gy+n.d/2]; const NS='http://www.w3.org/2000/svg'; const el=(t,a={},...kids)=>{const e=document.createElementNS(NS,t);for(const k in a)e.setAttribute(k,a[k]);kids.forEach(k=>e.appendChild(k));return e;}; - Network accessline 174
const NS='http://www.w3.org/2000/svg';
optional-skills/creative/tldraw-offline/SKILL.md
- Network accessline 38
- **tldraw offline installed and running**, with a document open. Releases: https://github.com/tldraw/tldraw-offline/releases/latest (macOS DMG, Windows - Network accessline 39
https://github.com/tldraw/tldraw-offline/releases/latest (macOS DMG, Windows - Network accessline 66
BASE=http://localhost:7236 - Network accessline 67
[Credential-related source omitted] - Network accessline 68
[Credential-related source omitted] - Dangerous shell operationline 69
[Credential-related source omitted] - Network accessline 69
DOC=$(curl -s "$BASE/api/search" -X POST -H 'content-type: application/json' \ - Network accessline 71
-d '{"code":"return (await api.getFocusedDoc()).id"}' | python -c "import sys,json;print(json.load(sys.stdin)['result'])") # run code with the live `editor` + `helpers` in scope curl -s "$BASE/api/doc/$DOC/exec" -X POST -H 'content-type: ap - Network accessline 72
[Credential-related source omitted] - Network accessline 73
curl -s "$BASE/api/doc/$DOC/exec" -X POST -H 'content-type: application/json' \ - Network accessline 82
# get the live script file path for the doc curl -s "$BASE/api/doc/$DOC/script-workspace" -X POST \ - Network accessline 83
curl -s "$BASE/api/doc/$DOC/script-workspace" -X POST \ - Network accessline 85
[Credential-related source omitted] - Network accessline 87
[Credential-related source omitted]
optional-skills/creative/tldraw-offline/scripts/counter.js
- Executable scriptline 1
Executable file
optional-skills/creative/tldraw-offline/scripts/main.js
- Executable scriptline 1
Executable file
optional-skills/creative/tldraw-offline/scripts/validate_shapes.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/creative/unreal-mcp/SKILL.md
- Network accessline 55
server connects but the agent has nothing to call. 4. **Edit > Editor Preferences > General > Model Context Protocol** — enable **Auto Start Server**. Default bind is `http://127.0.0.1:8000/mcp` - Network accessline 56
4. **Edit > Editor Preferences > General > Model Context Protocol** — enable **Auto Start Server**. Default bind is `http://127.0.0.1:8000/mcp` (port/path configurable in the same panel; server name is `unreal-mcp`). - Network accessline 57
**Auto Start Server**. Default bind is `http://127.0.0.1:8000/mcp` - Network accessline 66
This writes the `mcp_servers.unreal-engine` HTTP entry pointing at `http://127.0.0.1:8000/mcp` and probes the live server for its tools. Run it - Network accessline 67
`http://127.0.0.1:8000/mcp` and probes the live server for its tools. Run it
optional-skills/creative/unreal-mcp/references/pitfalls.md
- Dangerous shell operationline 32
it: `sudo xcode-select -s /path/to/Xcode.app`. Verify with - Network accessline 41
Server Port Number in Editor Preferences > Model Context Protocol AND the `url` in `~/.hermes/config.yaml` (`mcp_servers.unreal-engine`), then restart both sides. Verify: `curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:8000/mcp` - Network accessline 42
`url` in `~/.hermes/config.yaml` (`mcp_servers.unreal-engine`), then restart both sides. Verify: `curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:8000/mcp` (non-000 means something is listening; whether it's Unreal is a different - Network accessline 43
both sides. Verify: `curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:8000/mcp`
optional-skills/creative/unreal-mcp/references/tool-surface.md
- Network accessline 11
The **Unreal MCP** plugin hosts the HTTP server inside the editor process (default `http://127.0.0.1:8000/mcp`, loopback-only, no auth, HTTP + SSE - Network accessline 12
(default `http://127.0.0.1:8000/mcp`, loopback-only, no auth, HTTP + SSE - Network accessline 272
console. - **MCP Inspector** (`npx @modelcontextprotocol/inspector`, point at `http://127.0.0.1:8000/mcp`, transport "Streamable HTTP") lists every - Network accessline 273
- **MCP Inspector** (`npx @modelcontextprotocol/inspector`, point at `http://127.0.0.1:8000/mcp`, transport "Streamable HTTP") lists every advertised tool with schemas and offers form-based invocation — isolates - Network accessline 274
`http://127.0.0.1:8000/mcp`, transport "Streamable HTTP") lists every
optional-skills/data-science/jupyter-notebook/SKILL.md
optional-skills/devops/actual-setup/SKILL.md
- Network accessline 16
Sets up [actual.inc](https://actual.inc) (Actual Computer) as a Hermes inference - Network accessline 17
provider. Actual turns the user's own hardware into a private inference cluster and exposes an OpenAI-compatible API two ways: a hosted end-to-end-encrypted relay at `https://api.actual.inc` (authenticated with an `ac_` key), and a local - Network accessline 18
and exposes an OpenAI-compatible API two ways: a hosted end-to-end-encrypted relay at `https://api.actual.inc` (authenticated with an `ac_` key), and a local on-device daemon at `http://127.0.0.1:8080` (no auth on loopback). This skill - Network accessline 19
relay at `https://api.actual.inc` (authenticated with an `ac_` key), and a local - Network accessline 20
on-device daemon at `http://127.0.0.1:8080` (no auth on loopback). This skill - Network accessline 37
Responses transport, and local no-auth automatically. - Relay mode: an Actual account and an `ac_` inference key from https://actual.inc/user/keys. - Network accessline 38
- Relay mode: an Actual account and an `ac_` inference key from https://actual.inc/user/keys. - Local mode: the user has installed the daemon - Network accessline 39
https://actual.inc/user/keys. - Local mode: the user has installed the daemon (`curl -fsSL "https://actual.inc/install" | bash`) and completed device - Network accessline 40
- Local mode: the user has installed the daemon (`curl -fsSL "https://actual.inc/install" | bash`) and completed device authorization by running `actual` once and opening the printed - Dangerous shell operationline 41
(`curl -fsSL "https://actual.inc/install" | bash`) and completed device - Network accessline 41
(`curl -fsSL "https://actual.inc/install" | bash`) and completed device - Network accessline 42
authorization by running `actual` once and opening the printed `https://actual.inc/device?code=...` URL in a browser. Relay that URL to the user and WAIT — never invent an email or authorize on their behalf. Codes - Network accessline 43
`https://actual.inc/device?code=...` URL in a browser. Relay that URL to the - Access to secretsline 52
[Credential-related source omitted] - Network accessline 55
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Network accessline 78
3. Point Hermes at the daemon. `ACTUAL_BASE_URL` with a loopback host flips the built-in provider into local no-auth mode automatically — no key needed: append `ACTUAL_BASE_URL=http://127.0.0.1:8080` to `~/.hermes/.env`, then: - Network accessline 80
append `ACTUAL_BASE_URL=http://127.0.0.1:8080` to `~/.hermes/.env`, then: - Network accessline 92
| Thing | Value | |---|---| | Hosted relay | `https://api.actual.inc/v1` (normalized from bare host automatically) | - Network accessline 93
|---|---| | Hosted relay | `https://api.actual.inc/v1` (normalized from bare host automatically) | | Local daemon | `http://127.0.0.1:8080/v1` (no auth on loopback) | - Network accessline 94
| Hosted relay | `https://api.actual.inc/v1` (normalized from bare host automatically) | - Network accessline 95
| Local daemon | `http://127.0.0.1:8080/v1` (no auth on loopback) | - Access to secretsline 96
[Credential-related source omitted]
optional-skills/devops/actual-setup/references/opencode.md
- Network accessline 34
{ "$schema": "https://opencode.ai/config.json", - Network accessline 35
"$schema": "https://opencode.ai/config.json", - Network accessline 39
"name": "Actual (GLM cluster)", "options": { "baseURL": "https://api.actual.inc/v1", - Network accessline 40
"options": { "baseURL": "https://api.actual.inc/v1", "headers": { - Network accessline 41
"baseURL": "https://api.actual.inc/v1", - Network accessline 60
[Credential-related source omitted] - Network accessline 61
[Credential-related source omitted] - Network accessline 62
[Credential-related source omitted]
optional-skills/devops/docker-management/SKILL.md
- Executable scriptline 1
Executable file - Dangerous shell operationline 46
| Stop + remove | `docker stop NAME && docker rm NAME` | - Dangerous shell operationline 103
docker rm NAME # remove stopped container - Dangerous shell operationline 104
docker rm -f NAME # force remove running container - Dangerous shell operationline 212
docker volume rm mydata # remove (fails if in use) - Dangerous shell operationline 221
docker network rm mynet # remove network - Network accessline 265
- **Container started?** → `docker ps` (check status is "Up") - **Logs clean?** → `docker logs --tail 20 NAME` (no errors) - **Port accessible?** → `curl -s http://localhost:PORT` or `docker port NAME` - Network accessline 266
- **Logs clean?** → `docker logs --tail 20 NAME` (no errors) - **Port accessible?** → `curl -s http://localhost:PORT` or `docker port NAME` - **Image built?** → `docker images | grep TAG` - Network accessline 267
- **Port accessible?** → `curl -s http://localhost:PORT` or `docker port NAME`
optional-skills/devops/hermes-s6-container-supervision/SKILL.md
- Access to secretsline 37
[Credential-related source omitted] - Network accessline 82
1. **cont-init.d scripts receive no CMD args** — so the stage2 hook can't parse `docker run <image> chat -q "hi"` to set `HERMES_ARGS` for a service `run` script to consume. 2. **`/run/s6/basedir/bin/halt` does NOT propagate the exit code** - Network accessline 83
2. **`/run/s6/basedir/bin/halt` does NOT propagate the exit code** written to `/run/s6-linux-init-container-results/exitcode`. Containers always exit 143 (SIGTERM) regardless. Confirmed by skarnet (s6 author) in [issue #477](https://github.
optional-skills/devops/inference-sh-cli/SKILL.md
optional-skills/devops/inference-sh-cli/references/app-discovery.md
- Network accessline 110
- [Browsing the Grid](https://inference.sh/docs/apps/browsing-grid) - Visual app browsing - Network accessline 111
- [Apps Overview](https://inference.sh/docs/apps/overview) - Understanding apps - Network accessline 112
- [Running Apps](https://inference.sh/docs/apps/running) - How to run apps
optional-skills/devops/inference-sh-cli/references/authentication.md
- Dangerous shell operationline 6
curl -fsSL https://cli.inference.sh | sh - Network accessline 6
curl -fsSL https://cli.inference.sh | sh - Access to secretsline 30
[Credential-related source omitted] - Dangerous shell operationline 44
curl -fsSL https://cli.inference.sh | sh - Network accessline 44
curl -fsSL https://cli.inference.sh | sh - Access to secretsline 53
[Credential-related source omitted] - Network accessline 57
- [CLI Setup](https://inference.sh/docs/extend/cli-setup) - Complete CLI installation guide - Network accessline 58
- [API Authentication](https://inference.sh/docs/api/authentication) - API key management - Network accessline 59
- [Secrets](https://inference.sh/docs/secrets/overview) - Managing credentials
optional-skills/devops/inference-sh-cli/references/cli-reference.md
- Dangerous shell operationline 6
curl -fsSL https://cli.inference.sh | sh - Network accessline 6
curl -fsSL https://cli.inference.sh | sh - Access to secretsline 72
[Credential-related source omitted] - Network accessline 101
- [CLI Setup](https://inference.sh/docs/extend/cli-setup) - Complete CLI installation guide - Network accessline 102
- [Running Apps](https://inference.sh/docs/apps/running) - How to run apps via CLI - Network accessline 103
- [Creating an App](https://inference.sh/docs/extend/creating-app) - Build your own apps - Network accessline 104
- [Deploying](https://inference.sh/docs/extend/deploying) - Deploy apps to the cloud
optional-skills/devops/inference-sh-cli/references/running-apps.md
- Network accessline 150
"images": [ { "url": "https://cloud.inference.sh/...", - Network accessline 151
{ "url": "https://cloud.inference.sh/...", "content_type": "image/png" - Network accessline 152
"url": "https://cloud.inference.sh/...", - Network accessline 169
- [Running Apps](https://inference.sh/docs/apps/running) - Complete running apps guide - Network accessline 170
- [Streaming Results](https://inference.sh/docs/api/sdk/streaming) - Real-time progress updates - Network accessline 171
- [Setup Parameters](https://inference.sh/docs/apps/setup-parameters) - Configuring app inputs
optional-skills/devops/pinggy-tunnel/SKILL.md
- Access to secretsline 68
[Credential-related source omitted] - Network accessline 78
curl -sI http://127.0.0.1:8000/ | head -1 - Network accessline 105
sleep 4 grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/pinggy-8000.log | head -1 - Network accessline 106
grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/pinggy-8000.log | head -1 - Network accessline 112
You are not authenticated. Your tunnel will expire in 60 minutes. http://yqycl-98-162-69-48.a.free.pinggy.link - Network accessline 113
Your tunnel will expire in 60 minutes. http://yqycl-98-162-69-48.a.free.pinggy.link https://yqycl-98-162-69-48.a.free.pinggy.link - Network accessline 114
http://yqycl-98-162-69-48.a.free.pinggy.link - Network accessline 115
https://yqycl-98-162-69-48.a.free.pinggy.link - Network accessline 118
Hand the `https://...pinggy.link` URL to the user. - Network accessline 123
curl -sI https://<the-url>/ | head -3 - Network accessline 165
Then open `http://localhost:4300` in a browser to see live request/response pairs. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 214
echo $! >~/.hermes/cache/scratch/webhook-pinggy.pid sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/webhook-pinggy.log | head -1) - Network accessline 215
sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/webhook-pinggy.log | head -1) echo "Webhook URL: $URL" - Network accessline 216
URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/webhook-pinggy.log | head -1) - Network accessline 241
echo $! >~/.hermes/cache/scratch/mcp-pinggy.pid sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/mcp-pinggy.log | head -1) - Network accessline 242
sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/mcp-pinggy.log | head -1) echo "MCP URL: $URL" - Network accessline 243
URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/mcp-pinggy.log | head -1) - Network accessline 261
echo $! >~/.hermes/cache/scratch/llm-pinggy.pid sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/llm-pinggy.log | head -1) - Network accessline 262
sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/llm-pinggy.log | head -1) echo "Endpoint: $URL" - Network accessline 263
URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/llm-pinggy.log | head -1) - Network accessline 267
[Credential-related source omitted] - Network accessline 268
[Credential-related source omitted] - Network accessline 301
sleep 5 URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/pinggy-verify.log | head -1) - Network accessline 302
URL=$(grep -oE 'https://[a-z0-9-]+\.[a-z]+\.pinggy\.link' ~/.hermes/cache/scratch/pinggy-verify.log | head -1) echo "URL: $URL" curl -sI "$URL/" | head -1 - Network accessline 304
curl -sI "$URL/" | head -1 - Network accessline 309
Expected: a `pinggy.link` URL and `HTTP/2 200` on the curl head.
optional-skills/devops/setup-wizard-generator/templates/template.sh
- Executable scriptline 1
#!/usr/bin/env bash - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 174
[Credential-related source omitted] - Network accessline 192
stage "Stripe: API keys" say "We'll grab your Stripe test keys and store them for local dev + CI." open_url "https://dashboard.stripe.com/test/apikeys" - Network accessline 193
say "We'll grab your Stripe test keys and store them for local dev + CI." open_url "https://dashboard.stripe.com/test/apikeys" step "On the API keys page, copy the Publishable key (starts pk_test_)." - Network accessline 194
open_url "https://dashboard.stripe.com/test/apikeys" - Access to secretsline 199
[Credential-related source omitted] - Access to secretsline 200
[Credential-related source omitted] - Access to secretsline 201
[Credential-related source omitted]
optional-skills/devops/watchers/SKILL.md
- Network accessline 61
python $HERMES_HOME/skills/devops/watchers/scripts/watch_rss.py \ --name hn --url https://news.ycombinator.com/rss --max 5 - Network accessline 62
--name hn --url https://news.ycombinator.com/rss --max 5 - Network accessline 75
python $HERMES_HOME/skills/devops/watchers/scripts/watch_http_json.py \ --name api --url https://api.example.com/events \ - Network accessline 76
--name api --url https://api.example.com/events \ - Network accessline 84
> Every 15 minutes, run `watch_rss.py --name hn --url https://news.ycombinator.com/rss`. If it prints anything, summarize the headlines and deliver them. If it prints nothing, stay silent. - Dangerous shell operationline 99
rm $HERMES_HOME/watcher-state/hn.json
optional-skills/devops/watchers/scripts/_watermark.py
optional-skills/devops/watchers/scripts/watch_github.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 11
[Credential-related source omitted] - Network accessline 95
if args.search: url = ( "https://api.github.com/search/issues" - Network accessline 96
url = ( "https://api.github.com/search/issues" f"?q={urllib.parse.quote(args.search)}&per_page={args.per_page}" - Network accessline 97
"https://api.github.com/search/issues" f"?q={urllib.parse.quote(args.search)}&per_page={args.per_page}" ) - Network accessline 101
items_path = "items" elif args.scope == "commits": url = f"https://api.github.com/repos/{args.repo}/commits?per_page={args.per_page}" - Network accessline 102
elif args.scope == "commits": url = f"https://api.github.com/repos/{args.repo}/commits?per_page={args.per_page}" flatten = _flatten_commit - Network accessline 103
url = f"https://api.github.com/repos/{args.repo}/commits?per_page={args.per_page}" - Network accessline 106
else: url = ( f"https://api.github.com/repos/{args.repo}/{args.scope}" - Network accessline 107
url = ( f"https://api.github.com/repos/{args.repo}/{args.scope}" f"?per_page={args.per_page}&state=all" - Network accessline 108
f"https://api.github.com/repos/{args.repo}/{args.scope}" - Access to secretsline 118
[Credential-related source omitted] - Network accessline 122
req = urllib.request.Request(url) - Network accessline 126
try: with urllib.request.urlopen(req, timeout=args.timeout) as resp: - Network accessline 127
with urllib.request.urlopen(req, timeout=args.timeout) as resp:
optional-skills/devops/watchers/scripts/watch_http_json.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 6
hermes cron create api-events \ --schedule "*/1 * * * *" --no-agent \ --script "$HERMES_HOME/skills/devops/watchers/scripts/watch_http_json.py" \ --script-args "--name api --url https://api.example.com/events \ - Network accessline 7
--schedule "*/1 * * * *" --no-agent \\ --script "$HERMES_HOME/skills/devops/watchers/scripts/watch_http_json.py" \\ --script-args "--name api --url https://api.example.com/events \\ - Network accessline 8
--script "$HERMES_HOME/skills/devops/watchers/scripts/watch_http_json.py" \\ --script-args "--name api --url https://api.example.com/events \\ --id-field event_id --items-path data.events" - Network accessline 9
--script-args "--name api --url https://api.example.com/events \\ - Network accessline 79
req = urllib.request.Request(args.url, headers={"User-Agent": "Hermes-Watcher/1.0"}) - Network accessline 83
try: with urllib.request.urlopen(req, timeout=args.timeout) as resp: - Network accessline 84
with urllib.request.urlopen(req, timeout=args.timeout) as resp:
optional-skills/devops/watchers/scripts/watch_rss.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 6
hermes cron create my-feed \ --schedule "*/15 * * * *" --no-agent \ --script "$HERMES_HOME/skills/devops/watchers/scripts/watch_rss.py" \ --script-args "--name hn --url https://news.ycombinator.com/rss" - Network accessline 7
--schedule "*/15 * * * *" --no-agent \\ --script "$HERMES_HOME/skills/devops/watchers/scripts/watch_rss.py" \\ --script-args "--name hn --url https://news.ycombinator.com/rss" - Network accessline 9
--script-args "--name hn --url https://news.ycombinator.com/rss" - Network accessline 90
try: req = urllib.request.Request(args.url, headers={"User-Agent": "Hermes-Watcher/1.0"}) - Network accessline 91
req = urllib.request.Request(args.url, headers={"User-Agent": "Hermes-Watcher/1.0"}) - Network accessline 92
with urllib.request.urlopen(req, timeout=args.timeout) as resp:
optional-skills/email/agentmail/SKILL.md
- Network accessline 9
hermes: tags: [Email, CLI, AgentMail, Communication] homepage: https://agentmail.to - Network accessline 10
tags: [Email, CLI, AgentMail, Communication] homepage: https://agentmail.to prerequisites: - Network accessline 11
homepage: https://agentmail.to - Network accessline 15
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Network accessline 16
prompt: AgentMail API key (starts with am_) help: "Create one at https://console.agentmail.to — or run the CLI self-signup flow in references/signup.md to obtain a key without one." required_for: "authenticating the agentmail CLI; n - Network accessline 17
help: "Create one at https://console.agentmail.to — or run the CLI self-signup flow in references/signup.md to obtain a key without one." - Access to secretsline 49
[Credential-related source omitted] - Network accessline 64
- [AgentMail agent reference](https://agentmail.md): hosted copy. - Network accessline 65
- [AgentMail](https://agentmail.to): product landing page. - Network accessline 66
- [Console](https://console.agentmail.to): API keys and account management. - Network accessline 67
- [Docs](https://docs.agentmail.to): full product documentation. - Access to secretsline 85
[Credential-related source omitted]
optional-skills/email/agentmail/references/core.md
- Access to secretsline 12
[Credential-related source omitted] - Network accessline 109
[Credential-related source omitted] - Access to secretsline 109
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Network accessline 113
Base URLs: `https://api.agentmail.to/v0`, `https://api.agentmail.eu/v0`.
optional-skills/email/agentmail/references/mcp.md
- Network accessline 9
https://mcp.agentmail.to/mcp
optional-skills/email/agentmail/references/signup.md
optional-skills/email/agentmail/references/webhooks.md
optional-skills/email/agentmail/references/websockets.md
optional-skills/finance/3-statement-model/SKILL.md
- Network accessline 423
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings. - **Otherwise**, fall back to: - `web_search` / `web_ex - Network accessline 424
- **Otherwise**, fall back to: - `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Company IR pages for press releases, earnings decks - Network accessline 425
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Network accessline 433
This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the `excel-author` skill's conventions. Or
optional-skills/finance/3-statement-model/references/sec-filings.md
- Network accessline 13
1. Use SEC EDGAR: `https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=[TICKER]&type=10-K`
optional-skills/finance/comps-analysis/SKILL.md
- Network accessline 652
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings. - **Otherwise**, fall back to: - `web_search` / `web_ex - Network accessline 653
- **Otherwise**, fall back to: - `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Company IR pages for press releases, earnings decks - Network accessline 654
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Network accessline 662
This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the `excel-author` skill's conventions. Or
optional-skills/finance/dcf-model/SKILL.md
- Network accessline 1260
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings. - **Otherwise**, fall back to: - `web_search` / `web_ex - Network accessline 1261
- **Otherwise**, fall back to: - `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Company IR pages for press releases, earnings decks - Network accessline 1262
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Network accessline 1270
This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the `excel-author` skill's conventions. Or
optional-skills/finance/dcf-model/scripts/validate_dcf.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/finance/excel-author/SKILL.md
- Network accessline 18
Adapted from Anthropic's `xlsx-author` and `audit-xls` skills in the [anthropics/financial-services](https://github.com/anthropics/financial-services) repo. The MCP / Office-JS / Cowork-specific branches of the originals are dropped — this - Dangerous shell operationline 53
case "$old" in hermes-docs-venv.??????) rm -rf "$old" ;; esac - Dangerous shell operationline 55
[ "$(readlink hermes-docs-venv)" = "$stage" ] || rm -rf "$stage" "$stage.lnk" - Network accessline 275
Conventions (blue/black/green, formulas-over-hardcodes, named ranges, sensitivity rules) adapted from Anthropic's Claude for Financial Services plugin suite, Apache-2.0 licensed. Original: https://github.com/anthropics/financial-services/tr
optional-skills/finance/excel-author/scripts/recalc.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/finance/lbo-model/SKILL.md
- Network accessline 281
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings. - **Otherwise**, fall back to: - `web_search` / `web_ex - Network accessline 282
- **Otherwise**, fall back to: - `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Company IR pages for press releases, earnings decks - Network accessline 283
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Network accessline 291
This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the `excel-author` skill's conventions. Or
optional-skills/finance/merger-model/SKILL.md
- Network accessline 134
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings. - **Otherwise**, fall back to: - `web_search` / `web_ex - Network accessline 135
- **Otherwise**, fall back to: - `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Company IR pages for press releases, earnings decks - Network accessline 136
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings - Network accessline 144
This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the `excel-author` skill's conventions. Or
optional-skills/finance/polymarket/SKILL.md
- Network accessline 16
See `references/api-endpoints.md` for the full endpoint reference with curl examples.
optional-skills/finance/polymarket/scripts/polymarket.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
GAMMA = "https://gamma-api.polymarket.com" - Network accessline 23
CLOB = "https://clob.polymarket.com" - Network accessline 24
DATA = "https://data-api.polymarket.com" - Network accessline 29
req = urllib.request.Request(url, headers={"User-Agent": "hermes-agent/1.0"}) - Network accessline 30
try: with urllib.request.urlopen(req, timeout=15) as resp: return json.loads(resp.read().decode()) - Network accessline 31
with urllib.request.urlopen(req, timeout=15) as resp:
optional-skills/finance/pptx-author/SKILL.md
- Network accessline 18
Adapted from Anthropic's `pptx-author` and `pitch-deck` skills in [anthropics/financial-services](https://github.com/anthropics/financial-services). The MCP / Office-JS branches of the originals are dropped — this assumes headless Python. - Dangerous shell operationline 54
case "$old" in hermes-docs-venv.??????) rm -rf "$old" ;; esac - Dangerous shell operationline 56
[ "$(readlink hermes-docs-venv)" = "$stage" ] || rm -rf "$stage" "$stage.lnk" - Network accessline 204
Conventions adapted from Anthropic's Claude for Financial Services plugin suite, Apache-2.0 licensed. Original: https://github.com/anthropics/financial-services/tree/main/plugins/agent-plugins/pitch-agent/skills/pptx-author
optional-skills/finance/stocks/SKILL.md
- Network accessline 31
Python 3.8+ stdlib only. Optional: set `ALPHA_VANTAGE_KEY` to enrich `market_cap`, `pe_ratio`, and 52-week levels when Yahoo's crumb-protected fields come back null. Free key: https://www.alphavantage.co/support/#api-key - Network accessline 33
fields come back null. Free key: https://www.alphavantage.co/support/#api-key
optional-skills/finance/stocks/scripts/stocks_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
USER_AGENT = "Mozilla/5.0 (compatible; HermesAgent/1.0)" YF_BASE = "https://query1.finance.yahoo.com" - Network accessline 23
YF_BASE = "https://query1.finance.yahoo.com" - Network accessline 24
YF_BASE2 = "https://query2.finance.yahoo.com" - Network accessline 25
AV_BASE = "https://www.alphavantage.co/query" - Network accessline 106
def _build_request(url: str, headers: dict | None = None) -> urllib.request.Request: - Network accessline 107
req = urllib.request.Request(url) - Network accessline 153
try: req = _build_request("https://finance.yahoo.com/") - Network accessline 154
req = _build_request("https://finance.yahoo.com/") - Access to secretsline 243
[Credential-related source omitted] - Access to secretsline 377
[Credential-related source omitted]
optional-skills/gaming/minecraft-modpack-server/SKILL.md
- Network accessline 38
mkdir -p ~/minecraft-server cd ~/minecraft-server wget -O serverpack.zip "<URL>" - Network accessline 39
cd ~/minecraft-server wget -O serverpack.zip "<URL>" unzip -o serverpack.zip -d server - Network accessline 40
wget -O serverpack.zip "<URL>" - Dangerous shell operationline 48
- Minecraft 1.21+ → Java 21: `sudo apt install openjdk-21-jre-headless` - Dangerous shell operationline 49
- Minecraft 1.18-1.20 → Java 17: `sudo apt install openjdk-17-jre-headless` - Dangerous shell operationline 50
- Minecraft 1.16 and below → Java 8: `sudo apt install openjdk-8-jre-headless` - Dangerous shell operationline 128
sudo ufw allow 25565/tcp comment "Minecraft Server" - Dangerous shell operationline 130
Check with: `sudo ufw status | grep 25565` - Dangerous shell operationline 163
ls -1t "$BACKUP_DIR"/world_*.tar.gz | tail -n "$REMOVE" | xargs rm -f
optional-skills/health/fitness-nutrition/SKILL.md
- Network accessline 17
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Network accessline 18
prompt: "USDA FoodData Central API key (free)" help: "Get one free at https://fdc.nal.usda.gov/api-key-signup/ — or skip to use DEMO_KEY with lower rate limits" required_for: "higher rate limits on food/nutrition lookups (DEMO_KEY w - Network accessline 19
help: "Get one free at https://fdc.nal.usda.gov/api-key-signup/ — or skip to use DEMO_KEY with lower rate limits" - Network accessline 31
- **wger** (https://wger.de/api/v2/) — open exercise database, 690+ exercises with muscles, equipment, images. Public endpoints need zero authentication. - Network accessline 32
- **USDA FoodData Central** (https://api.nal.usda.gov/fdc/v1/) — US government nutrition database, 380,000+ foods. `DEMO_KEY` works instantly; free signup for higher limits. - Network accessline 112
QUERY="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$QUERY") curl -s "https://wger.de/api/v2/exercise/search/?term=${ENCODED}&language=english&format=json" \ - Network accessline 113
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$QUERY") curl -s "https://wger.de/api/v2/exercise/search/?term=${ENCODED}&language=english&format=json" \ | python -c " - Dangerous shell operationline 114
curl -s "https://wger.de/api/v2/exercise/search/?term=${ENCODED}&language=english&format=json" | python -c " - Network accessline 114
curl -s "https://wger.de/api/v2/exercise/search/?term=${ENCODED}&language=english&format=json" \ - Network accessline 125
# Get full details for a specific exercise EXERCISE_ID="$1" curl -s "https://wger.de/api/v2/exerciseinfo/${EXERCISE_ID}/?format=json" \ - Network accessline 126
EXERCISE_ID="$1" curl -s "https://wger.de/api/v2/exerciseinfo/${EXERCISE_ID}/?format=json" \ | python -c " - Dangerous shell operationline 127
curl -s "https://wger.de/api/v2/exerciseinfo/${EXERCISE_ID}/?format=json" | python -c " - Network accessline 127
curl -s "https://wger.de/api/v2/exerciseinfo/${EXERCISE_ID}/?format=json" \ - Network accessline 147
# Combine filters as needed: ?muscles=4&equipment=1&language=2&status=2 FILTER="$1" # e.g. "muscles=4" or "category=11" or "equipment=3" curl -s "https://wger.de/api/v2/exercise/?${FILTER}&language=2&status=2&limit=20&format=json" \ - Network accessline 148
FILTER="$1" # e.g. "muscles=4" or "category=11" or "equipment=3" curl -s "https://wger.de/api/v2/exercise/?${FILTER}&language=2&status=2&limit=20&format=json" \ | python -c " - Dangerous shell operationline 149
curl -s "https://wger.de/api/v2/exercise/?${FILTER}&language=2&status=2&limit=20&format=json" | python -c " - Network accessline 149
curl -s "https://wger.de/api/v2/exercise/?${FILTER}&language=2&status=2&limit=20&format=json" \ - Access to secretsline 161
[Credential-related source omitted] - Network accessline 167
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Network accessline 168
[Credential-related source omitted] - Dangerous shell operationline 169
[Credential-related source omitted] - Network accessline 169
[Credential-related source omitted] - Access to secretsline 169
[Credential-related source omitted] - Network accessline 188
[Credential-related source omitted] - Network accessline 189
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Dangerous shell operationline 190
[Credential-related source omitted] - Network accessline 190
[Credential-related source omitted] - Access to secretsline 190
[Credential-related source omitted]
optional-skills/health/fitness-nutrition/references/FORMULAS.md
optional-skills/health/fitness-nutrition/scripts/body_calc.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/health/fitness-nutrition/scripts/nutrition_search.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 10
[Credential-related source omitted] - Network accessline 20
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Network accessline 21
BASE = "https://api.nal.usda.gov/fdc/v1" - Access to secretsline 27
[Credential-related source omitted] - Network accessline 30
) try: req = urllib.request.Request(url, headers={"Accept": "application/json"}) - Network accessline 31
try: req = urllib.request.Request(url, headers={"Accept": "application/json"}) with urllib.request.urlopen(req, timeout=15) as r: - Network accessline 32
req = urllib.request.Request(url, headers={"Accept": "application/json"}) - Network accessline 33
with urllib.request.urlopen(req, timeout=15) as r: - Network accessline 79
[Credential-related source omitted] - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 80
[Credential-related source omitted] - Network accessline 81
print("Free signup: https://fdc.nal.usda.gov/api-key-signup/")
optional-skills/health/neuroskill-bci/SKILL.md
- Network accessline 17
Connect Hermes to a running [NeuroSkill](https://neuroskill.com/) instance to read - Network accessline 451
- [NeuroSkill Paper — arXiv:2603.03212](https://arxiv.org/abs/2603.03212) (Kosmyna & Hauptmann, MIT Media Lab) - Network accessline 452
- [NeuroSkill Desktop App](https://github.com/NeuroSkill-com/skill) (GPLv3) - Network accessline 453
- [NeuroLoop CLI Companion](https://github.com/NeuroSkill-com/neuroloop) (GPLv3) - Network accessline 454
- [MIT Media Lab Project](https://www.media.mit.edu/projects/neuroskill/overview/)
optional-skills/health/neuroskill-bci/references/api.md
optional-skills/mcp/fastmcp/SKILL.md
- Network accessline 9
hermes: tags: [MCP, FastMCP, Python, Tools, Resources, Prompts, Deployment] homepage: https://gofastmcp.com - Network accessline 10
tags: [MCP, FastMCP, Python, Tools, Resources, Prompts, Deployment] homepage: https://gofastmcp.com related_skills: [hermes-agent, mcporter] - Network accessline 11
homepage: https://gofastmcp.com - Network accessline 154
fastmcp run acme_server.py:mcp --transport http --host 127.0.0.1 --port 8000 fastmcp list http://127.0.0.1:8000/mcp --json - Network accessline 155
fastmcp list http://127.0.0.1:8000/mcp --json - Network accessline 156
fastmcp call http://127.0.0.1:8000/mcp search_resources query=router --json
optional-skills/mcp/fastmcp/references/fastmcp-cli.md
- Network accessline 49
fastmcp list http://127.0.0.1:8000/mcp --json
optional-skills/mcp/fastmcp/scripts/scaffold_fastmcp.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/mcp/fastmcp/templates/api_wrapper.py
- Executable scriptline 1
Executable file - Network accessline 12
API_BASE_URL = os.getenv("API_BASE_URL", "https://api.example.com") - Access to secretsline 13
[Credential-related source omitted] - Network accessline 24
def _request(method: str, path: str, *, params: dict[str, Any] | None = None) -> Any: url = f"{API_BASE_URL.rstrip('/')}/{path.lstrip('/')}" with httpx.Client(timeout=REQUEST_TIMEOUT, headers=_headers()) as client: - Network accessline 25
url = f"{API_BASE_URL.rstrip('/')}/{path.lstrip('/')}" with httpx.Client(timeout=REQUEST_TIMEOUT, headers=_headers()) as client: response = client.request(method, url, params=params) - Network accessline 26
with httpx.Client(timeout=REQUEST_TIMEOUT, headers=_headers()) as client:
optional-skills/mcp/fastmcp/templates/database_server.py
- Executable scriptline 1
Executable file
optional-skills/mcp/fastmcp/templates/file_processor.py
- Executable scriptline 1
Executable file
optional-skills/mcp/mcp-oauth-remote-gateway/SKILL.md
- Network accessline 46
1. Picks a free local port `P`. 2. Registers a dynamic OAuth client with the AS, sending `redirect_uri = http://127.0.0.1:P/callback`. - Network accessline 47
2. Registers a dynamic OAuth client with the AS, sending `redirect_uri = http://127.0.0.1:P/callback`. - Network accessline 51
When Hermes runs remotely, the `127.0.0.1` in the `redirect_uri` is the remote container's loopback, not the user's. After authorizing, the user's browser 302s to `http://127.0.0.1:P/callback?code=...`, which resolves to the user's own - Network accessline 52
container's loopback, not the user's. After authorizing, the user's browser 302s to `http://127.0.0.1:P/callback?code=...`, which resolves to the user's own laptop and fails to connect. The callback never reaches the Hermes process, the - Network accessline 53
to `http://127.0.0.1:P/callback?code=...`, which resolves to the user's own - Network accessline 82
`ps aux | grep 'hermes dashboard'`). It exposes a connector/MCP console — endpoints like `/api/mcp/servers`, `/api/mcp/status`, and `/connectors` (all login-gated; a cookieless curl returning 401/302 confirms they exist). - Network accessline 84
login-gated; a cookieless curl returning 401/302 confirms they exist). - Network accessline 106
`RAILWAY_PUBLIC_DOMAIN` / `RAILWAY_STATIC_URL` (the `*.up.railway.app` host) and `RAILWAY_SERVICE_*_URL` vars, which sometimes carry a friendlier custom domain. Hand the user the full `https://` URL and point them at the Connectors/MCP - Network accessline 107
`RAILWAY_SERVICE_*_URL` vars, which sometimes carry a friendlier custom domain. Hand the user the full `https://` URL and point them at the Connectors/MCP section. ALWAYS pipe through the `sed` redaction above — these env greps sit next - Network accessline 108
Hand the user the full `https://` URL and point them at the Connectors/MCP - Network accessline 153
curl -sI https://mcp.example.com | grep -i www-authenticate - Network accessline 154
# → Bearer realm="mcp", resource_metadata="https://mcp.example.com/.well-known/oauth-protected-resource" - Network accessline 165
/.well-known/openid-configuration ; do echo "=== $p ===" curl -s -A "python-httpx/0.27" "https://mcp.example.com$p" | head -c 400; echo - Network accessline 166
echo "=== $p ===" curl -s -A "python-httpx/0.27" "https://mcp.example.com$p" | head -c 400; echo done - Network accessline 167
curl -s -A "python-httpx/0.27" "https://mcp.example.com$p" | head -c 400; echo - Network accessline 183
{ "client_name": "Hermes Agent (manual OAuth)", "redirect_uris": ["http://127.0.0.1:8765/callback"], - Network accessline 184
"client_name": "Hermes Agent (manual OAuth)", "redirect_uris": ["http://127.0.0.1:8765/callback"], "grant_types": ["authorization_code", "refresh_token"], - Network accessline 185
"redirect_uris": ["http://127.0.0.1:8765/callback"], - Network accessline 223
After approving, your browser will try to load http://127.0.0.1:8765/callback - Access to secretsline 241
[Credential-related source omitted] - Access to secretsline 251
[Credential-related source omitted] - Network accessline 261
{ "client_id": "...", "redirect_uris": ["http://127.0.0.1:8765/callback"], - Network accessline 262
"client_id": "...", "redirect_uris": ["http://127.0.0.1:8765/callback"], "grant_types": ["authorization_code", "refresh_token"], - Network accessline 263
"redirect_uris": ["http://127.0.0.1:8765/callback"], - Network accessline 279
mcp_servers: <name>: url: "https://mcp.example.com" - Network accessline 280
<name>: url: "https://mcp.example.com" auth: oauth - Network accessline 281
url: "https://mcp.example.com" - Access to secretsline 303
[Credential-related source omitted] - Network accessline 331
5. **Trailing slash matters.** Some servers advertise the resource as `https://mcp.example.com/` with a trailing slash and reject tokens issued against the no-slash variant. Copy the `resource` value verbatim from the `.well-known/oauth-pro - Access to secretsline 337
[Credential-related source omitted] - Access to secretsline 339
[Credential-related source omitted] - Access to secretsline 341
[Credential-related source omitted] - Access to secretsline 351
[Credential-related source omitted] - Access to secretsline 353
[Credential-related source omitted] - Network accessline 355
[Credential-related source omitted] - Access to secretsline 355
[Credential-related source omitted] - Access to secretsline 365
[Credential-related source omitted]
optional-skills/mcp/mcp-oauth-remote-gateway/references/stripe-mcp-oauth-revocation.md
- Network accessline 22
## Why the three usual hypotheses are all wrong Per Stripe's OAuth docs (https://docs.stripe.com/stripe-apps/api-authentication/oauth): - Network accessline 23
Per Stripe's OAuth docs (https://docs.stripe.com/stripe-apps/api-authentication/oauth): - Network accessline 34
## The durable fix: drop OAuth, use a restricted API key as a Bearer token Stripe's MCP docs (https://docs.stripe.com/mcp) are explicit that for - Network accessline 35
Stripe's MCP docs (https://docs.stripe.com/mcp) are explicit that for - Network accessline 43
mcp_servers: stripe: url: https://mcp.stripe.com - Network accessline 44
stripe: url: https://mcp.stripe.com headers: - Network accessline 45
url: https://mcp.stripe.com
optional-skills/mcp/mcp-oauth-remote-gateway/scripts/diagnose-oauth-mcp.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 5
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Network accessline 49
else: body = json.dumps(data).encode() if data is not None else None req = urllib.request.Request(url, data=body, method="POST") - Network accessline 50
body = json.dumps(data).encode() if data is not None else None req = urllib.request.Request(url, data=body, method="POST") for k, v in (headers or {}).items(): - Network accessline 51
req = urllib.request.Request(url, data=body, method="POST") - Network accessline 54
req.add_header("User-Agent", UA) try: r = urllib.request.urlopen(req, timeout=timeout) - Network accessline 55
try: r = urllib.request.urlopen(req, timeout=timeout) return r.status, dict(r.headers), r.read() - Network accessline 56
r = urllib.request.urlopen(req, timeout=timeout) - Network accessline 62
def _get_json(url, timeout=20): req = urllib.request.Request(url, headers={"User-Agent": UA}) - Network accessline 63
req = urllib.request.Request(url, headers={"User-Agent": UA}) - Network accessline 64
return json.loads(urllib.request.urlopen(req, timeout=timeout).read()) - Access to secretsline 67
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted] - Access to secretsline 155
[Credential-related source omitted]
optional-skills/mcp/mcporter/SKILL.md
- Network accessline 9
hermes: tags: [MCP, Tools, API, Integrations, Interop] homepage: https://mcporter.dev - Network accessline 10
tags: [MCP, Tools, API, Integrations, Interop] homepage: https://mcporter.dev prerequisites: - Network accessline 11
homepage: https://mcporter.dev - Network accessline 18
Use `mcporter` to discover, call, and manage [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers and tools directly from the terminal. - Network accessline 46
mcporter auto-discovers servers configured by other MCP clients (Claude Desktop, Cursor, etc.) on the machine. To find new servers to use, browse registries like [mcpfinder.dev](https://mcpfinder.dev) or [mcp.so](https://mcp.so), then conne - Network accessline 49
# Connect to any MCP server by URL (no config needed) mcporter list --http-url https://some-mcp-server.com --name my_server - Network accessline 50
mcporter list --http-url https://some-mcp-server.com --name my_server - Network accessline 65
# Ad-hoc HTTP server (no config needed) mcporter call https://api.example.com/mcp.fetch url=https://example.com - Network accessline 66
mcporter call https://api.example.com/mcp.fetch url=https://example.com - Network accessline 68
# Ad-hoc stdio server mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com - Network accessline 69
mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com
optional-skills/media/gif-search/SKILL.md
- Network accessline 1
--- name: gif-search description: "Search/download GIFs from Tenor via curl + jq." - Network accessline 2
name: gif-search description: "Search/download GIFs from Tenor via curl + jq." version: 1.1.0 - Network accessline 3
description: "Search/download GIFs from Tenor via curl + jq." - Access to secretsline 9
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 32
Get a free API key at https://developers.google.com/tenor/guides/quickstart — the Google Cloud Console Tenor API key is free and has generous rate limits. - Access to secretsline 37
[Credential-related source omitted] - Network accessline 42
# Search and get GIF URLs curl -s "https://tenor.googleapis.com/v2/search?q=thumbs+up&limit=5&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.gif.url' - Network accessline 43
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Network accessline 45
# Get smaller/preview versions curl -s "https://tenor.googleapis.com/v2/search?q=nice+work&limit=3&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.tinygif.url' - Network accessline 46
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Network accessline 52
# Search and download the top result URL=$(curl -s "https://tenor.googleapis.com/v2/search?q=celebration&limit=1&key=${TENOR_API_KEY}" | jq -r '.results[0].media_formats.gif.url') - Network accessline 53
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Network accessline 54
curl -sL "$URL" -o celebration.gif - Network accessline 60
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted]
optional-skills/media/songsee/SKILL.md
- Network accessline 9
hermes: tags: [Audio, Visualization, Spectrogram, Music, Analysis] homepage: https://github.com/steipete/songsee - Network accessline 10
tags: [Audio, Visualization, Spectrogram, Music, Analysis] homepage: https://github.com/steipete/songsee prerequisites: - Network accessline 11
homepage: https://github.com/steipete/songsee - Network accessline 22
Requires [Go](https://go.dev/doc/install):
optional-skills/mlops/accelerate/SKILL.md
- Network accessline 344
- Docs: https://huggingface.co/docs/accelerate - Network accessline 345
- GitHub: https://github.com/huggingface/accelerate - Network accessline 346
- Version: 1.11.0+ - Tutorial: "Accelerate your scripts" - Examples: https://github.com/huggingface/accelerate/tree/main/examples - Network accessline 347
- Tutorial: "Accelerate your scripts" - Examples: https://github.com/huggingface/accelerate/tree/main/examples - Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries - Network accessline 348
- Examples: https://github.com/huggingface/accelerate/tree/main/examples
optional-skills/mlops/accelerate/references/custom-plugins.md
- Network accessline 450
- Accelerate Plugins: https://huggingface.co/docs/accelerate/package_reference/kwargs - Network accessline 451
- DeepSpeed Config: https://www.deepspeed.ai/docs/config-json/ - Network accessline 452
- FSDP Guide: https://pytorch.org/docs/stable/fsdp.html - Network accessline 453
- Custom Training Loops: https://huggingface.co/docs/accelerate/usage_guides/training_tpu
optional-skills/mlops/accelerate/references/megatron-integration.md
- Network accessline 18
# Clone Megatron-LM repository git clone https://github.com/NVIDIA/Megatron-LM.git - Network accessline 19
git clone https://github.com/NVIDIA/Megatron-LM.git - Network accessline 23
# Install Apex (NVIDIA optimizations) git clone https://github.com/NVIDIA/apex - Network accessline 24
git clone https://github.com/NVIDIA/apex - Network accessline 486
- Megatron-LM: https://github.com/NVIDIA/Megatron-LM - Network accessline 487
- Accelerate Megatron docs: https://huggingface.co/docs/accelerate/usage_guides/megatron_lm - Network accessline 489
- NVIDIA Apex: https://github.com/NVIDIA/apex
optional-skills/mlops/accelerate/references/performance.md
- Access to secretsline 303
[Credential-related source omitted] - Network accessline 522
- Accelerate Performance: https://huggingface.co/docs/accelerate/usage_guides/performance - Network accessline 523
- PyTorch Profiler: https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html - Network accessline 524
- NCCL Tuning: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html - Network accessline 525
- Flash Attention: https://github.com/Dao-AILab/flash-attention
optional-skills/mlops/chroma/SKILL.md
- Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 236
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 404
- **GitHub**: https://github.com/chroma-core/chroma ⭐ 24,300+ - Network accessline 405
- **Docs**: https://docs.trychroma.com - Network accessline 406
- **Discord**: https://discord.gg/MMeYNTmh3x
optional-skills/mlops/chroma/references/integration.md
- Network accessline 38
- **Docs**: https://docs.trychroma.com
optional-skills/mlops/evaluation/evaluating-llms-harness/SKILL.md
- Dangerous shell operationline 193
Avoid for frequent eval (too slow): - Network accessline 493
- GitHub: https://github.com/EleutherAI/lm-evaluation-harness - Network accessline 494
- Docs: https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs - Network accessline 496
- Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard (uses this harness)
optional-skills/mlops/evaluation/evaluating-llms-harness/references/api-evaluation.md
- Access to secretsline 36
[Credential-related source omitted] - Network accessline 77
lm_eval --model openai-chat-completions --model_args model=gpt-4-turbo, base_url=https://api.openai.com/v1, - Network accessline 80
base_url=https://api.openai.com/v1,\ - Access to secretsline 121
[Credential-related source omitted] - Network accessline 150
lm_eval --model anthropic-chat --model_args model=claude-3-5-sonnet-20241022, base_url=https://api.anthropic.com, - Network accessline 153
base_url=https://api.anthropic.com,\ - Network accessline 196
lm_eval --model local-completions --model_args model=meta-llama/Llama-2-7b-hf, base_url=http://localhost:8000/v1, - Network accessline 199
base_url=http://localhost:8000/v1,\ - Network accessline 216
lm_eval --model local-completions --model_args model=meta-llama/Llama-2-7b-hf, base_url=http://localhost:8080/v1 - Network accessline 219
base_url=http://localhost:8080/v1 \ - Network accessline 233
lm_eval --model local-completions --model_args model=llama2:7b, base_url=http://localhost:11434/v1 - Network accessline 236
base_url=http://localhost:11434/v1 \ - Network accessline 249
lm_eval --model local-completions --model_args model=llama2, base_url=http://localhost:8080/v1 - Network accessline 252
base_url=http://localhost:8080/v1 \ - Access to secretsline 269
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Access to secretsline 277
[Credential-related source omitted] - Network accessline 300
model = MyCustomAPI( base_url="https://api.example.com/v1", - Network accessline 301
base_url="https://api.example.com/v1", - Access to secretsline 302
[Credential-related source omitted] - Access to secretsline 417
[Credential-related source omitted] - Access to secretsline 418
[Credential-related source omitted] - Network accessline 441
curl http://localhost:8000/v1/models - Network accessline 459
lm_eval --model local-completions --model_args base_url=http://api.example.com/v1, - Network accessline 461
base_url=http://api.example.com/v1,\ - Network accessline 468
lm_eval --model local-completions --model_args base_url=https://localhost:8000/v1, - Network accessline 470
base_url=https://localhost:8000/v1,\ - Network accessline 486
- OpenAI API: https://platform.openai.com/docs/api-reference - Network accessline 487
- Anthropic API: https://docs.anthropic.com/claude/reference
optional-skills/mlops/evaluation/evaluating-llms-harness/references/distributed-eval.md
optional-skills/mlops/faiss/SKILL.md
optional-skills/mlops/faiss/references/index_types.md
optional-skills/mlops/flash-attention/SKILL.md
- Network accessline 260
git clone https://github.com/Dao-AILab/flash-attention.git - Network accessline 367
- Paper: "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (NeurIPS 2022) - Paper: "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning" (ICLR 2024) - Blog: https://tridao.me/blog/202 - Network accessline 368
- Paper: "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning" (ICLR 2024) - Blog: https://tridao.me/blog/2024/flash3/ - GitHub: https://github.com/Dao-AILab/flash-attention - Network accessline 369
- Blog: https://tridao.me/blog/2024/flash3/ - Network accessline 370
- GitHub: https://github.com/Dao-AILab/flash-attention - Network accessline 371
- PyTorch docs: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
optional-skills/mlops/inference/llama-cpp/SKILL.md
- Network accessline 31
1. Search for candidate repos on the Hub: - Base: `https://huggingface.co/models?apps=llama.cpp&sort=trending` - Network accessline 32
- Base: `https://huggingface.co/models?apps=llama.cpp&sort=trending` - Network accessline 34
- Add `num_parameters=min:0,max:24B` or similar when the user has size constraints 2. Open the repo with the llama.cpp local-app view: - `https://huggingface.co/<repo>?local-app=llama.cpp` - Network accessline 35
2. Open the repo with the llama.cpp local-app view: - `https://huggingface.co/<repo>?local-app=llama.cpp` 3. Treat the local-app snippet as the source of truth when it is visible: - Network accessline 36
- `https://huggingface.co/<repo>?local-app=llama.cpp` - Network accessline 43
- if that section is not visible in the fetched page source, say so and fall back to the tree API plus generic quant guidance 5. Query the tree API to confirm what actually exists: - `https://huggingface.co/api/models/<repo>/tree/main?re - Network accessline 44
5. Query the tree API to confirm what actually exists: - `https://huggingface.co/api/models/<repo>/tree/main?recursive=true` - keep entries where `type` is `file` and `path` ends with `.gguf` - Network accessline 45
- `https://huggingface.co/api/models/<repo>/tree/main?recursive=true` - Network accessline 47
- use `path` and `size` as the source of truth for filenames and byte sizes - separate quantized checkpoints from `mmproj-*.gguf` projector files and `BF16/` shard files - use `https://huggingface.co/<repo>/tree/main` only as a human - Network accessline 48
- separate quantized checkpoints from `mmproj-*.gguf` projector files and `BF16/` shard files - use `https://huggingface.co/<repo>/tree/main` only as a human fallback 6. If the local-app snippet is not text-visible, reconstruct the comma - Network accessline 49
- use `https://huggingface.co/<repo>/tree/main` only as a human fallback - Network accessline 69
git clone https://github.com/ggml-org/llama.cpp - Network accessline 99
curl http://localhost:8080/v1/chat/completions \ - Network accessline 198
- `https://huggingface.co/api/models/<repo>/tree/main?recursive=true` - Network accessline 207
https://huggingface.co/models?apps=llama.cpp&sort=trending - Network accessline 208
https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending - Network accessline 209
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending - Network accessline 210
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 211
https://huggingface.co/api/models/<repo>/tree/main?recursive=true - Network accessline 212
https://huggingface.co/<repo>/tree/main - Network accessline 242
- **GitHub**: https://github.com/ggml-org/llama.cpp - Network accessline 243
- **Hugging Face GGUF + llama.cpp docs**: https://huggingface.co/docs/hub/gguf-llamacpp - Network accessline 244
- **Hugging Face Local Apps docs**: https://huggingface.co/docs/hub/main/local-apps - Network accessline 245
- **Hugging Face Local Agents docs**: https://huggingface.co/docs/hub/agents-local - Network accessline 246
- **Example local-app page**: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF?local-app=llama.cpp - Network accessline 247
- **Example tree API**: https://huggingface.co/api/models/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main?recursive=true - Network accessline 248
- **Example llama.cpp search**: https://huggingface.co/models?num_parameters=min:0,max:24B&apps=llama.cpp&sort=trending
optional-skills/mlops/inference/llama-cpp/references/advanced-usage.md
- Access to secretsline 470
[Credential-related source omitted]
optional-skills/mlops/inference/llama-cpp/references/hub-discovery.md
- Network accessline 8
Search: https://huggingface.co/models?apps=llama.cpp&sort=trending - Network accessline 9
https://huggingface.co/models?apps=llama.cpp&sort=trending - Network accessline 11
Search with text: https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending - Network accessline 12
https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending - Network accessline 14
Search with size bounds: https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending - Network accessline 15
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending - Network accessline 17
Repo local-app view: https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 18
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 20
Repo tree API: https://huggingface.co/api/models/<repo>/tree/main?recursive=true - Network accessline 21
https://huggingface.co/api/models/<repo>/tree/main?recursive=true - Network accessline 23
Repo file tree: https://huggingface.co/<repo>/tree/main - Network accessline 24
https://huggingface.co/<repo>/tree/main - Network accessline 39
Example: https://huggingface.co/models?search=Qwen&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending - Network accessline 46
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 67
https://huggingface.co/api/models/<repo>/tree/main?recursive=true - Network accessline 95
Use `https://huggingface.co/<repo>/tree/main` only as a human fallback if the API endpoint fails or the user wants the web view. - Network accessline 127
https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF?local-app=llama.cpp - Network accessline 128
https://huggingface.co/api/models/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main?recursive=true - Network accessline 129
https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main
optional-skills/mlops/inference/llama-cpp/references/quantization.md
- Network accessline 10
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 16
https://huggingface.co/api/models/<repo>/tree/main?recursive=true - Network accessline 174
https://huggingface.co/models?apps=llama.cpp&sort=trending - Network accessline 175
https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending - Network accessline 176
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending - Network accessline 182
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 183
https://huggingface.co/api/models/<repo>/tree/main?recursive=true
optional-skills/mlops/inference/llama-cpp/references/server.md
- Network accessline 10
https://huggingface.co/<repo>?local-app=llama.cpp - Network accessline 52
curl http://localhost:8080/v1/chat/completions \ - Network accessline 67
curl http://localhost:8080/v1/chat/completions \ - Network accessline 80
FROM ubuntu:22.04 RUN apt-get update && apt-get install -y git build-essential RUN git clone https://github.com/ggerganov/llama.cpp - Network accessline 81
RUN apt-get update && apt-get install -y git build-essential RUN git clone https://github.com/ggerganov/llama.cpp WORKDIR /llama.cpp - Network accessline 82
RUN git clone https://github.com/ggerganov/llama.cpp - Network accessline 98
# Server metrics endpoint curl http://localhost:8080/metrics - Network accessline 99
curl http://localhost:8080/metrics - Network accessline 101
# Health check curl http://localhost:8080/health - Network accessline 102
curl http://localhost:8080/health - Network accessline 121
server { location / { proxy_pass http://llama_cpp; - Network accessline 122
location / { proxy_pass http://llama_cpp; proxy_read_timeout 300s; - Network accessline 123
proxy_pass http://llama_cpp;
optional-skills/mlops/inference/llama-cpp/references/troubleshooting.md
- Dangerous shell operationline 22
sudo apt install nvidia-cuda-toolkit - Network accessline 335
# Use correct base URL format client = OpenAI( base_url="http://localhost:8080/v1", # Include /v1 - Network accessline 336
[Credential-related source omitted] - Network accessline 337
base_url="http://localhost:8080/v1", # Include /v1 - Access to secretsline 338
[Credential-related source omitted] - Network accessline 429
1. **GitHub Issues**: https://github.com/ggml-org/llama.cpp/issues - Network accessline 430
2. **Discussions**: https://github.com/ggml-org/llama.cpp/discussions
optional-skills/mlops/inference/serving-llms-vllm/SKILL.md
- Network accessline 46
[Credential-related source omitted] - Network accessline 47
[Credential-related source omitted] - Network accessline 48
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 106
# Create test_load.py with sample requests # Run: locust -f test_load.py --host http://localhost:8000 - Network accessline 107
# Run: locust -f test_load.py --host http://localhost:8000 - Network accessline 117
curl http://localhost:8000/metrics | grep vllm - Network accessline 367
- Official docs: https://docs.vllm.ai - Network accessline 368
- GitHub: https://github.com/vllm-project/vllm - Network accessline 370
- Community: https://discuss.vllm.ai
optional-skills/mlops/inference/serving-llms-vllm/references/optimization.md
- Network accessline 97
curl http://localhost:9090/metrics | grep cache_hit
optional-skills/mlops/inference/serving-llms-vllm/references/quantization.md
optional-skills/mlops/inference/serving-llms-vllm/references/server-deployment.md
- Network accessline 133
location / { proxy_pass http://vllm_backend; - Network accessline 134
proxy_pass http://vllm_backend; - Network accessline 143
# Metrics endpoint location /metrics { proxy_pass http://localhost:9090/metrics; - Network accessline 144
location /metrics { proxy_pass http://localhost:9090/metrics; } - Network accessline 145
proxy_pass http://localhost:9090/metrics; - Network accessline 225
curl http://localhost:8000/health - Network accessline 231
#!/usr/bin/env bash until curl -f http://localhost:8000/health; do - Network accessline 232
until curl -f http://localhost:8000/health; do
optional-skills/mlops/inference/serving-llms-vllm/references/troubleshooting.md
- Network accessline 207
curl http://localhost:8000/health - Dangerous shell operationline 222
sudo ufw allow 8000 - Network accessline 233
client = OpenAI( base_url="http://localhost:8000/v1", - Network accessline 234
base_url="http://localhost:8000/v1", - Access to secretsline 235
[Credential-related source omitted] - Network accessline 251
session = requests.Session() - Network accessline 310
# From node 1 to node 2 ping NODE2_IP nc -zv NODE2_IP 29500 # Check port accessibility - Network accessline 312
nc -zv NODE2_IP 29500 # Check port accessibility - Network accessline 376
# Prometheus metrics curl http://localhost:9090/metrics - Network accessline 377
curl http://localhost:9090/metrics - Network accessline 379
# Filter for specific metrics curl http://localhost:9090/metrics | grep vllm_time_to_first_token - Network accessline 380
curl http://localhost:9090/metrics | grep vllm_time_to_first_token - Network accessline 393
# Health check curl http://localhost:8000/health - Network accessline 394
curl http://localhost:8000/health - Network accessline 396
# Model info curl http://localhost:8000/v1/models - Network accessline 397
curl http://localhost:8000/v1/models - Network accessline 399
# Test completion curl http://localhost:8000/v1/completions \ - Network accessline 400
curl http://localhost:8000/v1/completions \
optional-skills/mlops/lambda-labs/SKILL.md
- Network accessline 47
1. Create account at https://lambda.ai - Network accessline 54
1. Go to https://cloud.lambda.ai/instances - Access to secretsline 68
[Credential-related source omitted] - Network accessline 145
# Configure with API key configuration = lambda_cloud_client.Configuration( host="https://cloud.lambdalabs.com/api/v1", - Network accessline 146
[Credential-related source omitted] - Network accessline 147
host="https://cloud.lambdalabs.com/api/v1", - Access to secretsline 148
[Credential-related source omitted] - Network accessline 225
[Credential-related source omitted] - Access to secretsline 225
[Credential-related source omitted] - Network accessline 226
https://cloud.lambdalabs.com/api/v1/instance-types | jq - Network accessline 232
[Credential-related source omitted] - Access to secretsline 232
[Credential-related source omitted] - Network accessline 233
-X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \ - Network accessline 245
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Network accessline 246
-X POST https://cloud.lambdalabs.com/api/v1/instance-operations/terminate \ - Access to secretsline 299
[Credential-related source omitted] - Access to secretsline 309
[Credential-related source omitted] - Network accessline 346
# From local machine with tunnel ssh -L 8888:localhost:8888 ubuntu@<IP> # Open http://localhost:8888 - Network accessline 348
# Open http://localhost:8888 - Network accessline 359
# Clone repo git clone https://github.com/user/project - Network accessline 360
git clone https://github.com/user/project - Network accessline 545
- **Documentation**: https://docs.lambda.ai - Network accessline 546
- **Console**: https://cloud.lambda.ai - Network accessline 547
- **Pricing**: https://lambda.ai/instances - Network accessline 548
- **Support**: https://support.lambdalabs.com - Network accessline 549
- **Blog**: https://lambda.ai/blog
optional-skills/mlops/lambda-labs/references/advanced-usage.md
- Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Network accessline 145
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted] - Network accessline 146
[Credential-related source omitted] - Network accessline 147
host="https://cloud.lambdalabs.com/api/v1", - Access to secretsline 148
[Credential-related source omitted] - Access to secretsline 208
[Credential-related source omitted] - Access to secretsline 253
[Credential-related source omitted] - Network accessline 398
# Install miniconda (if not present) wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh - Network accessline 399
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh - Network accessline 465
# Access at http://localhost:6006 - Access to secretsline 474
[Credential-related source omitted] - Access to secretsline 552
[Credential-related source omitted] - Access to secretsline 554
[Credential-related source omitted] - Access to secretsline 575
[Credential-related source omitted] - Access to secretsline 579
[Credential-related source omitted] - Access to secretsline 582
[Credential-related source omitted] - Access to secretsline 603
[Credential-related source omitted] - Access to secretsline 604
[Credential-related source omitted] - Access to secretsline 606
[Credential-related source omitted] - Access to secretsline 607
[Credential-related source omitted] - Access to secretsline 610
[Credential-related source omitted]
optional-skills/mlops/lambda-labs/references/troubleshooting.md
- Network accessline 11
[Credential-related source omitted] - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
https://cloud.lambdalabs.com/api/v1/instance-types | jq '.data | to_entries[] | select(.value.regions_with_capacity_available | length > 0) | .key' - Access to secretsline 47
[Credential-related source omitted] - Network accessline 49
[Credential-related source omitted] - Network accessline 50
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 51
https://cloud.lambdalabs.com/api/v1/instance-types - Network accessline 81
[Credential-related source omitted] - Network accessline 82
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Network accessline 83
https://cloud.lambdalabs.com/api/v1/instances | jq '.data[].ip' - Access to secretsline 93
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Dangerous shell operationline 145
sudo reboot - Network accessline 147
# Reinstall NVIDIA drivers (if needed) wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Dangerous shell operationline 148
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Network accessline 148
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Dangerous shell operationline 149
sudo reboot - Network accessline 194
# Lambda Stack should have compatible versions # If mismatch, reinstall Lambda Stack wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Network accessline 195
# If mismatch, reinstall Lambda Stack wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - sudo reboot - Dangerous shell operationline 196
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Network accessline 196
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - - Dangerous shell operationline 197
sudo reboot - Network accessline 199
# Or install specific PyTorch version pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html - Network accessline 200
pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html - Access to secretsline 215
[Credential-related source omitted] - Dangerous shell operationline 289
find /lambda/nfs/storage/checkpoints -mtime +7 -delete - Network accessline 306
# Or use SSH tunneling (recommended) ssh -L 6006:localhost:6006 ubuntu@<IP> # Access at http://localhost:6006 - Network accessline 308
# Access at http://localhost:6006 - Network accessline 366
# For CUDA packages, match CUDA version pip install torch --index-url https://download.pytorch.org/whl/cu121 - Network accessline 367
pip install torch --index-url https://download.pytorch.org/whl/cu121 - Dangerous shell operationline 380
sudo apt install python3.11 python3.11-venv python3.11-dev - Network accessline 478
[Credential-related source omitted] - Network accessline 479
[Credential-related source omitted] - Access to secretsline 479
[Credential-related source omitted] - Network accessline 480
https://cloud.lambdalabs.com/api/v1/instances | jq '.data[].id' - Network accessline 517
1. **Documentation**: https://docs.lambda.ai - Network accessline 518
2. **Support**: https://support.lambdalabs.com
optional-skills/mlops/modal/SKILL.md
- Access to secretsline 241
[Credential-related source omitted] - Network accessline 315
> **Modal 1.0 autoscaler renames** (see the [migration guide](https://modal.com/docs/guide/modal-1-0-migration)): - Network accessline 348
- **Documentation**: https://modal.com/docs - Network accessline 349
- **Examples**: https://github.com/modal-labs/modal-examples - Network accessline 350
- **Pricing**: https://modal.com/pricing - Network accessline 351
- **Discord**: https://discord.gg/modal
optional-skills/mlops/modal/references/advanced-usage.md
- Network accessline 87
image = modal.Image.debian_slim().pip_install( "git+https://github.com/huggingface/transformers.git@main" - Network accessline 88
"git+https://github.com/huggingface/transformers.git@main" - Network accessline 148
@app.function(allow_concurrent_inputs=10) async def fetch_data(url: str): async with aiohttp.ClientSession() as session: - Network accessline 149
async def fetch_data(url: str): async with aiohttp.ClientSession() as session: return await session.get(url) - Network accessline 150
async with aiohttp.ClientSession() as session: - Access to secretsline 431
[Credential-related source omitted] - Network accessline 499
# Deployed endpoints accessible via HTTPS curl -X POST https://your-workspace--my-app-predict.modal.run \ - Network accessline 500
curl -X POST https://your-workspace--my-app-predict.modal.run \
optional-skills/mlops/modal/references/troubleshooting.md
- Access to secretsline 19
[Credential-related source omitted] - Network accessline 111
).pip_install( "torch", index_url="https://download.pytorch.org/whl/cu121" # Match CUDA - Network accessline 112
"torch", index_url="https://download.pytorch.org/whl/cu121" # Match CUDA ) - Network accessline 113
index_url="https://download.pytorch.org/whl/cu121" # Match CUDA - Access to secretsline 371
[Credential-related source omitted] - Network accessline 482
1. **Documentation**: https://modal.com/docs - Network accessline 483
2. **Examples**: https://github.com/modal-labs/modal-examples - Network accessline 484
3. **Discord**: https://discord.gg/modal - Network accessline 485
4. **Status**: https://status.modal.com
optional-skills/mlops/models/segment-anything-model/SKILL.md
- Network accessline 48
# From GitHub pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 49
pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 61
# ViT-H (largest, most accurate) - 2.4GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 62
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 64
# ViT-L (medium) - 1.2GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth - Network accessline 65
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth - Network accessline 67
# ViT-B (smallest, fastest) - 375MB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth - Network accessline 68
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth - Network accessline 502
- **GitHub**: https://github.com/facebookresearch/segment-anything - Network accessline 503
- **Paper**: https://arxiv.org/abs/2304.02643 - Network accessline 504
- **Demo**: https://segment-anything.com - Network accessline 505
- **SAM 2 (Video)**: https://github.com/facebookresearch/segment-anything-2 - Network accessline 506
- **HuggingFace**: https://huggingface.co/facebook/sam-vit-huge
optional-skills/mlops/models/segment-anything-model/references/advanced-usage.md
- Network accessline 10
pip install git+https://github.com/facebookresearch/segment-anything-2.git - Network accessline 52
pip install groundingdino-py pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 53
pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 349
# MedSAM is a fine-tuned SAM for medical images # https://github.com/bowang-lab/MedSAM - Network accessline 350
# https://github.com/bowang-lab/MedSAM
optional-skills/mlops/models/segment-anything-model/references/troubleshooting.md
- Network accessline 16
# Install PyTorch with CUDA pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 - Network accessline 17
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 - Network accessline 30
# Install from GitHub pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 31
pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 33
# Or clone and install git clone https://github.com/facebookresearch/segment-anything.git - Network accessline 34
git clone https://github.com/facebookresearch/segment-anything.git - Network accessline 63
# Download correct checkpoint wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 64
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 472
1. **GitHub Issues**: https://github.com/facebookresearch/segment-anything/issues - Network accessline 473
2. **HuggingFace Forums**: https://discuss.huggingface.co - Network accessline 474
3. **Paper**: https://arxiv.org/abs/2304.02643
optional-skills/mlops/nemo-curator/SKILL.md
- Network accessline 62
> stage/import surface differs per modality — treat the examples in this skill below as > **conceptual** (0.x-style) and follow the current > [quickstart](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/quickstart.py) - Network accessline 63
> **conceptual** (0.x-style) and follow the current > [quickstart](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/quickstart.py) > and [text guide](https://docs.nvidia.com/nemo/curator/latest/get-started/text) for the - Network accessline 64
> [quickstart](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/quickstart.py) - Network accessline 65
> and [text guide](https://docs.nvidia.com/nemo/curator/latest/get-started/text) for the - Network accessline 395
- **GitHub**: https://github.com/NVIDIA-NeMo/Curator - Network accessline 396
- **Docs**: https://docs.nvidia.com/nemo/curator/latest/
optional-skills/mlops/obliteratus/SKILL.md
- Network accessline 27
Walkthrough of OBLITERATUS used by a Hermes agent to abliterate Gemma: https://www.youtube.com/watch?v=8fG9BrNTeHs ("OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails") - Network accessline 28
https://www.youtube.com/watch?v=8fG9BrNTeHs ("OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails") - Network accessline 51
git clone https://github.com/elder-plinius/OBLITERATUS.git
optional-skills/mlops/peft/SKILL.md
- Network accessline 431
- **GitHub**: https://github.com/huggingface/peft - Network accessline 432
- **Docs**: https://huggingface.co/docs/peft - Network accessline 433
- **LoRA Paper**: arXiv:2106.09685 - **QLoRA Paper**: arXiv:2305.14314 - **Models**: https://huggingface.co/models?library=peft - Network accessline 435
- **Models**: https://huggingface.co/models?library=peft
optional-skills/mlops/peft/references/troubleshooting.md
- Network accessline 18
# Or compile from source for specific CUDA git clone https://github.com/TimDettmers/bitsandbytes.git - Network accessline 19
git clone https://github.com/TimDettmers/bitsandbytes.git - Network accessline 218
**Error**: `ValueError: We couldn't connect to 'https://huggingface.co'` - Network accessline 457
1. **Check PEFT GitHub Issues**: https://github.com/huggingface/peft/issues - Network accessline 458
2. **HuggingFace Forums**: https://discuss.huggingface.co/ - Network accessline 459
3. **PEFT Documentation**: https://huggingface.co/docs/peft
optional-skills/mlops/pytorch-fsdp/references/common-patterns.md
- Network accessline 33
**Pattern 5:** Groups# By default collectives operate on the default group (also called the world) and require all processes to enter the distributed function call. However, some workloads can benefit from more fine-grained communication. T - Network accessline 39
**Pattern 6:** Warning Safe concurrent usage: When using multiple process groups with the NCCL backend, the user must ensure a globally consistent execution order of collectives across ranks. If multiple threads within a process issue colle
optional-skills/mlops/pytorch-fsdp/references/other.md
- Network accessline 9
**URL:** https://pytorch.org/docs/stable/notes/ddp.html - Network accessline 74
**URL:** https://pytorch.org/docs/stable/ - Network accessline 92
**URL:** https://pytorch.org/docs/stable/distributed.algorithms.join.html - Network accessline 159
**URL:** https://pytorch.org/docs/stable/distributed._dist2.html - Network accessline 380
**URL:** https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html - Network accessline 578
**URL:** https://pytorch.org/docs/stable/distributed.html - Network accessline 693
pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. As of now, the only options we support is ProcessGroupNCCL.Option - Network accessline 835
See Using multiple NCCL communicators concurrently <https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#using-multiple-nccl-communicators-concurrently> for more details. - Network accessline 843
pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the nccl backend, is_high_priority_stream can be specifi - Access to secretsline 1725
[Credential-related source omitted] - Network accessline 1805
**URL:** https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html - Network accessline 1964
**URL:** https://pytorch.org/docs/stable/ddp_comm_hooks.html - Network accessline 2168
**URL:** https://pytorch.org/docs/stable/distributed.checkpoint.html - Network accessline 2900
**URL:** https://pytorch.org/docs/stable/distributed.tensor.html - Network accessline 3290
**URL:** https://pytorch.org/docs/stable/fsdp.html - Network accessline 3373
device_mesh (Optional[DeviceMesh]) – DeviceMesh can be used as an alternative to process_group. When device_mesh is passed, FSDP will use the underlying process groups for all-gather and reduce-scatter collective communications. Therefore, - Network accessline 3729
**URL:** https://pytorch.org/docs/stable/distributed.optim.html - Network accessline 3854
**URL:** https://pytorch.org/docs/stable/distributed.elastic.html - Network accessline 3869
**URL:** https://pytorch.org/docs/stable/distributed.pipelining.html - Network accessline 4075
The Interleaved 1F1B schedule. See https://arxiv.org/pdf/2104.04473 for details. Will perform one forward and one backward on the microbatches in steady state and supports multiple stages per rank. When microbatches are ready for multiple l - Network accessline 4083
Breadth-First Pipeline Parallelism. See https://arxiv.org/abs/2211.05953 for details. Similar to Interleaved 1F1B, Looped BFS supports multiple stages per rank. What is different is that when microbatches are ready for multiple local stages - Network accessline 4085
The Interleaved Zero Bubble schedule. See https://arxiv.org/pdf/2401.10241 for details. Will perform one forward and one backward on inputs for the microbatches in steady state and supports multiple stages per rank. Uses the backward for we - Network accessline 4089
The Zero Bubble schedule (ZBV variant). See https://arxiv.org/pdf/2401.10241 Section 6 for details. - Network accessline 4097
The DualPipeV schedule. A more efficient schedule variant based on the DualPipe schedule introduced by DeepSeek in https://arxiv.org/pdf/2412.19437 - Network accessline 4121
**URL:** https://pytorch.org/docs/stable/distributed.tensor.parallel.html - Network accessline 4128
Tensor Parallelism(TP) is built on top of the PyTorch DistributedTensor (DTensor)[https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/README.md] and provides different parallelism styles: Colwise, Rowwise, and Sequence Par
optional-skills/mlops/pytorch-lightning/SKILL.md
- Network accessline 343
- Docs: https://lightning.ai/docs/pytorch/stable/ - Network accessline 344
- GitHub: https://github.com/Lightning-AI/pytorch-lightning ⭐ 29,000+ - Network accessline 345
- Version: 2.5.5+ - Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples - Discord: https://discord.gg/lightning-ai - Network accessline 346
- Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples - Network accessline 347
- Discord: https://discord.gg/lightning-ai
optional-skills/mlops/pytorch-lightning/references/callbacks.md
- Network accessline 434
- Callback API: https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html - Network accessline 435
- Built-in callbacks: https://lightning.ai/docs/pytorch/stable/api_references.html#callbacks - Network accessline 436
- Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples/callbacks
optional-skills/mlops/pytorch-lightning/references/distributed.md
- Network accessline 487
- Distributed strategies: https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html - Network accessline 488
- FSDP guide: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/fsdp.html - Network accessline 489
- DeepSpeed: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/deepspeed.html - Network accessline 490
- Multi-node: https://lightning.ai/docs/pytorch/stable/clouds/cluster.html
optional-skills/mlops/pytorch-lightning/references/hyperparameter-tuning.md
- Network accessline 553
- Ray Tune + Lightning: https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html - Network accessline 554
- Optuna: https://optuna.readthedocs.io/ - Network accessline 555
- WandB Sweeps: https://docs.wandb.ai/guides/sweeps - Network accessline 556
- Lightning Tuner: https://lightning.ai/docs/pytorch/stable/tuning.html
optional-skills/mlops/research/dspy/SKILL.md
- Network accessline 35
# Latest development version pip install git+https://github.com/stanfordnlp/dspy.git - Network accessline 36
pip install git+https://github.com/stanfordnlp/dspy.git - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Network accessline 317
lm = dspy.OllamaLocal( model="llama3.1", base_url="http://localhost:11434" - Network accessline 318
model="llama3.1", base_url="http://localhost:11434" ) - Network accessline 319
base_url="http://localhost:11434" - Network accessline 582
- **Documentation**: https://dspy.ai - Network accessline 583
- **GitHub**: https://github.com/stanfordnlp/dspy (22k+ stars) - Network accessline 584
- **Discord**: https://discord.gg/XCGy2WDCQB
optional-skills/mlops/research/dspy/references/examples.md
- Dangerous shell operationline 165
result = eval(expression, {"__builtins__": {}}, {}) - Network accessline 661
- **Documentation**: https://dspy.ai - Network accessline 662
- **Examples Repo**: https://github.com/stanfordnlp/dspy/tree/main/examples - Network accessline 663
- **Discord**: https://discord.gg/XCGy2WDCQB
optional-skills/mlops/research/dspy/references/modules.md
- Dangerous shell operationline 135
return eval(expression)
optional-skills/mlops/research/dspy/references/optimizers.md
- Network accessline 564
- **Paper**: "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines" - **GitHub**: https://github.com/stanfordnlp/dspy - Network accessline 565
- **GitHub**: https://github.com/stanfordnlp/dspy - Network accessline 566
- **Discord**: https://discord.gg/XCGy2WDCQB
optional-skills/mlops/saelens/SKILL.md
- Network accessline 19
**GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars) - Network accessline 361
Browse pre-trained SAE features at [neuronpedia.org](https://neuronpedia.org): - Network accessline 393
### Tutorials - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - Network accessline 394
- [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - Network accessline 395
- [Training a Sparse Autoencoder](https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb) - Network accessline 396
- [ARENA SAE Curriculum](https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab) - Network accessline 398
### Papers - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023) - Network accessline 399
- [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023) - Network accessline 400
- [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) - Anthropic (2024) - Network accessline 401
- [Sparse Autoencoders Find Highly Interpretable Features](https://arxiv.org/abs/2309.08600) - Cunningham et al. (ICLR 2024) - Network accessline 403
### Official Documentation - [SAELens Docs](https://jbloomaus.github.io/SAELens/) - Network accessline 404
- [SAELens Docs](https://jbloomaus.github.io/SAELens/) - Network accessline 405
- [Neuronpedia](https://neuronpedia.org) - Feature browser
optional-skills/mlops/saelens/references/README.md
optional-skills/mlops/saelens/references/tutorials.md
- Network accessline 307
### Official Tutorials - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - Network accessline 308
- [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - Network accessline 309
- [Training SAEs](https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb) - Network accessline 310
- [Logits Lens with Features](https://github.com/jbloomAus/SAELens/blob/main/tutorials/logits_lens_with_features.ipynb) - Network accessline 312
### ARENA Curriculum Comprehensive SAE course: https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab - Network accessline 313
Comprehensive SAE course: https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab - Network accessline 315
### Key Papers - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023) - Network accessline 316
- [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023) - Network accessline 317
- [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) - Anthropic (2024) - Network accessline 318
- [Sparse Autoencoders Find Interpretable Features](https://arxiv.org/abs/2309.08600) - ICLR 2024
optional-skills/mlops/simpo/SKILL.md
- Network accessline 26
# Install PyTorch 2.2.2 # Visit: https://pytorch.org/get-started/locally/ - Network accessline 27
# Visit: https://pytorch.org/get-started/locally/ - Network accessline 29
# Install alignment-handbook git clone https://github.com/huggingface/alignment-handbook.git - Network accessline 30
git clone https://github.com/huggingface/alignment-handbook.git - Network accessline 217
- Paper: https://arxiv.org/abs/2405.14734 (NeurIPS 2024) - Network accessline 218
- GitHub: https://github.com/princeton-nlp/SimPO - Network accessline 219
- Models: https://huggingface.co/princeton-nlp - Network accessline 220
- Alignment Handbook: https://github.com/huggingface/alignment-handbook
optional-skills/mlops/simpo/references/datasets.md
optional-skills/mlops/simpo/references/hyperparameters.md
optional-skills/mlops/simpo/references/loss-functions.md
optional-skills/mlops/slime/SKILL.md
optional-skills/mlops/slime/references/api-reference.md
optional-skills/mlops/slime/references/troubleshooting.md
optional-skills/mlops/tensorrt-llm/SKILL.md
- Network accessline 43
# Docker (recommended) — images are on NGC (nvcr.io), not Docker Hub. # Replace x.y.z with the desired version (e.g. 1.2.1). Browse tags on NGC: # https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags - Network accessline 44
# Replace x.y.z with the desired version (e.g. 1.2.1). Browse tags on NGC: # https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags docker pull nvcr.io/nvidia/tensorrt-llm/release:x.y.z - Network accessline 45
# https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags - Network accessline 87
# Client request curl -X POST http://localhost:8000/v1/chat/completions \ - Network accessline 88
curl -X POST http://localhost:8000/v1/chat/completions \ - Network accessline 190
- **Docs**: https://nvidia.github.io/TensorRT-LLM/ - Network accessline 191
- **GitHub**: https://github.com/NVIDIA/TensorRT-LLM - Network accessline 192
- **Models**: https://huggingface.co/models?library=tensorrt_llm
optional-skills/mlops/tensorrt-llm/references/multi-gpu.md
optional-skills/mlops/tensorrt-llm/references/serving.md
- Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 74
curl -X POST http://localhost:8000/v1/chat/completions \ - Network accessline 114
curl -X POST http://localhost:8000/v1/chat/completions \ - Network accessline 137
curl -X POST http://localhost:8000/v1/completions \ - Network accessline 160
# Scrape metrics curl http://localhost:9090/metrics - Network accessline 161
curl http://localhost:9090/metrics - Network accessline 176
# Readiness probe curl http://localhost:8000/health/ready - Network accessline 177
curl http://localhost:8000/health/ready - Network accessline 179
# Liveness probe curl http://localhost:8000/health/live - Network accessline 180
curl http://localhost:8000/health/live - Network accessline 182
# Model info curl http://localhost:8000/v1/models - Network accessline 183
curl http://localhost:8000/v1/models - Network accessline 306
listen 80; location / { proxy_pass http://tensorrt_llm; - Network accessline 307
location / { proxy_pass http://tensorrt_llm; proxy_read_timeout 300s; # Long timeout for slow generations - Network accessline 308
proxy_pass http://tensorrt_llm; - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 391
[Credential-related source omitted] - Access to secretsline 392
[Credential-related source omitted] - Network accessline 394
# Client request curl -X POST http://localhost:8000/v1/chat/completions \ - Network accessline 395
[Credential-related source omitted] - Access to secretsline 395
[Credential-related source omitted] - Access to secretsline 396
[Credential-related source omitted] - Network accessline 430
# Check queue size curl http://localhost:9090/metrics | grep queue_size - Network accessline 431
curl http://localhost:9090/metrics | grep queue_size - Network accessline 433
# Check active requests curl http://localhost:9090/metrics | grep active_requests - Network accessline 434
curl http://localhost:9090/metrics | grep active_requests
optional-skills/mlops/torchtitan/SKILL.md
- Network accessline 26
# From source (latest features, requires PyTorch nightly) git clone https://github.com/pytorch/torchtitan - Network accessline 27
git clone https://github.com/pytorch/torchtitan - Network accessline 34
# Get HF token from https://huggingface.co/settings/tokens - Network accessline 201
USE_CPP=0 pip install git+https://github.com/pytorch/ao.git - Network accessline 384
- GitHub: https://github.com/pytorch/torchtitan - Network accessline 385
- Paper: https://arxiv.org/abs/2410.06511 - Network accessline 386
- ICLR 2025: https://iclr.cc/virtual/2025/poster/29620 - Network accessline 387
- PyTorch Forum: https://discuss.pytorch.org/c/distributed/torchtitan/44
optional-skills/mlops/torchtitan/references/checkpoint.md
- Network accessline 156
Checkpoints saved with `last_save_model_only = true` can be loaded directly into [torchtune](https://github.com/pytorch/torchtune) for fine-tuning.
optional-skills/mlops/torchtitan/references/float8.md
- Network accessline 13
USE_CPP=0 pip install git+https://github.com/pytorch/ao.git - Network accessline 127
Check [torchao microbenchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#performance) for forward+backward pass speedups on "layer norm => linear => sigmoid" for different M,N,K sizes. - Network accessline 133
For NVIDIA Blackwell GPUs, TorchTitan supports MXFP8 (Microscaling FP8) for both dense and MoE models. See [docs/mxfp8.md](https://github.com/pytorch/torchtitan/blob/main/docs/mxfp8.md) for details.
optional-skills/mlops/training/axolotl/SKILL.md
- Network accessline 72
**Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https://github.com/axolotl-ai-
optional-skills/mlops/training/axolotl/references/api.md
- Network accessline 9
**URL:** https://docs.axolotl.ai/docs/api/cli.cloud.modal_.html - Network accessline 40
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.base.html - Network accessline 99
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.input_output.html - Network accessline 135
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.completion.html - Network accessline 171
**URL:** https://docs.axolotl.ai/docs/api/utils.collators.core.html - Network accessline 182
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.data.batch_dataset_fetcher.html - Network accessline 230
**URL:** https://docs.axolotl.ai/docs/api/core.datasets.chat.html - Network accessline 259
**URL:** https://docs.axolotl.ai/docs/api/utils.freeze.html - Network accessline 307
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.unsloth_.html - Network accessline 318
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.datasets.html - Network accessline 380
**URL:** https://docs.axolotl.ai/docs/api/core.chat.format.llama3x.html - Network accessline 393
**URL:** https://docs.axolotl.ai/docs/api/datasets.html - Network accessline 424
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.bradley_terry.llama3.html - Network accessline 435
chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs - Network accessline 448
**URL:** https://docs.axolotl.ai/docs/api/common.datasets.html - Network accessline 502
**URL:** https://docs.axolotl.ai/docs/api/cli.train.html - Network accessline 534
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.fetch.html - Network accessline 557
**URL:** https://docs.axolotl.ai/docs/api/utils.tokenization.html - Network accessline 597
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.sampler.html - Network accessline 610
Repeat random sampler (similar to the one implemented in https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds sequence parallelism functionality; i.e., duplicating data across ranks in the same sequence parall - Network accessline 665
**URL:** https://docs.axolotl.ai/docs/api/evaluate.html - Network accessline 699
**URL:** https://docs.axolotl.ai/docs/api/utils.optimizers.adopt.html - Network accessline 708
Copied from https://github.com/iShohei220/adopt - Network accessline 747
**URL:** https://docs.axolotl.ai/docs/api/prompt_tokenizers.html - Network accessline 834
**URL:** https://docs.axolotl.ai/docs/api/cli.art.html - Network accessline 856
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.perplexity.html - Network accessline 869
Calculate perplexity as defined in https://huggingface.co/docs/transformers/en/perplexity. This is a custom variant that doesn’t re-tokenize the input or re-load the model. - Network accessline 889
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.train.html - Network accessline 937
**URL:** https://docs.axolotl.ai/docs/api/cli.vllm_serve.html - Network accessline 974
**URL:** https://docs.axolotl.ai/docs/api/convert.html - Network accessline 1031
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.utils.html - Network accessline 1069
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.pygmalion.html - Network accessline 1106
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.mlflow_.html - Network accessline 1130
**URL:** https://docs.axolotl.ai/docs/api/loaders.adapter.html - Network accessline 1160
**URL:** https://docs.axolotl.ai/docs/api/cli.cloud.base.html - Network accessline 1182
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_flash.html - Network accessline 1195
From: https://github.com/dvlab-research/LongLoRA/blob/main/llama_attn_replace.py - Network accessline 1223
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_patch_multipack.html - Network accessline 1236
**URL:** https://docs.axolotl.ai/docs/api/cli.inference.html - Network accessline 1286
**URL:** https://docs.axolotl.ai/docs/api/loaders.tokenizer.html - Network accessline 1304
Ref: https://github.com/huggingface/transformers/issues/27974#issuecomment-1854188941 - Network accessline 1326
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.sweeps.html - Network accessline 1353
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chatml.html - Network accessline 1369
chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs - Network accessline 1401
**URL:** https://docs.axolotl.ai/docs/api/cli.quantize.html - Network accessline 1424
**URL:** https://docs.axolotl.ai/docs/api/utils.dict.html - Network accessline 1437
Remove null from a dictionary-like obj or list. These can appear due to Dataset loading causing schema merge. See https://github.com/axolotl-ai-cloud/axolotl/pull/2909 - Network accessline 1455
**URL:** https://docs.axolotl.ai/docs/api/ - Network accessline 1501
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.lora_kernels.html - Network accessline 1565
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.stablelm_attn_hijack_flash.html - Network accessline 1597
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.rng_state_loader.html - Network accessline 1608
See https://github.com/huggingface/transformers/pull/37162 - Network accessline 1625
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.utils.html - Network accessline 1636
**URL:** https://docs.axolotl.ai/docs/api/core.training_args.html - Network accessline 1690
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.btlm_attn_hijack_flash.html - Network accessline 1703
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.passthrough.html - Network accessline 1716
**URL:** https://docs.axolotl.ai/docs/api/kernels.swiglu.html - Network accessline 1730
See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202). - Network accessline 1732
Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. - Network accessline 1754
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.trainer.html - Network accessline 1808
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.user_defined.html - Network accessline 1854
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.training.html - Network accessline 1894
**URL:** https://docs.axolotl.ai/docs/api/utils.quantization.html - Network accessline 1960
**URL:** https://docs.axolotl.ai/docs/api/logging_config.html - Network accessline 2007
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.stepwise_supervised.html - Network accessline 2037
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.model.html - Network accessline 2075
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.enums.html - Network accessline 2121
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.trl.html - Network accessline 2170
**URL:** https://docs.axolotl.ai/docs/api/utils.schedulers.html - Network accessline 2196
Implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf) Create a schedule with a learning rate that decreases following the values of the cosine function between - Network accessline 2251
**URL:** https://docs.axolotl.ai/docs/api/cli.merge_lora.html - Network accessline 2284
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_w_system.html - Network accessline 2346
**URL:** https://docs.axolotl.ai/docs/api/loaders.patch_manager.html - Network accessline 2398
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.peft.html - Network accessline 2444
**URL:** https://docs.axolotl.ai/docs/api/common.const.html - Network accessline 2455
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.user_defined.html - Network accessline 2468
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.base.html - Network accessline 2481
**URL:** https://docs.axolotl.ai/docs/api/cli.delinearize_llama4.html - Network accessline 2506
**URL:** https://docs.axolotl.ai/docs/api/integrations.base.html - Network accessline 2662
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.chat_template.html - Network accessline 2760
**URL:** https://docs.axolotl.ai/docs/api/kernels.quantize.html - Network accessline 2790
**URL:** https://docs.axolotl.ai/docs/api/integrations.spectrum.args.html - Network accessline 2814
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_chat.html - Network accessline 2877
**URL:** https://docs.axolotl.ai/docs/api/utils.collators.mamba.html - Network accessline 2899
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.messages.chat.html - Network accessline 2928
**URL:** https://docs.axolotl.ai/docs/api/train.html - Network accessline 2998
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.load.html - Network accessline 3022
**URL:** https://docs.axolotl.ai/docs/api/loaders.model.html - Network accessline 3065
**URL:** https://docs.axolotl.ai/docs/api/utils.distributed.html - Network accessline 3143
**URL:** https://docs.axolotl.ai/docs/api/cli.config.html - Network accessline 3193
**URL:** https://docs.axolotl.ai/docs/api/cli.checks.html - Network accessline 3225
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.llama2_chat.html - Network accessline 3239
Prompt Strategy for finetuning Llama2 chat models see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation. - Network accessline 3241
This implementation is based on the Vicuna PR and the fastchat repo, see also: https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847 - Network accessline 3253
Tokenizing strategy for Llama2 prompts. adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py - Network accessline 3255
A class that manages prompt templates and keeps all conversation history. copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py - Network accessline 3297
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.html - Network accessline 3308
**URL:** https://docs.axolotl.ai/docs/api/cli.utils.args.html - Network accessline 3351
**URL:** https://docs.axolotl.ai/docs/api/integrations.grokfast.optimizer.html - Network accessline 3362
**URL:** https://docs.axolotl.ai/docs/api/core.builders.causal.html - Network accessline 3389
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.user_defined.html - Network accessline 3402
**URL:** https://docs.axolotl.ai/docs/api/cli.evaluate.html - Network accessline 3434
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.utils.html - Network accessline 3463
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_instruct.html - Network accessline 3476
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.lisa.html - Network accessline 3481
Adapted from https://github.com/OptimalScale/LMFlow/pull/701 for HF transformers & Axolotl Arxiv: https://arxiv.org/abs/2403.17919 License: Apache 2.0 - Network accessline 3487
**URL:** https://docs.axolotl.ai/docs/api/models.mamba.modeling_mamba.html - Network accessline 3498
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.metharme.html - Network accessline 3535
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.mamba.html - Network accessline 3563
**URL:** https://docs.axolotl.ai/docs/api/utils.ctx_managers.sequence_parallel.html - Network accessline 3638
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.qat.html - Network accessline 3670
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.zephyr.html - Network accessline 3683
**URL:** https://docs.axolotl.ai/docs/api/kernels.utils.html - Network accessline 3694
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.multipack.html - Network accessline 3707
**URL:** https://docs.axolotl.ai/docs/api/cli.main.html - Network accessline 3753
cli.main.fetch(directory, dest) - Network accessline 3765
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.optimizer.html - Network accessline 3797
**URL:** https://docs.axolotl.ai/docs/api/integrations.kd.trainer.html - Network accessline 3835
**URL:** https://docs.axolotl.ai/docs/api/integrations.lm_eval.args.html - Network accessline 3859
**URL:** https://docs.axolotl.ai/docs/api/integrations.cut_cross_entropy.args.html - Network accessline 3883
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mistral_attn_hijack_flash.html - Network accessline 3896
**URL:** https://docs.axolotl.ai/docs/api/loaders.constants.html - Network accessline 3907
**URL:** https://docs.axolotl.ai/docs/api/utils.bench.html - Network accessline 3929
**URL:** https://docs.axolotl.ai/docs/api/utils.trainer.html - Network accessline 3994
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.config.html - Network accessline 4024
**URL:** https://docs.axolotl.ai/docs/api/cli.args.html - Network accessline 4095
**URL:** https://docs.axolotl.ai/docs/api/common.architectures.html - Network accessline 4106
**URL:** https://docs.axolotl.ai/docs/api/cli.merge_sharded_fsdp_weights.html - Network accessline 4157
**URL:** https://docs.axolotl.ai/docs/api/utils.data.streaming.html - Network accessline 4168
**URL:** https://docs.axolotl.ai/docs/api/core.chat.format.chatml.html - Network accessline 4181
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.chatml.html - Network accessline 4221
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.trl.html - Network accessline 4241
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_xformers.html - Network accessline 4248
Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments - Network accessline 4254
**URL:** https://docs.axolotl.ai/docs/api/kernels.geglu.html - Network accessline 4269
See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202). - Network accessline 4271
Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. - Network accessline 4293
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.profiler.html - Network accessline 4320
**URL:** https://docs.axolotl.ai/docs/api/kernels.lora.html - Network accessline 4336
See “LoRA: Low-Rank Adaptation of Large Language Models” (https://arxiv.org/abs/2106.09685). - Network accessline 4338
Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. - Network accessline 4422
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.trainer_fsdp_optim.html - Network accessline 4446
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.multimodal.html - Network accessline 4481
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.llama3.html - Network accessline 4497
chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs - Network accessline 4529
**URL:** https://docs.axolotl.ai/docs/api/core.chat.format.shared.html - Network accessline 4542
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_expand_mask.html - Network accessline 4549
expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf - Network accessline 4555
**URL:** https://docs.axolotl.ai/docs/api/core.chat.messages.html - Network accessline 4621
**URL:** https://docs.axolotl.ai/docs/api/core.datasets.transforms.chat_builder.html - Network accessline 4653
**URL:** https://docs.axolotl.ai/docs/api/utils.chat_templates.html - Network accessline 4664
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.dpo.trainer.html - Network accessline 4697
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_disk.html - Network accessline 4778
**URL:** https://docs.axolotl.ai/docs/api/utils.samplers.multipack.html - Network accessline 4865
**URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.scheduler.html - Network accessline 4902
**URL:** https://docs.axolotl.ai/docs/api/utils.collators.batching.html - Network accessline 4983
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orcamini.html - Network accessline 4992
Prompt Strategy for finetuning Orca Mini (v2) models see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information - Network accessline 5015
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chat_template.html - Network accessline 5046
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.relora.html - Network accessline 5053
Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune. - Network accessline 5068
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.transformers_fa_utils.html - Network accessline 5078
see https://github.com/huggingface/transformers/pull/35834 - Network accessline 5099
**URL:** https://docs.axolotl.ai/docs/api/utils.collators.mm_chat.html - Network accessline 5130
**URL:** https://docs.axolotl.ai/docs/api/utils.lora.html - Network accessline 5154
**URL:** https://docs.axolotl.ai/docs/api/utils.model_shard_quant.html - Network accessline 5191
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_cpu.html - Network accessline 5216
**URL:** https://docs.axolotl.ai/docs/api/core.builders.base.html - Network accessline 5247
**URL:** https://docs.axolotl.ai/docs/api/core.builders.rl.html - Network accessline 5269
**URL:** https://docs.axolotl.ai/docs/api/utils.schemas.integrations.html - Network accessline 5326
**URL:** https://docs.axolotl.ai/docs/api/utils.data.sft.html - Network accessline 5350
**URL:** https://docs.axolotl.ai/docs/api/integrations.liger.args.html - Network accessline 5374
**URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mixtral.html - Network accessline 5385
**URL:** https://docs.axolotl.ai/docs/api/cli.preprocess.html - Network accessline 5417
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.llama3.html - Network accessline 5457
**URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orpo.chat_template.html - Network accessline 5517
**URL:** https://docs.axolotl.ai/docs/api/loaders.processor.html - Network accessline 5528
**URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.comet_.html
optional-skills/mlops/training/axolotl/references/dataset-formats.md
- Network accessline 9
**URL:** https://docs.axolotl.ai/docs/dataset-formats/tokenized.html - Network accessline 34
**URL:** https://docs.axolotl.ai/docs/dataset-formats/index.html - Network accessline 247
**URL:** https://docs.axolotl.ai/docs/dataset-formats/conversation.html - Network accessline 377
**URL:** https://docs.axolotl.ai/docs/dataset-formats/pretraining.html - Network accessline 411
**URL:** https://docs.axolotl.ai/docs/dataset-formats/template_free.html - Network accessline 502
**URL:** https://docs.axolotl.ai/docs/dataset-formats/ - Network accessline 715
**URL:** https://docs.axolotl.ai/docs/dataset-formats - Network accessline 928
**URL:** https://docs.axolotl.ai/docs/dataset-formats/inst_tune.html - Network accessline 1004
**URL:** https://docs.axolotl.ai/docs/dataset-formats/stepwise_supervised.html
optional-skills/mlops/training/axolotl/references/other.md
- Network accessline 9
**URL:** https://docs.axolotl.ai/docs/mixed_precision.html - Network accessline 95
**URL:** https://docs.axolotl.ai/docs/faq.html - Network accessline 150
A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717. - Network accessline 158
A: We use torch APIs to retrieve this information. You can see https://docs.pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management for more information. - Network accessline 246
**URL:** https://docs.axolotl.ai/docs/installation.html - Network accessline 264
Follow the instructions at: https://pytorch.org/get-started/locally/ - Network accessline 294
Install PyTorch: https://pytorch.org/get-started/locally/ - Dangerous shell operationline 310
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 310
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 332
**URL:** https://docs.axolotl.ai/docs/dataset_preprocessing.html - Network accessline 358
**URL:** https://docs.axolotl.ai/docs/inference.html - Network accessline 421
**URL:** https://docs.axolotl.ai/docs/multimodal.html - Network accessline 535
**URL:** https://docs.axolotl.ai/docs/reward_modelling.html - Network accessline 604
**URL:** https://docs.axolotl.ai/docs/rlhf.html - Network accessline 634
Paper: https://arxiv.org/abs/2403.07691 - Network accessline 724
**URL:** https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 810
**URL:** https://docs.axolotl.ai/docs/quantize.html - Network accessline 864
**URL:** https://docs.axolotl.ai/docs/nccl.html - Network accessline 911
**URL:** https://docs.axolotl.ai/docs/multi-node.html - Network accessline 1001
**URL:** https://docs.axolotl.ai/docs/dataset_loading.html - Access to secretsline 1053
[Credential-related source omitted] - Network accessline 1099
The path should start with https://. - Network accessline 1143
**URL:** https://docs.axolotl.ai/docs/multi-gpu.html - Network accessline 1238
**URL:** https://docs.axolotl.ai/docs/ray-integration.html - Network accessline 1324
**URL:** https://docs.axolotl.ai/docs/sequence_parallelism.html - Network accessline 1391
**URL:** https://docs.axolotl.ai/docs/qat.html - Network accessline 1423
**URL:** https://docs.axolotl.ai/docs/fsdp_qlora.html - Network accessline 1454
**URL:** https://docs.axolotl.ai/docs/custom_integrations.html - Network accessline 1474
See https://github.com/apple/ml-cross-entropy - Network accessline 1520
See https://github.com/ironjr/grokfast - Network accessline 1546
To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation: https://github.com/vllm-project/llm-compressor/blob/main/README.md - Network accessline 1560
https://github.com/vllm-project/llm-compressor - Network accessline 1566
See https://github.com/EleutherAI/lm-evaluation-harness - Network accessline 1572
See https://github.com/linkedin/Liger-Kernel - Network accessline 1580
See https://github.com/cognitivecomputations/spectrum - Network accessline 1598
See this repo for an example: https://github.com/axolotl-ai-cloud/diff-transformer - Network accessline 1609
pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec" - Network accessline 1627
journal = {arXiv}, year = {2024}, url = {https://arxiv.org/abs/2411.09009}, - Network accessline 1628
year = {2024}, url = {https://arxiv.org/abs/2411.09009}, } - Network accessline 1629
url = {https://arxiv.org/abs/2411.09009}, - Network accessline 1637
**URL:** https://docs.axolotl.ai/docs/config-reference.html - Network accessline 1793
process_reward_model: bool | None # Coefficient to incentivize the reward model to output mean-zero rewards (proposed by # https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. - Network accessline 1794
# Coefficient to incentivize the reward model to output mean-zero rewards (proposed by # https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. center_rewards_coefficient: float | None - Network accessline 1795
# https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. - Network accessline 1868
field_messages: str | None # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). - Network accessline 1869
# Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). field_tools: str | None - Network accessline 1870
# schema](https://json-schema.org/learn/getting-started-step-by-step). - Network accessline 2025
field_messages: str | None # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). - Network accessline 2026
# Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). field_tools: str | None - Network accessline 2027
# schema](https://json-schema.org/learn/getting-started-step-by-step). - Network accessline 2209
field_messages: str | None # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). - Network accessline 2210
# Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON # schema](https://json-schema.org/learn/getting-started-step-by-step). field_tools: str | None - Network accessline 2211
# schema](https://json-schema.org/learn/getting-started-step-by-step). - Access to secretsline 2289
[Credential-related source omitted] - Access to secretsline 2291
[Credential-related source omitted] - Network accessline 2358
# Whether to use gradient checkpointing. Available options are: true, false, 'offload', # 'offload_disk'. # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing - Network accessline 2359
# 'offload_disk'. # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing gradient_checkpointing: Literal['offload', 'offload_disk'] | bool | None = False - Network accessline 2360
# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing - Network accessline 2425
# Whether to use xformers attention patch https://github.com/facebookresearch/xformers - Network accessline 2426
xformers_attention: bool | None # Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/ # torch.nn.functional.scaled_dot_product_attention.html - Network accessline 2427
# Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/ - Network accessline 2428
# torch.nn.functional.scaled_dot_product_attention.html sdp_attention: bool | None # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf - Network accessline 2429
sdp_attention: bool | None # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf s2_attention: bool | None - Network accessline 2430
# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf - Network accessline 2432
flex_attention: bool | None flex_attn_compile_kwargs: dict[str, Any] | None # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention - Network accessline 2433
flex_attn_compile_kwargs: dict[str, Any] | None # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention flash_attention: bool | None - Network accessline 2434
# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention - Network accessline 2457
# Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2458
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2459
lora_mlp_kernel: bool | None # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2460
# Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html lora_qkv_kernel: bool | None - Network accessline 2461
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2462
lora_qkv_kernel: bool | None # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2463
# Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html lora_o_kernel: bool | None - Network accessline 2464
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html - Network accessline 2540
# a single GPU's VRAM. E.g., if 4 GPUs are available, set this value to 2 to split each # sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized # subsequences. See https://docs.axolotl.ai/docs/sequence_paral - Network accessline 2541
# sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized # subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more # details. - Network accessline 2542
# subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more - Network accessline 2609
# Logging frequency logging_steps: int | None # Stop training after this many evaluation losses have increased in a row. https://huggi - Network accessline 2610
logging_steps: int | None # Stop training after this many evaluation losses have increased in a row. https://huggi # ngface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppin - Network accessline 2611
# Stop training after this many evaluation losses have increased in a row. https://huggi - Network accessline 2620
use_tensorboard: bool | None # Enable the pytorch profiler to capture the first N steps of training to the # output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more - Network accessline 2621
# Enable the pytorch profiler to capture the first N steps of training to the # output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more # information. Snapshots can be visualized @ https://pytorch.org/memory_viz - Network accessline 2622
# output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more - Network accessline 2623
# information. Snapshots can be visualized @ https://pytorch.org/memory_viz - Network accessline 2631
# throughput of non-padding tokens. include_tkps: bool | None = True # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to - Network accessline 2632
include_tkps: bool | None = True # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to # add noise to embeddings. Currently only supported on Llama and Mistral - Network accessline 2633
# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to - Network accessline 2717
# Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available # plugins or doc below for more details. # https://docs.axolotl.ai/docs/custom_integrations.html - Network accessline 2718
# plugins or doc below for more details. # https://docs.axolotl.ai/docs/custom_integrations.html plugins: list[str] | None - Network accessline 2719
# https://docs.axolotl.ai/docs/custom_integrations.html - Network accessline 2815
# A list of token indices to fine-tune on the `embed_tokens` layer. Otherwise, a dict # mapping an embedding layer name to its trainable token indices. See # https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-trai - Network accessline 2816
# mapping an embedding layer name to its trainable token indices. See # https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train- # tokens-alongside-lora - Network accessline 2817
# https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train- - Access to secretsline 2964
[Credential-related source omitted] - Network accessline 3036
**URL:** https://docs.axolotl.ai - Network accessline 3100
title = {Axolotl: Open Source LLM Post-Training}, author = {{Axolotl maintainers and contributors}}, url = {https://github.com/axolotl-ai-cloud/axolotl}, - Network accessline 3101
author = {{Axolotl maintainers and contributors}}, url = {https://github.com/axolotl-ai-cloud/axolotl}, license = {Apache-2.0}, - Network accessline 3102
url = {https://github.com/axolotl-ai-cloud/axolotl}, - Network accessline 3112
**URL:** https://docs.axolotl.ai/docs/getting-started.html - Network accessline 3219
**URL:** https://docs.axolotl.ai/docs/multipack.html - Network accessline 3291
**URL:** https://docs.axolotl.ai/docs/batch_vs_grad.html - Network accessline 3351
**URL:** https://docs.axolotl.ai/docs/debugging.html - Network accessline 3493
**URL:** https://docs.axolotl.ai/docs/docker.html
optional-skills/mlops/training/unsloth/references/llms-full.md
- Network accessline 5
At [Unsloth](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/), our mission is to make AI as accurate and accessible as possible. Train, run, evaluate and save gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma LLMs 2 - Network accessline 9
<a href="beginner-start-here" class="button primary">Get started</a> <a href="https://github.com/unslothai/unsloth" class="button secondary">Our GitHub</a> - Network accessline 11
[Credential-related source omitted] - Network accessline 14
{% column %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 15
{% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) {% endcontent-ref %} - Network accessline 16
[fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 19
{% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 20
[unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 25
{% column %} {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 26
{% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) {% endcontent-ref %} - Network accessline 27
[all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 30
{% content-ref url="../models/tutorials-how-to-fine-tune-and-run-llms" %} [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms) - Network accessline 31
[tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms) - Network accessline 36
[Credential-related source omitted] - Network accessline 40
* Unsloth streamlines model training locally and on Colab/Kaggle, covering loading, quantization, training, evaluation, saving, exporting, and integration with inference engines like Ollama, llama.cpp, and vLLM. * We directly collaborate wi - Network accessline 41
* We directly collaborate with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_con - Network accessline 42
* Unsloth is the only training framework to support all model types: [vision](https://docs.unsloth.ai/basics/vision-fine-tuning), [text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), BERT, [reinforcement l - Network accessline 46
* Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **8-bit** training. * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speec - Network accessline 47
* The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-n - Network accessline 48
* Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more. Any model that works in transform - Network accessline 49
* **0% loss in accuracy** - no approximation methods - all exact. * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! * Unsloth supports Linux, Windows, Colab, Kagg - Network accessline 50
* [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! - Network accessline 51
* Unsloth supports Linux, Windows, Colab, Kaggle, **NVIDIA** and [**AMD**](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) & **Intel**. See: - Network accessline 53
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 54
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 65
Use our official **Docker image**: `unsloth/unsloth`. Read our [**Docker guide**](https://docs.unsloth.ai/get-started/install-and-update/docker)**.** - Network accessline 67
For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/install-and-update/windows-installation). - Network accessline 69
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 70
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 75
[**Fine-tuning** an LLM](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) customizes its behavior, enhances domain knowledge, and optimizes performance for specific tasks. By fine-tuning a pre-trained model (e.g. Llama-3.1-8B) on - Network accessline 81
[**Reinforcement Learning (RL)**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) is where an "agent" learns to make decisions by interacting with an environment and receiving **feedback** in the form of **rewards** or - Network accessline 95
{% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 96
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 99
{% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 100
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 103
[Credential-related source omitted] - Network accessline 108
If you're a beginner, here might be the first questions you'll ask before your first fine-tune. You can also always ask our community by joining our [Reddit page](https://www.reddit.com/r/unsloth/). - Network accessline 112
[Credential-related source omitted] - Network accessline 121
* **Operating System**: Works on Linux and Windows. * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.u - Network accessline 122
* Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-sp - Network accessline 123
Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20 & 50, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow. - Network accessline 124
* The official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker Hub. - Network accessline 125
* Unsloth works on [AMD](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) and [Intel](https://github.com/unslothai/unsloth/pull/2621) GPUs! Apple/Silicon/MLX is in the works. - Network accessline 140
**For context length benchmarks, see** [**here**](https://docs.unsloth.ai/basics/unsloth-benchmarks#context-length-benchmarks)**.** - Network accessline 222
**Not necessarily.** Many assume RAG will consistently outperform a fine-tuned model, but that’s not the case when fine-tuning is done properly. In fact, a well-tuned model often matches or even surpasses RAG-based systems on specialized ta - Network accessline 228
**Not at all!** While full fine-tuning or pretraining can be costly, these are not necessary (pretraining is especially not necessary). In most cases, LoRA or QLoRA fine-tuning can be done for minimal cost. In fact, with Unsloth’s [free not - Network accessline 247
We recommend starting with **QLoRA**, as it’s one of the most efficient and accessible methods available. Thanks to Unsloth’s [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss compared to standard 16-bit LoRA f - Network accessline 253
{% content-ref url="../fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 254
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 262
Also see our GitHub repo for our notebooks: [github.com/unslothai/notebooks](https://github.com/unslothai/notebooks/) - Network accessline 270
* [**gpt-oss (20b)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) • [Inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\) - Network accessline 271
* [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new** - Network accessline 272
* [Qwen3 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) • [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL - Network accessline 273
* [**Qwen3-2507-4B**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) • [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) • [Instruct](https://colab.r - Network accessline 274
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversation - Network accessline 275
* [IBM Granite-4.0-H](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) - new - Network accessline 276
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Vision](https://colab - Network accessline 277
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)  - Network accessline 278
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Alpaca.ipynb) • [Llama 3.2 (1B + 3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3 - Network accessline 282
* [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) (automatic kernels creation) - new - Network accessline 283
* [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) (auto win 2048 game) - new - Network accessline 284
* [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision **GSPO** - new - Network accessline 285
* [Qwen3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA - Network accessline 286
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 287
* [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecase) - Network accessline 288
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 289
* [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA - Network accessline 290
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 291
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)  - Network accessline 292
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) - Network accessline 296
* [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) - new - Network accessline 297
* [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb) - Network accessline 298
* [Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Speech-to-Text (STT) - Network accessline 299
* [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb) - Network accessline 300
* [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb) - Network accessline 301
* [Oute-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_\(1B\).ipynb) - Network accessline 305
* [Whisper-Large-V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Network accessline 306
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - Audio - Network accessline 310
* [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) **- new** - Network accessline 311
* [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new** - Network accessline 312
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - vision - Network accessline 313
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - vision - Network accessline 314
* [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb) - Network accessline 315
* [Qwen2.5-VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb) - Network accessline 316
* [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb) - Network accessline 317
* [Qwen3-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 318
* [Qwen2.5-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) - Vision GSPO - Network accessline 319
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 329
* [gpt-oss-120b ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(120B\)_A100-Fine-tuning.ipynb)- new - Network accessline 330
* [Qwen3 (32B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(32B\)_A100-Reasoning-Conversational.ipynb) - new - Network accessline 331
* [Llama 3.3 (70B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.3_\(70B\)_A100-Conversational.ipynb) - new - Network accessline 332
* [Gemma 3 (27B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(27B\)_A100-Conversational.ipynb) - new - Network accessline 336
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 337
* [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new** - Network accessline 338
* [**ModernBERT-large**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) **- new** as of Aug 19 - Network accessline 339
* [**Synthetic Data Generation Llama 3.2 (3B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_\(3B\).ipynb) - new - Network accessline 340
* [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new** - Network accessline 341
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 342
* [Mistral v0.3 Instruct (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 343
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) - Network accessline 344
* [ORPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb) - Network accessline 345
* [Continued Pretraining](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 346
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 347
* [***Inference only***](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Inference.ipynb) - Network accessline 348
* [Llama 3 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Alpaca.ipynb) - Network accessline 352
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 353
* [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new** - Network accessline 354
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 355
* [**BERT - Text Classification**](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) **- new as of Aug 19** - Network accessline 356
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) - Network accessline 357
* [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new** - Network accessline 358
* [Continued Pretraining (CPT)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 359
* [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail - Network accessline 360
* [KTO](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) by Jeffrey - Network accessline 361
* [Inference chat UI](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb) - Network accessline 362
* [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 363
* [ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing) - Network accessline 364
* [Text Completion](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb) - Network accessline 368
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 369
* [Gemma 2 (9B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(9B\)-Alpaca.ipynb) - Network accessline 370
* [Mistral NeMo (12B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Nemo_\(12B\)-Alpaca.ipynb) - Network accessline 371
* [Phi-3.5 (mini)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) - Network accessline 372
* [Phi-3 (medium)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3_Medium-Conversational.ipynb) - Network accessline 373
* [Gemma 2 (2B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(2B\)-Alpaca.ipynb) - Network accessline 374
* [Qwen 2.5 Coder (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(14B\)-Conversational.ipynb) - Network accessline 375
* [Mistral Small (22B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_\(22B\)-Alpaca.ipynb) - Network accessline 376
* [TinyLlama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/TinyLlama_\(1.1B\)-Alpaca.ipynb) - Network accessline 377
* [CodeGemma (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_\(7B\)-Conversational.ipynb) - Network accessline 378
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Alpaca.ipynb) - Network accessline 379
* [Qwen2 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_\(7B\)-Alpaca.ipynb) - Network accessline 385
* [**gpt-oss (20B)**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-gpt-oss-\(20B\)-Fine-tuning.ipynb\&accelerator=nvidiaTeslaT4) **- new** - Network accessline 386
* [Gemma 3n (E4B)](https://www.kaggle.com/code/danielhanchen/gemma-3n-4b-multimodal-finetuning-inference) - Network accessline 387
* [Qwen3 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(14B\).ipynb) - Network accessline 388
* [Magistral-2509 (24B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=nvidiaTeslaT4) - new - Network accessline 389
* [Gemma 3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(4B\).ipynb) - Network accessline 390
* [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4-Conversational.ipynb) - Network accessline 391
* [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Alpaca.ipynb) - Network accessline 392
* [Llama 3.2 (1B + 3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 393
* [Qwen 2.5 (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(7B\)-Alpaca.ipynb) - Network accessline 397
* [**Qwen2.5-VL**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_5_7B_VL_GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Vision GRPO - new - Network accessline 398
* [Qwen3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(4B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Network accessline 399
* [Gemma 3 (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 400
* [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 401
* [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4_\(14B\)-GRPO.ipynb) - Network accessline 402
* [Qwen 2.5 (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 406
* [Sesame-CSM (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Sesame_CSM_\(1B\)-TTS.ipynb) - Network accessline 407
* [Orpheus-TTS (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Orpheus_\(3B\)-TTS.ipynb) - Network accessline 408
* [Whisper Large V3](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Whisper.ipynb) – Speech-to-Text - Network accessline 409
* [Llasa-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llasa_TTS_\(1B\).ipynb) - Network accessline 410
* [Spark-TTS (0.5B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Spark_TTS_\(0_5B\).ipynb) - Network accessline 411
* [Oute-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Oute_TTS_\(1B\).ipynb) - Network accessline 415
* [Llama 3.2 Vision (11B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(11B\)-Vision.ipynb) - Network accessline 416
* [Qwen 2.5-VL (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_VL_\(7B\)-Vision.ipynb) - Network accessline 417
* [Pixtral (12B) 2409](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Pixtral_\(12B\)-Vision.ipynb) - Network accessline 421
* [Tool Calling](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb\&accelerator=nvidiaTeslaT4) - Network accessline 422
* [ORPO](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-ORPO.ipynb) - Network accessline 423
* [Continued Pretraining](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 424
* [DPO Zephyr](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Zephyr_\(7B\)-DPO.ipynb) - Network accessline 425
* [Inference only](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Inference.ipynb) - Network accessline 426
* [Ollama](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-Ollama.ipynb) - Network accessline 427
* [Text Completion](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_\(7B\)-Text_Completion.ipynb) - Network accessline 428
* [CodeForces-cot (Reasoning)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb) - Network accessline 429
* [Unsloth Studio (chat UI)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Unsloth_Studio.ipynb) - Network accessline 433
* [Gemma 2 (9B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(9B\)-Alpaca.ipynb) - Network accessline 434
* [Gemma 2 (2B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(2B\)-Alpaca.ipynb) - Network accessline 435
* [CodeGemma (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_\(7B\)-Conversational.ipynb) - Network accessline 436
* [Mistral NeMo (12B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Nemo_\(12B\)-Alpaca.ipynb) - Network accessline 437
* [Mistral Small (22B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Small_\(22B\)-Alpaca.ipynb) - Network accessline 438
* [TinyLlama (1.1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-TinyLlama_\(1.1B\)-Alpaca.ipynb) - Network accessline 440
To view a complete list of all our Kaggle notebooks, [click here](https://github.com/unslothai/notebooks#-kaggle-notebooks). - Network accessline 442
{% hint style="info" %} Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)! - Network accessline 443
Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)! - Network accessline 449
Unsloth model catalog for all our [Dynamic](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) GGUF, 4-bit, 16-bit models on Hugging Face. - Network accessline 459
| Model | Variant | GGUF | Instruct (4-bit) - Network accessline 460
| ------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------- | -------------------------------------- - Network accessline 461
| [**gpt-oss** ](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) | 120b | [link](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 462
| | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 463
| [**DeepSeek-V3.1**](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) | Terminus | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF) | — - Network accessline 464
| | V3.1 | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF) | — - Network accessline 465
| [**Qwen3-VL**](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune) | 2B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 466
| | 2B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 467
| | 4B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 468
| | 4B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 469
| | 8B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 470
| | 8B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 471
| | 30B-A3B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Instruct-GGUF) | — - Network accessline 472
| | 30B-A3B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF) | — - Network accessline 473
| | 32B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 474
| | 32B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 475
| | 235B-A22B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Instruct-GGUF) | — - Network accessline 476
| | 235B-A22B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Thinking-GGUF) | — - Network accessline 477
| [**Qwen3-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) | 30B-A3B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF) | — - Network accessline 478
| | 30B-A3B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF) | — - Network accessline 479
| | 235B-A22B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF/) | — - Network accessline 480
| | 235B-A22B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF/) | — - Network accessline 481
| **Qwen3-Coder** | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF) | — - Network accessline 482
| | 480B-A35B | [link](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF) | — - Network accessline 483
| **Granite-4.0 (new)** | H-Small | [link](https://huggingface.co/unsloth/granite-4.0-h-small-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 484
| **GLM (new)** | 4.6 | [link](https://huggingface.co/unsloth/GLM-4.6-GGUF) | — - Network accessline 485
| | 4.5-Air | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF) | — - Network accessline 486
| **Kimi-K2-0905** | 1T | [link](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF) | — - Network accessline 487
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 488
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 489
| **DeepSeek-R1-0528** | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 490
| | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF) | — - Network accessline 491
| **Mistral** | Magistral Small (2509) | [link](https://huggingface.co/unsloth/Magistral-Small-2509-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 492
| | Magistral Small (2507) | [link](https://huggingface.co/unsloth/Magistral-Small-2507-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 493
| | Small 3.2 24B (2506) | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 494
| FLUX.1 | Kontext-dev | [link](https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF) | — - Network accessline 495
| **Qwen3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 496
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 497
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 498
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 499
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 500
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 501
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 502
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF) | — - Network accessline 503
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 504
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | — - Network accessline 505
| **Grok 2** | 270B | [link](https://huggingface.co/unsloth/grok-2-GGUF) | — - Network accessline 506
| **Qwen-2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF) | — - Network accessline 507
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF) | — - Network accessline 508
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 509
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 513
| Model | Variant | GGUF | Instruct (4-bit) | | ----------------- | ------- - Network accessline 514
| ----------------- | ---------------------- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | **DeepSeek-V3.1** | Terminu - Network accessline 515
| **DeepSeek-V3.1** | Terminus | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF) | | - Network accessline 516
| | V3.1 | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF) | | - Network accessline 517
| **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF) | — | - Network accessline 518
| | V3 | [link](https://huggingface.co/unsloth/DeepSeek-V3-GGUF) | — | - Network accessline 519
| **DeepSeek-R1** | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF) | — | - Network accessline 520
| | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit) | - Network accessline 521
| | R1 | [link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF) | — | - Network accessline 522
| | R1 Zero | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero-GGUF) | — | - Network accessline 523
| | Distill Llama 3 8 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit) | - Network accessline 524
| | Distill Llama 3.3 70 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-bnb-4bit) | - Network accessline 525
| | Distill Qwen 2.5 1.5 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit) | - Network accessline 526
| | Distill Qwen 2.5 7 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit) | - Network accessline 527
| | Distill Qwen 2.5 14 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit) | - Network accessline 528
| | Distill Qwen 2.5 32 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit) | - Network accessline 532
| Model | Variant | GGUF | Instruct (4-bit) | | ------------- | ------------ - Network accessline 533
| ------------- | ------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | **Llama 4** | Scout 17 B-1 - Network accessline 534
| **Llama 4** | Scout 17 B-16 E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) | - Network accessline 535
| | Maverick 17 B-128 E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | — | - Network accessline 536
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit) | - Network accessline 537
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit) | - Network accessline 538
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit) | - Network accessline 539
| | 11 B Vision | — | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit) | - Network accessline 540
| | 90 B Vision | — | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit) | - Network accessline 541
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit) | - Network accessline 542
| | 70 B | — | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit) | - Network accessline 543
| | 405 B | — | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct-bnb-4bit) | - Network accessline 544
| **Llama 3** | 8 B | — | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) | - Network accessline 545
| | 70 B | — | [link](https://huggingface.co/unsloth/llama-3-70b-bnb-4bit) | - Network accessline 546
| **Llama 2** | 7 B | — | [link](https://huggingface.co/unsloth/llama-2-7b-chat-bnb-4bit) | - Network accessline 547
| | 13 B | — | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 548
| **CodeLlama** | 7 B | — | [link](https://huggingface.co/unsloth/codellama-7b-bnb-4bit) | - Network accessline 549
| | 13 B | — | [link](https://huggingface.co/unsloth/codellama-13b-bnb-4bit) | - Network accessline 550
| | 34 B | — | [link](https://huggingface.co/unsloth/codellama-34b-bnb-4bit) | - Network accessline 554
| Model | Variant | GGUF | Instruct (4-bit) | | ------------ | ------------- | -------------------------------- - Network accessline 555
| ------------ | ------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------- | | **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/u - Network accessline 556
| **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit) | - Possible obfuscationline 556
| **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit) | - Network accessline 557
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit) | - Network accessline 558
| **Gemma 3** | 270M | [link](https://huggingface.co/unsloth/gemma-3-270m-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-270m-it) | - Network accessline 559
| | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit) | - Network accessline 560
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) | - Network accessline 561
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-12b-it-unsloth-bnb-4bit) | - Network accessline 562
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-27b-it-unsloth-bnb-4bit) | - Network accessline 563
| **MedGemma** | 4 B (vision) | [link](https://huggingface.co/unsloth/medgemma-4b-it-GGUF) | [link](https://huggingface.co/unsloth/medgemma-4b-it-unsloth-bnb-4bit) | - Network accessline 564
| | 27 B (vision) | [link](https://huggingface.co/unsloth/medgemma-27b-it-GGUF) | [link](https://huggingface.co/unsloth/medgemma-27b-text-it-unsloth-bnb-4bit) | - Network accessline 565
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit) | - Network accessline 566
| | 9 B | — | [link](https://huggingface.co/unsloth/gemma-2-9b-it-bnb-4bit) | - Network accessline 567
| | 27 B | — | [link](https://huggingface.co/unsloth/gemma-2-27b-it-bnb-4bit) | - Network accessline 571
| Model | Variant | GGUF | Instruct (4-bit) | | -------------------------- | ---- - Network accessline 572
| -------------------------- | ---------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | | **Qwen 3** | 0.6 - Network accessline 573
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-unsloth-bnb-4bit) | - Network accessline 574
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit) | - Network accessline 575
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-4B-unsloth-bnb-4bit) | - Network accessline 576
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit) | - Network accessline 577
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit) | - Network accessline 578
| | 30 B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-bnb-4bit) | - Network accessline 579
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-32B-unsloth-bnb-4bit) | - Network accessline 580
| | 235 B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF) | — | - Network accessline 581
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF) | — | - Network accessline 582
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF) | — | - Network accessline 583
| **Qwen 2.5 VL** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit) | - Network accessline 584
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit) | - Network accessline 585
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit) | - Network accessline 586
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-unsloth-bnb-4bit) | - Network accessline 587
| **Qwen 2.5** | 0.5 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit) | - Network accessline 588
| | 1.5 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit) | - Network accessline 589
| | 3 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit) | - Network accessline 590
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-bnb-4bit) | - Network accessline 591
| | 14 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct-bnb-4bit) | - Network accessline 592
| | 32 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct-bnb-4bit) | - Network accessline 593
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct-bnb-4bit) | - Network accessline 594
| **Qwen 2.5 Coder (128 K)** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit) | - Network accessline 595
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit) | - Network accessline 596
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit) | - Network accessline 597
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit) | - Network accessline 598
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit) | - Network accessline 599
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit) | - Network accessline 600
| **QwQ** | 32 B | [link](https://huggingface.co/unsloth/QwQ-32B-GGUF) | [link](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit) | - Network accessline 601
| **QVQ (preview)** | 72 B | — | [link](https://huggingface.co/unsloth/QVQ-72B-Preview-bnb-4bit) | - Network accessline 602
| **Qwen 2 (chat)** | 1.5 B | — | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct-bnb-4bit) | - Network accessline 603
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct-bnb-4bit) | - Network accessline 604
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct-bnb-4bit) | - Network accessline 605
| **Qwen 2 VL** | 2 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit) | - Network accessline 606
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit) | - Network accessline 607
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct-bnb-4bit) | - Network accessline 611
<table><thead><tr><th width="174">Model</th><th>Variant</th><th>GGUF</th><th>Instruct (4-bit)</th></tr></thead><tbody><tr><td><strong>Mistral Small</strong></td><td>3.2-24 B (2506)</td><td><a href="https://huggingface.co/unsloth/Mistral-Sma - Network accessline 615
| Model | Variant | GGUF | Instruct (4-bit) | | ----------- | ---------------- | ------------------------ - Network accessline 616
| ----------- | ---------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](https://huggingfa - Network accessline 617
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-unsloth-bnb-4bit) | - Network accessline 618
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF) | [link](https://huggingface.co/unsloth/phi-4-reasoning-unsloth-bnb-4bit) | - Network accessline 619
| | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit) | - Network accessline 620
| | Phi-4 (instruct) | [link](https://huggingface.co/unsloth/phi-4-GGUF) | [link](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit) | - Network accessline 621
| | mini (instruct) | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit) | - Network accessline 622
| **Phi-3.5** | mini | — | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct-bnb-4bit) | - Network accessline 623
| **Phi-3** | mini | — | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit) | - Network accessline 624
| | medium | — | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct-bnb-4bit) | - Network accessline 628
| Model | Variant | GGUF | Instruct (4-bit) | | -------------- | ----------------- | ----- - Network accessline 629
| -------------- | ----------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | GLM | 4.5-Air | [link - Network accessline 630
| GLM | 4.5-Air | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF) | | - Network accessline 631
| | 4.5 | [4.5](https://huggingface.co/unsloth/GLM-4.5-GGUF) | | - Network accessline 632
| | 4-32B-0414 | [4-32B-0414](https://huggingface.co/unsloth/GLM-4-32B-0414-GGUF) | | - Network accessline 633
| Hunyuan | A13B | [link](https://huggingface.co/unsloth/Hunyuan-A13B-Instruct-GGUF) | — | - Network accessline 634
| Orpheus | 0.1-ft (3B) | [link](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit) | - Network accessline 635
| **LLava** | 1.5 (7 B) | — | [link](https://huggingface.co/unsloth/llava-1.5-7b-hf-bnb-4bit) | - Network accessline 636
| | 1.6 Mistral (7 B) | — | [link](https://huggingface.co/unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit) | - Network accessline 637
| **TinyLlama** | Chat | — | [link](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit) | - Network accessline 638
| **SmolLM 2** | 135 M | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-bnb-4bit) | - Network accessline 639
| | 360 M | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-bnb-4bit) | - Network accessline 640
| | 1.7 B | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-bnb-4bit) | - Network accessline 641
| **Zephyr-SFT** | 7 B | — | [link](https://huggingface.co/unsloth/zephyr-sft-bnb-4bit) | - Network accessline 642
| **Yi** | 6 B (v1.5) | — | [link](https://huggingface.co/unsloth/Yi-1.5-6B-bnb-4bit) | - Network accessline 643
| | 6 B (v1.0) | — | [link](https://huggingface.co/unsloth/yi-6b-bnb-4bit) | - Network accessline 644
| | 34 B (chat) | — | [link](https://huggingface.co/unsloth/yi-34b-chat-bnb-4bit) | - Network accessline 645
| | 34 B (base) | — | [link](https://huggingface.co/unsloth/yi-34b-bnb-4bit) | - Network accessline 653
| Model | Variant | Instruct (16-bit) | | -------------------- | ---------------------- | --------------------------------------------------------------- - Network accessline 654
| -------------------- | ---------------------- | -------------------------------------------------------------------------- | | **gpt-oss** (new) | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b) - Network accessline 655
| **gpt-oss** (new) | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b) | - Network accessline 656
| | 120b | [link](https://huggingface.co/unsloth/gpt-oss-120b) | - Network accessline 657
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | - Network accessline 658
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) | - Network accessline 659
| **DeepSeek-R1-0528** | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B) | - Network accessline 660
| | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528) | - Network accessline 661
| **Mistral** | Small 3.2 24B (2506) | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506) | - Network accessline 662
| | Small 3.1 24B (2503) | [link](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503) | - Network accessline 663
| | Small 3.0 24B (2501) | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501) | - Network accessline 664
| | Magistral Small (2506) | [link](https://huggingface.co/unsloth/Magistral-Small-2506) | - Network accessline 665
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | - Network accessline 666
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B) | - Network accessline 667
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B) | - Network accessline 668
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B) | - Network accessline 669
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B) | - Network accessline 670
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B) | - Network accessline 671
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B) | - Network accessline 672
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B) | - Network accessline 673
| **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | - Network accessline 674
| | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) | - Network accessline 675
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B) | - Network accessline 676
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B) | - Network accessline 677
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | - Network accessline 678
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning) | - Network accessline 682
| Model | Variant | Instruct (16-bit) | | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSe - Network accessline 683
| --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324) | | - Network accessline 684
| **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324) | - Network accessline 685
| | V3 | [link](https://huggingface.co/unsloth/DeepSeek-V3) | - Network accessline 686
| **DeepSeek-R1** | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528) | - Network accessline 687
| | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B) | - Network accessline 688
| | R1 | [link](https://huggingface.co/unsloth/DeepSeek-R1) | - Network accessline 689
| | R1 Zero | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero) | - Network accessline 690
| | Distill Llama 3 8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) | - Network accessline 691
| | Distill Llama 3.3 70B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B) | - Network accessline 692
| | Distill Qwen 2.5 1.5B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B) | - Network accessline 693
| | Distill Qwen 2.5 7B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B) | - Network accessline 694
| | Distill Qwen 2.5 14B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B) | - Network accessline 695
| | Distill Qwen 2.5 32B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B) | - Network accessline 699
| Family | Variant | Instruct (16-bit) | | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4* - Network accessline 700
| ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | | - Network accessline 701
| **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | - Network accessline 702
| | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) | - Network accessline 703
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct) | - Network accessline 704
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) | - Network accessline 705
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) | - Network accessline 706
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct) | - Network accessline 707
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct) | - Network accessline 708
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) | - Network accessline 709
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct) | - Network accessline 710
| | 405 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct) | - Network accessline 711
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct) | - Network accessline 712
| | 70 B | [link](https://huggingface.co/unsloth/llama-3-70b-Instruct) | - Network accessline 713
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b-chat) | - Network accessline 717
| Model | Variant | Instruct (16-bit) | | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3 - Network accessline 718
| ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | | | E4B | [link](https://huggingface.co/unsloth/gemma-3 - Network accessline 719
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | - Network accessline 720
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) | - Network accessline 721
| **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-it) | - Network accessline 722
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-it) | - Network accessline 723
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-it) | - Network accessline 724
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-it) | - Network accessline 725
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2b-it) | - Network accessline 726
| | 9 B | [link](https://huggingface.co/unsloth/gemma-9b-it) | - Network accessline 727
| | 27 B | [link](https://huggingface.co/unsloth/gemma-27b-it) | - Network accessline 731
| Family | Variant | Instruct (16-bit) | | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3 - Network accessline 732
| ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | | - Network accessline 733
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | - Network accessline 734
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B) | - Network accessline 735
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B) | - Network accessline 736
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B) | - Network accessline 737
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B) | - Network accessline 738
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B) | - Network accessline 739
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B) | - Network accessline 740
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B) | - Network accessline 741
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B) | - Network accessline 742
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B) | - Network accessline 743
| **Qwen 2.5 VL** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct) | - Network accessline 744
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct) | - Network accessline 745
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct) | - Network accessline 746
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct) | - Network accessline 747
| **Qwen 2.5** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct) | - Network accessline 748
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) | - Network accessline 749
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct) | - Network accessline 750
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct) | - Network accessline 751
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) | - Network accessline 752
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct) | - Network accessline 753
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct) | - Network accessline 754
| **Qwen 2.5 Coder 128 K** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K) | - Network accessline 755
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K) | - Network accessline 756
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K) | - Network accessline 757
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K) | - Network accessline 758
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K) | - Network accessline 759
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K) | - Network accessline 760
| **QwQ** | 32 B | [link](https://huggingface.co/unsloth/QwQ-32B) | - Network accessline 761
| **QVQ (preview)** | 72 B | — | | **Qwen 2 (Chat)** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) | | - Network accessline 762
| **Qwen 2 (Chat)** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) | - Network accessline 763
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct) | - Network accessline 764
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct) | - Network accessline 765
| **Qwen 2 VL** | 2 B | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct) | - Network accessline 766
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct) | - Network accessline 767
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct) | - Network accessline 771
| Model | Variant | Instruct (16-bit) | | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral** | Small - Network accessline 772
| ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral** | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | | **Mistral** | Large - Network accessline 773
| **Mistral** | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | - Network accessline 774
| **Mistral** | Large 2407 | [link](https://huggingface.co/unsloth/Mistral-Large-Instruct-2407) | - Network accessline 775
| **Mistral** | 7B v0.3 | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3) | - Network accessline 776
| **Mistral** | 7B v0.2 | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) | - Network accessline 777
| **Pixtral** | 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-2409) | - Network accessline 778
| **Mixtral** | 8×7B | [link](https://huggingface.co/unsloth/Mixtral-8x7B-Instruct-v0.1) | - Network accessline 779
| **Mistral NeMo** | 12B 2407 | [link](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) | - Network accessline 780
| **Devstral** | Small 2505 | [link](https://huggingface.co/unsloth/Devstral-Small-2505) | - Network accessline 784
| Model | Variant | Instruct (16-bit) | | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](ht - Network accessline 785
| ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | | | Reasoning | [link](ht - Network accessline 786
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | - Network accessline 787
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning) | - Network accessline 788
| | Phi-4 (core) | [link](https://huggingface.co/unsloth/Phi-4) | - Network accessline 789
| | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning) | - Network accessline 790
| | Mini | [link](https://huggingface.co/unsloth/Phi-4-mini) | - Network accessline 791
| **Phi-3.5** | Mini | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) | - Network accessline 792
| **Phi-3** | Mini | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) | - Network accessline 793
| | Medium | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) | - Network accessline 797
| Model | Instruct (16-bit) | | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft) | [link](https://huggingfac - Network accessline 798
| ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft) | | Orpheus-3B (v0.1 pt) | [link](https://huggingfac - Network accessline 799
| Orpheus-3B (v0.1 ft) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft) | - Network accessline 800
| Orpheus-3B (v0.1 pt) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | - Network accessline 801
| Sesame-CSM 1B | [link](https://huggingface.co/unsloth/csm-1b) | - Network accessline 802
| Whisper Large V3 (STT) | [link](https://huggingface.co/unsloth/whisper-large-v3) | - Network accessline 803
| Llasa-TTS 1B | [link](https://huggingface.co/unsloth/Llasa-1B) | - Network accessline 804
| Spark-TTS 0.5B | [link](https://huggingface.co/unsloth/Spark-TTS-0.5B) | - Network accessline 805
| Oute-TTS 1B | [link](https://huggingface.co/unsloth/Llama-OuteTTS-1.0-1B) | - Network accessline 813
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------ | ----------------- | ---------- - Network accessline 814
| ------------ | ----------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | **Gemma 3n** | E2B | [link](htt - Network accessline 815
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E2B) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-unsloth-bnb-4bit) | - Network accessline 816
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B) | [link](https://huggingface.co/unsloth/gemma-3n-E4B-unsloth-bnb-4bit) | - Network accessline 817
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit) | - Network accessline 818
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit) | - Network accessline 819
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-Base) | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit) | - Network accessline 820
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-Base) | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit) | - Network accessline 821
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-Base) | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit) | - Network accessline 822
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-bnb-4bit) | - Network accessline 823
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) | - Network accessline 824
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 828
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------- | ----------------- | ----------------------------------- - Network accessline 829
| ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unslo - Network accessline 830
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | — | - Network accessline 831
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 832
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B) | — | - Network accessline 833
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B) | — | - Network accessline 834
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B) | — | - Network accessline 835
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision) | — | - Network accessline 836
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision) | — | - Network accessline 837
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) | — | - Network accessline 838
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B) | — | - Network accessline 839
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b) | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) | - Network accessline 840
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b) | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit) | - Network accessline 841
| | 13 B | [link](https://huggingface.co/unsloth/llama-2-13b) | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 845
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------ | ------- | ------------------------------------------------- - Network accessline 846
| ------------ | ------- | --------------------------------------------------------- | -------------------------------------------------------------------------- | | **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B- - Network accessline 847
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit) | - Network accessline 848
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit) | - Network accessline 849
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-Base) | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit) | - Network accessline 850
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-Base) | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit) | - Network accessline 851
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-Base) | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit) | - Network accessline 852
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-unsloth-bnb-4bit) | - Network accessline 853
| **Qwen 2.5** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B) | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-bnb-4bit) | - Network accessline 854
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B) | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-bnb-4bit) | - Network accessline 855
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-3B) | [link](https://huggingface.co/unsloth/Qwen2.5-3B-bnb-4bit) | - Network accessline 856
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-7B) | [link](https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit) | - Network accessline 857
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-14B) | [link](https://huggingface.co/unsloth/Qwen2.5-14B-bnb-4bit) | - Network accessline 858
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-32B) | [link](https://huggingface.co/unsloth/Qwen2.5-32B-bnb-4bit) | - Network accessline 859
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-72B) | [link](https://huggingface.co/unsloth/Qwen2.5-72B-bnb-4bit) | - Network accessline 860
| **Qwen 2** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B) | [link](https://huggingface.co/unsloth/Qwen2-1.5B-bnb-4bit) | - Network accessline 861
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-7B) | [link](https://huggingface.co/unsloth/Qwen2-7B-bnb-4bit) | - Network accessline 865
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------- | ----------------- | ----------------------------------- - Network accessline 866
| ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unslo - Network accessline 867
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | — | - Network accessline 868
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 869
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B) | — | - Network accessline 870
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B) | — | - Network accessline 871
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B) | — | - Network accessline 872
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision) | — | - Network accessline 873
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision) | — | - Network accessline 874
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) | — | - Network accessline 875
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B) | — | - Network accessline 876
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b) | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) | - Network accessline 877
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b) | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit) | - Network accessline 878
| | 13 B | [link](https://huggingface.co/unsloth/llama-2-13b) | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 882
| Model | Variant | Base (16-bit) | Base (4-bit) | | ----------- | ------- | ----------------------------------------------------- | --- - Network accessline 883
| ----------- | ------- | ----------------------------------------------------- | ---------------------------------------------------------------------- | | **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-pt) | [li - Network accessline 884
| **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-pt) | [link](https://huggingface.co/unsloth/gemma-3-1b-pt-unsloth-bnb-4bit) | - Network accessline 885
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-pt) | [link](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-bnb-4bit) | - Network accessline 886
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-pt) | [link](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit) | - Network accessline 887
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-pt) | [link](https://huggingface.co/unsloth/gemma-3-27b-pt-unsloth-bnb-4bit) | - Network accessline 888
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2-2b) | — | - Network accessline 889
| | 9 B | [link](https://huggingface.co/unsloth/gemma-2-9b) | — | - Network accessline 890
| | 27 B | [link](https://huggingface.co/unsloth/gemma-2-27b) | — | - Network accessline 894
| Model | Variant | Base (16-bit) | Base (4-bit) | | ----------- | ---------------- | ----------------------------------- - Network accessline 895
| ----------- | ---------------- | ------------------------------------------------------------------ | --------------------------------------------------------------- | | **Mistral** | Small 24B 2501 | [link](https://huggingface.co/unslo - Network accessline 896
| **Mistral** | Small 24B 2501 | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) | — | - Network accessline 897
| | NeMo 12B 2407 | [link](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) | — | - Network accessline 898
| | 7B v0.3 | [link](https://huggingface.co/unsloth/mistral-7b-v0.3) | [link](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) | - Network accessline 899
| | 7B v0.2 | [link](https://huggingface.co/unsloth/mistral-7b-v0.2) | [link](https://huggingface.co/unsloth/mistral-7b-v0.2-bnb-4bit) | - Network accessline 900
| | Pixtral 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-Base-2409) | — | - Network accessline 904
| Model | Variant | Base (16-bit) | Base (4-bit) | | -------------- | -------------- | ----------------- - Network accessline 905
| -------------- | -------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **TinyLlama** | 1.1 B (Base) | [link](https://hu - Network accessline 906
| **TinyLlama** | 1.1 B (Base) | [link](https://huggingface.co/unsloth/tinyllama) | [link](https://huggingface.co/unsloth/tinyllama-bnb-4bit) | - Network accessline 907
| **Orpheus-3b** | 0.1-pretrained | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained-unsloth-bnb-4bit) | - Network accessline 916
Unsloth works on Linux, Windows, NVIDIA, AMD, Google Colab and more. See our [system requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements). - Network accessline 939
<pre class="language-bash"><code class="lang-bash">pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 940
<strong>pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 949
'2025.1.5' is one of the previous old versions of Unsloth. Change it to a specific release listed on our [Github here](https://github.com/unslothai/unsloth/releases). - Network accessline 967
pip uninstall unsloth unsloth_zoo -y && pip install --no-deps git+https://github.com/unslothai/unsloth_zoo.git && pip install --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 981
pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 982
pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 989
{% hint style="warning" %} Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install). - Network accessline 990
Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install). - Network accessline 1000
pip install --upgrade pip pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1001
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1007
pip install --upgrade pip pip install "unsloth[cu124-torch250] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1008
pip install "unsloth[cu124-torch250] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1014
pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1015
pip install "unsloth[cu118-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1016
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1017
pip install "unsloth[cu118-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1019
pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1020
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1022
pip install "unsloth[cu121-torch250] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1023
pip install "unsloth[cu124-ampere-torch250] @ git+https://github.com/unslothai/unsloth.git" - Dangerous shell operationline 1029
wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python - - Network accessline 1029
wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python - - Network accessline 1049
else: raise RuntimeError(f"Torch = {v} too new!") x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "") print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"') - Network accessline 1051
print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"') - Network accessline 1061
Unsloth Docker image: [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) - Network accessline 1074
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\ - Network accessline 1075
Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation): - Dangerous shell operationline 1078
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 1085
[Credential-related source omitted] - Network accessline 1092
[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate one needed. - Network accessline 1102
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 1111
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If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 1124
[Credential-related source omitted] - Access to secretsline 1139
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* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 1229
Docker might be the easiest way for Windows users to get started with Unsloth as there is no setup needed or dependency issues. [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell - Network accessline 1231
For installation instructions, please follow our [Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker), otherwise here is a quickstart guide: - Network accessline 1238
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other). Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/ins - Dangerous shell operationline 1241
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 1254
[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. - Network accessline 1270
Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. Access the `unsloth-notebooks` tabs to see Unsloth notebooks. - Network accessline 1277
If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 1291
You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx) - Network accessline 1299
* Launch the Installer here: [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/) - Network accessline 1344
Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive). - Network accessline 1346
Then install Miniconda (which has Python) here: [https://www.anaconda.com/docs/getting-started/miniconda/install](https://www.anaconda.com/docs/getting-started/miniconda/install#quickstart-install-instructions) - Network accessline 1352
You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully. [Install PyTorch](https://pytorch.org/get-started/locally/) - Network accessline 1361
pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1375
* Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12) - Network accessline 1389
1. Install `torch` and `triton`. Go to <https://pytorch.org> to install it. For example `pip install torch torchvision torchaudio triton` - Network accessline 1390
2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers. 3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succee - Network accessline 1391
3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to <https://github.com/facebookresearch/xformers>. Another option is to install `fl - Network accessline 1392
4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-m - Network accessline 1399
1. **Install NVIDIA CUDA Toolkit**: * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 1400
* Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 1402
* **Note**: No additional setup is required after installation for Unsloth. 2. **Install Microsoft C++ Build Tools**: * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.mic - Network accessline 1403
2. **Install Microsoft C++ Build Tools**: * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). * During installation, select the ** - Network accessline 1404
* Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). - Network accessline 1421
* Verify: Open a new terminal and type `cl`. It should show version info. 4. **Install Conda** 1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 1422
4. **Install Conda** 1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) 2. Follow installation instruction from the website - Network accessline 1423
1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 1429
1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**. - Network accessline 1459
1. Install python though [Python's official site](https://www.python.org/downloads/windows/). - Dangerous shell operationline 1471
sudo apt update && sudo apt upgrade -y - Dangerous shell operationline 1477
sudo apt install python3-pip - Network accessline 1527
Install the latest PyTorch, TorchAO, Xformers from <https://pytorch.org/> - Network accessline 1532
pip install --upgrade torch==2.8.0 pytorch-triton-rocm torchvision torchaudio torchao==0.13.0 xformers --index-url https://download.pytorch.org/whl/rocm6.4 - Network accessline 1546
pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 1547
pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 1548
pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth" - Network accessline 1555
And that's it! Try some examples in our [**Unsloth Notebooks**](https://docs.unsloth.ai/get-started/unsloth-notebooks) page! - Network accessline 1559
You can use our :ledger:[gpt-oss RL auto win 2048](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game_BF16.ipynb) example on a MI300X (192GB) GPU. The goal is to play the 2048 game automatic - Network accessline 1564
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You can also use our :ledger:[automatic kernel gen RL notebook](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_GRPO_BF16.ipynb) also with gpt-oss to auto create matrix multiplication kernels in Python. The notebook also - Network accessline 1606
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AMD GPUs also need the bitsandbytes `blocksize` to be 128 and not 64 - this also means our pre-quantized models (for example [unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)) fr - Network accessline 1625
{% hint style="warning" %} Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install). - Network accessline 1626
Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install). - Network accessline 1642
If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below: - Network accessline 1645
mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Network accessline 1646
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Dangerous shell operationline 1648
rm -rf ~/miniconda3/miniconda.sh - Network accessline 1658
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max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any! # Get LAION dataset url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" - Network accessline 1680
# Get LAION dataset url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" dataset = load_dataset("json", data_files = {"train" : url}, split = "train") - Network accessline 1681
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" - Network accessline 1688
] # More models at https://huggingface.co/unsloth - Network accessline 1735
# Go to https://docs.unsloth.ai for advanced tips like - Possible obfuscationline 1752
* <U+200B>**DeepSeek-R1-Distill-Llama-8B** is a fine-tuned version of Llama-3.1-8B. DeepSeek utilized data generated by DeepSeek-R1, to fine-tune Llama-3.1-8B. This process, known as distillation (a subcategory of fine-tuning), injects the data in - Network accessline 1754
With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune for free on Colab, Kaggle, or locally with just 3GB VRAM by using our [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). By fine-tuning a pre-trained m - Network accessline 1770
You may have heard that fine-tuning does not make a model learn new knowledge or RAG performs better than fine-tuning. That is **false**. Read more FAQ + misconceptions [here](https://docs.unsloth.ai/beginner-start-here/faq-+-is-fine-tuning - Network accessline 1772
{% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 1773
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 1783
[Credential-related source omitted] - Network accessline 1787
We recommend starting with **Instruct models**, as they allow direct fine-tuning using conversational chat templates (ChatML, ShareGPT etc.) and require less data compared to **Base models** (which uses Alpaca, Vicuna etc). Learn more about - Network accessline 1789
* Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi - Network accessline 1801
We recommend starting with QLoRA, as it is one of the most accessible and effective methods for training models. Our [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss for QLoRA compared to LoRA is now largely r - Network accessline 1803
You can also do [Text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [reasoning (GRPO)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [vision](https://docs.unsloth.ai/basics/vision-f - Network accessline 1807
{% content-ref url="fine-tuning-llms-guide/what-model-should-i-use" %} [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 1808
[what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 1815
* You will need to create a dataset usually with 2 columns - question and answer. The quality and amount will largely reflect the end result of your fine-tune so it's imperative to get this part right. * You can [synthetically generate data - Network accessline 1816
* You can [synthetically generate data](https://docs.unsloth.ai/get-started/datasets-guide#synthetic-data-generation) and structure your dataset (into QA pairs) using ChatGPT or local LLMs. - Network accessline 1817
* You can also use our new Synthetic Dataset notebook which automatically parses documents (PDFs, videos etc.), generates QA pairs and auto cleans data using local models like Llama 3.2. [Access the notebook here.](https://colab.research.go - Network accessline 1823
{% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 1824
[datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 1827
For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which - Network accessline 1831
Learn how to choose the right [hyperparameters](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) using best practices from research and real-world experiments - and understand how each one affects your - Network accessline 1835
{% content-ref url="fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 1836
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 1841
We would recommend beginners to utilise our pre-made [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) first as it's the easiest way to get started with guided steps. However, if installing locally is a must, you can instal - Network accessline 1843
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 1844
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 1849
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 1850
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 1859
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For testing, you can also take 20% of your training data and use that for testing. If you already used all of the training data, then you have to manually evaluate it. You can also use automatic eval tools like EleutherAI’s [lm-evaluation- - Network accessline 1876
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{% content-ref url="../basics/running-and-saving-models" %} [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 1889
[running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 1892
We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingfac - Network accessline 1894
[Credential-related source omitted] - Network accessline 1898
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To learn more about fine-tuning tips and tricks, head over to our blogs which provide tremendous and educational value: <https://unsloth.ai/blog/> - Network accessline 1906
If you need any help on fine-tuning, you can also join our Discord server [here](https://discord.gg/unsloth) or [Reddit r/unsloth](https://www.reddit.com/r/unsloth/). Thanks for reading and hopefully this was helpful! - Network accessline 1908
[Credential-related source omitted] - Network accessline 1922
* E.g. For image-based training, select a vision model such as *Llama 3.2 Vision*. For code datasets, opt for a specialized model like *Qwen Coder 2.5*. * **Licensing and Requirements**: Different models may have specific licensing terms an - Network accessline 1923
* **Licensing and Requirements**: Different models may have specific licensing terms and [system requirements](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#system-requirements). Be sure to review these carefully to avoid - Network accessline 1930
* Use our [VRAM guideline](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#approximate-vram-requirements-based-on-model-parameters) to determine the VRAM requirements for the model you’re considering. - Network accessline 1938
* We recommend using the latest model for the best performance and capabilities. For instance, as of January 2025, the leading 70B model is *Llama 3.3*. * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get- - Network accessline 1939
* You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-started/all-our-models) to find the newest and relevant options. - Network accessline 1960
Base models, on the other hand, are the original pre-trained versions without instruction fine-tuning. These are specifically designed for customization through fine-tuning, allowing you to adapt them to your unique needs. Base models are c - Network accessline 1967
* **300–1,000 Rows of High-Quality Data**: With a medium-sized, high-quality dataset, fine-tuning the base or instruct model are both viable options. * **Less than 300 Rows**: For smaller datasets, the instruct model is typically the better - Network accessline 1969
* For information how how big your dataset should be, [see here](https://docs.unsloth.ai/get-started/datasets-guide#how-big-should-my-dataset-be) - Network accessline 1977
* Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi - Network accessline 1996
One of the key parts of creating a dataset is your [chat template](https://docs.unsloth.ai/basics/chat-templates) and how you are going to design it. Tokenization is also important as it breaks text into tokens, which can be words, sub-word - Network accessline 2019
* Chat-based dialogues (Q\&A, learn a new language, customer support, conversations). * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification. - Network accessline 2020
* Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb), summarization, generation tasks). - Network accessline 2049
For [**continued pretraining**](https://docs.unsloth.ai/basics/continued-pretraining), we use raw text format without specific structure: - Network accessline 2057
If we are adapting a model to a new task, and intend for the model to output text in a single turn based on a specific set of instructions, we can use **Instruction** format in [Alpaca style](https://docs.unsloth.ai/basics/tutorial-how-to-f - Network accessline 2189
<mark style="color:green;">**A:**</mark> If your dataset is already formatted in the Alpaca format, then follow the formatting steps as shown in the Llama3.1 [notebook ](https://colab.research.google.com/github/unslothai/notebooks/blob/mai - Network accessline 2197
<mark style="color:green;">**A:**</mark> The `chat_template` attribute when a model is first uploaded by the original model owners sometimes contains errors and may take time to be updated. In contrast, at Unsloth, we thoroughly check and - Network accessline 2201
<mark style="color:green;">**A:**</mark> Submit a feature request on the unsloth github issues [forum](https://github.com/unslothai/unsloth). As a temporary workaround, you could also use the tokenizer's own apply\_chat\_template function - Network accessline 2207
* Produce entirely new data - either from scratch or from your existing dataset * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) - Network accessline 2208
* Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) and become too specific - Network accessline 2213
We collaborated with Meta to launch a free notebook for creating Synthetic Datasets automatically using local models like Llama 3.2. [Access the notebook here.](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_ - Network accessline 2260
If you want to fine-tune a model that already has reasoning capabilities like the distilled versions of DeepSeek-R1 (e.g. DeepSeek-R1-Distill-Llama-8B), you will need to still follow question/task and answer pairs however, for your answer y - Network accessline 2262
For a model that does not have reasoning and you want to train it so that it later encompasses reasoning capabilities, you will need to utilize a standard dataset but this time without reasoning in its answers. This is training process is k - Network accessline 2268
* Standardize the format of all datasets, combine them into a single dataset, and fine-tune on this unified dataset. * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebo - Network accessline 2269
* Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebook to fine-tune on multiple datasets directly. - Network accessline 2281
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You can access the GPT4 version of the Alpaca dataset [here](https://huggingface.co/datasets/vicgalle/alpaca-gpt4.). Below shows some examples of the dataset: - Network accessline 2287
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The dataset for fine-tuning a vision or multimodal model also includes image inputs. For example, the [Llama 3.2 Vision Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scroll - Network accessline 2365
We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and - Network accessline 2376
| Image - Network accessline 2378
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For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX). - Network accessline 2506
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* **LoRA:** 16-bit fine-tuning. It's slightly faster and slightly more accurate, but consumes significantly more VRAM (4× more than QLoRA). Recommended for 16-bit environments and scenarios where maximum accuracy is required. * **QLoRA:** 4 - Network accessline 2537
:sloth: *70B LLaMA fits in <48GB VRAM with QLoRA in Unsloth -* [*more details here*](https://unsloth.ai/blog/llama3-3)*.* - Network accessline 2581
[Read our blog post](https://unsloth.ai/blog/gradient) for more details. - Network accessline 2585
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A regularization technique that helps [prevent overfitting](#overfitting-poor-generalization-too-specialized) by randomly setting a fraction of the LoRA activations to zero during each training step. [Recent research suggests](https://arxiv - Network accessline 2633
Options are `True`, `False`, and `"unsloth"`. \ 🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*]( - Network accessline 2634
🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](https://unsloth.ai/blog/long-context)*.*\\ - Network accessline 2646
An advanced feature that implements [**Rank-Stabilized LoRA**](https://arxiv.org/abs/2312.03732). If set to `True`, the effective scaling becomes `lora_alpha / sqrt(r)` instead of the standard `lora_alpha / r`. This can sometimes improve st - Network accessline 2652
An advanced technique, as proposed in [**LoftQ**](https://arxiv.org/abs/2310.08659), initializes LoRA matrices with the top 'r' singular vectors from the pretrained weights. This can improve accuracy but may cause a significant memory spike - Network accessline 2656
When validating that **LoRA** adapter weights have been updated after fine-tuning, avoid using **np.allclose()** for comparison. This method can miss subtle but meaningful changes, particularly in **LoRA A**, which is initialized with small - Network accessline 2677
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According to the [rsLoRA (rank stabilized lora) paper](https://arxiv.org/abs/2312.03732), we should instead scale alpha by the sqrt of the rank. Other options exist, but theoretically this is the optimum. The left plot shows other ranks and - Network accessline 2704
According to empirical experiments and research papers like the original [QLoRA paper](https://arxiv.org/pdf/2305.14314), it's best to apply LoRA to both attention and MLP layers. - Network accessline 2709
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The [QLoRA paper](https://arxiv.org/pdf/2305.14314) shows that masking out inputs and **training only on completions** (outputs or assistant messages) can further **increase accuracy** by a few percentage points (*1%*). Below demonstrates h - Network accessline 2751
The QLoRA paper states that **training on completions only** increases accuracy by quite a bit, especially for multi-turn conversational finetunes! We do this in our [conversational notebooks here](https://colab.research.google.com/github/u - Network accessline 2753
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***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 2829
By the end of this tutorial, you will create a custom chatbot by **finetuning Llama-3** with [**Unsloth**](https://github.com/unslothai/unsloth) for free. It can run locally via [**Ollama**](https://github.com/ollama/ollama) on your PC, or - Network accessline 2831
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**Unsloth** makes finetuning much easier, and can automatically export the finetuned model to **Ollama** with integrated automatic `Modelfile` creation! If you need help, you can join our Discord server: <https://discord.com/invite/unsloth> - Network accessline 2835
{% hint style="warning" %} **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You c - Network accessline 2836
**If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You can use it directly there or - Network accessline 2841
[Unsloth](https://github.com/unslothai/unsloth) makes finetuning LLMs like Llama-3, Mistral, Phi-3 and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy! We will be using Google Colab which provides a free GPU during - Network accessline 2843
* [Ollama Llama-3 Alpaca](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) (notebook which we will be using) - Network accessline 2844
* [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing) - Network accessline 2848
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[Ollama ](https://github.com/ollama/ollama)allows you to run language models from your own computer in a quick and simple way! It quietly launches a program which can run a language model like Llama-3 in the background. If you suddenly want - Network accessline 2854
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Options include `True`, `False` and `"unsloth"`. We suggest `"unsloth"` since we reduce memory usage by an extra 30% and support extremely long context finetunes.You can read up here: <https://unsloth.ai/blog/long-context> for more details. - Network accessline 2957
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You can access the GPT4 version of the Alpaca dataset here: <https://huggingface.co/datasets/vicgalle/alpaca-gpt4>. An older first version of the dataset is here: <https://github.com/tatsu-lab/stanford_alpaca>. Below shows some examples of - Network accessline 2963
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To access the Titanic finetuning notebook or if you want to upload a CSV or Excel file, go here: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing> - Network accessline 2989
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Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin - Network accessline 3063
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We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via <https://huggingface - Network accessline 3112
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Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf> - Network accessline 3134
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If you want to learn how to do reward modelling, do continued pretraining, export to vLLM or GGUF, do text completion, or learn more about finetuning tips and tricks, head over to our [Github](https://github.com/unslothai/unsloth#-finetune- - Network accessline 3180
If you need any help on finetuning, you can also join our Discord server [here](https://discord.gg/unsloth). If you want help with Ollama, you can also join their server [here](https://discord.gg/ollama). - Network accessline 3184
To access our Alpaca dataset example click [here](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing), and our CSV / Excel finetuning guide is [here](https://colab.research.google.com/drive/1VYkncZMfGFkeCE - Network accessline 3197
{% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3198
For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3206
This article covers everything (from beginner to advanced) you need to know about GRPO, Reinforcement Learning (RL) and reward functions, along with tips, and the basics of using GRPO with [Unsloth](https://github.com/unslothai/unsloth). If - Network accessline 3229
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{% column %} OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**< - Network accessline 3259
OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**</mark> to pro - Network accessline 3268
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{% column %} In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agen - Network accessline 3276
In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agent**</mark> is - Network accessline 3284
The formula for PPO looks quite complicated because it was designed to be stable. Visit our [AI Engineer talk](https://docs.unsloth.ai/ai-engineers-2025) we gave in 2025 about RL for more in depth maths derivations about PPO. - Network accessline 3291
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{% column %} DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are: - Network accessline 3295
DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are: - Network accessline 3307
2. Code output can be verified as having executed correctly or not. 3. Designing verifiable reward functions can be tough, and so most examples are math or code. 4. Use-cases for GRPO isn’t just for code or math—its reasoning process can en - Network accessline 3309
4. Use-cases for GRPO isn’t just for code or math—its reasoning process can enhance tasks like email automation, database retrieval, law, and medicine, greatly improving accuracy based on your dataset and reward function - the trick is to d - Network accessline 3321
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* With 15GB VRAM, Unsloth allows you to transform any model up to 17B parameters like Llama 3.1 (8B), Phi-4 (14B), Mistral (7B) or Qwen2.5 (7B) into a reasoning model * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.un - Network accessline 3374
* **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!** - Network accessline 3377
{% content-ref url="reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo" %} [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train - Network accessline 3378
[tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo) - Network accessline 3383
| [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **GSPO -** new | [**Qwen3-VL-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_ - Network accessline 3384
| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- - Network accessline 3385
| [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/mai - Network accessline 3386
| [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) | [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\) - Network accessline 3387
| [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) | [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\ - Network accessline 3389
{% hint style="success" %} **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in - Network accessline 3390
**NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in GRPOConfig to enable: - Network accessline 3416
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For a tutorial on how to transform any open LLM into a reasoning model using Unsloth & GRPO, [see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo). - Network accessline 3422
{% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3423
For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3435
{% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew - Network accessline 3436
If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should - Network accessline 3452
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In our other examples, we use existing GSM8K reward functions by [@willccbb](https://x.com/willccbb) which is popular and shown to be quite effective: - Network accessline 3539
You can now use [vLLM](https://github.com/vllm-project/vllm/) directly in your finetuning stack, which allows for much more throughput and allows you to finetune and do inference on the model at the same time! On 1x A100 40GB, expect 4000 t - Network accessline 3559
1. For GRPO's **GPU VRAM requirements for QLoRA 4-bit**, the general rule is the model parameters = the amount of VRAM you will need (you can use less VRAM but this just to be safe). The more context length you set, the more VRAM. LoRA 16-b - Network accessline 3560
2. Our new memory efficient linear kernels for GRPO slashes memory usage by 8x or more. This shaves 68.5GB of memory, whilst being actually faster through the help of torch.compile! 3. We leverage our smart [Unsloth gradient checkpointing]( - Network accessline 3561
3. We leverage our smart [Unsloth gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm which we released a while ago. It smartly offloads intermediate activations to system RAM asynchronously whilst being only 1% slower. - Network accessline 3580
{% embed url="<https://www.youtube.com/watch?v=OkEGJ5G3foU>" %} - Network accessline 3584
1. Nathan Lambert's RLHF Book is a must! <https://rlhfbook.com/c/11-policy-gradients.html> - Network accessline 3585
2. Yannic Kilcher's GRPO Youtube video is also a must! <https://www.youtube.com/watch?v=bAWV_yrqx4w> - Network accessline 3586
3. We did a 3 hour workshop at AI Engineer World's Fair 2025. Slides are other material are at <https://docs.unsloth.ai/ai-engineers-2025> - Network accessline 3587
4. Advanced GRPO notebook via Unsloth. <https://docs.unsloth.ai/basics/reinforcement-learning-guide/tutorial-train-your-own-reasoning-model-with-grpo> - Network accessline 3588
5. GRPO from a base model notebook: <https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb> - Network accessline 3595
DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. - Network accessline 3599
These instructions are for our pre-made Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). If you are installing Unsloth locally, you can also copy our notebooks inside your favorite code editor. We'll be using - Network accessline 3601
| [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **-** GSPO | [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_ - Network accessline 3603
| [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb - Network accessline 3610
If you're using our Colab notebook, click **Runtime > Run all**. We'd highly recommend you checking out our [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) before getting started. - Network accessline 3612
If installing locally, ensure you have the correct [requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and use `pip install unsloth` on Linux or follow our [Windows install ](https://docs.unsloth.ai/ - Network accessline 3614
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Before we get started, it is recommended to learn more about GRPO, reward functions and how they work. Read more about them including [tips & tricks](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#basics-tips)[ here] - Network accessline 3630
We have pre-selected optimal settings for the best results for you already and you can change the model to whichever you want listed in our [supported models](https://docs.unsloth.ai/get-started/all-our-models). Would not recommend changing - Network accessline 3632
{% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3633
For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 3636
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We have pre-selected OpenAI's [GSM8K](https://huggingface.co/datasets/openai/gsm8k) dataset which contains grade school math problems but you could change it to your own or any public one on Hugging Face. You can read more about [datasets h - Network accessline 3647
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[Reward Functions/Verifiers](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#reward-functions-verifier) lets us know if the model is doing well or not according to the dataset you have provided. Each generation run wi - Network accessline 3720
You can input your generations into an LLM like ChatGPT 4o or Llama 3.1 (8B) and design a reward function and verifier to evaluate it. For example, feed your generations into a LLM of your choice and set a rule: "If the answer sounds too ro - Network accessline 3733
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We have pre-selected hyperparameters for the most optimal results however you could change them. Read all about [parameters here](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide). For **advanced GRPO** - Network accessline 3742
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{% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew - Network accessline 3775
If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should - Network accessline 3780
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You can then save your model to GGUF, Ollama etc. by following our [guide here](https://docs.unsloth.ai/fine-tuning-llms-guide#id-7.-running--saving-the-model). - Network accessline 3798
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We have multiple options for saving your fine-tuned model, but we’ll focus on the easiest and most popular approaches which you can read more about [here](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 3850
{% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %} - Network accessline 3854
{% embed url="<https://www.youtube.com/watch?t=3289s&v=bbFEYPx9Hpo>" %} - Network accessline 3858
{% embed url="<https://www.youtube.com/watch?v=juOh1afy-IE>" %} - Network accessline 3860
{% embed url="<https://www.youtube.com/watch?v=oF0_eMhzRaQ>" %} - Network accessline 3904
This can zero out all `completion_mask` entries when many completions are truncated, making `n_mask_per_reward = 0` and causing KL to become NaN. [See](https://github.com/unslothai/unsloth-zoo/blob/e705f7cb50aa3470a0b6e36052c61b7486a39133/u - Access to secretsline 4111
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This means the memory space for inference and training can in theory be re-used, since inference and training are separate modes - this is where [vLLM's sleep mode feature](https://docs.vllm.ai/en/latest/features/sleep_mode.html#rlhf-weight - Access to secretsline 4186
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<table data-full-width="true"><thead><tr><th>Experiments</th><th>Config</th><th>Status</th><th>GPU Memory usage</th><th>Comments</th></tr></thead><tbody><tr><td><ol><li><a href="https://colab.research.google.com/drive/18CssBY5C0mStnLvu2Hlt4 - Network accessline 4333
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We now select better compilation flags and reduce compile times by 50% or more. We also managed to dynamically patch any vLLM version to handle `gc.collect` better for backwards compatibility reasons, as inspired from this [vLLM pull reques - Network accessline 4349
All our GRPO notebooks have Unsloth Standby on by default and all optimizations! See <https://docs.unsloth.ai/get-started/unsloth-notebooks> for all our GRPO notebooks, or try the below: - Network accessline 4351
* [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA - Network accessline 4352
* [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecases) - Network accessline 4353
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 4354
* [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA - Network accessline 4355
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 4356
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb) - Network accessline 4357
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) - Network accessline 4358
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 4367
It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking). - Network accessline 4369
<div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div> - Network accessline 4371
**Can you counter reward hacking? Yes!** In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation s - Network accessline 4396
We're introducing GSPO which is a variant of [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#from-rlhf-ppo-to-grpo-and-rlvr) made by the Qwen team at Alibaba. They noticed the observation that when GRPO takes i - Network accessline 4398
* Use our free GSPO notebooks for: [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) and [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blo - Network accessline 4402
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* [GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-rl-notebooks) - Network accessline 4459
* [ORPO notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb) - Network accessline 4460
* [DPO Zephyr notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 4461
* [KTO notebook](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) - Network accessline 4462
* [SimPO notebook](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing) - Network accessline 4464
We're also in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#acceler - Access to secretsline 4471
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You can fine-tune DeepSeek-OCR to enhance its vision or language performance. In our Unsloth [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb), we demonstrat - Network accessline 4540
> **Our model upload that enables fine-tuning + more inference support:** [**DeepSeek-OCR**](https://huggingface.co/unsloth/DeepSeek-OCR) - Network accessline 4561
source .venv/bin/activate # Until v0.11.1 release, you need to install vLLM from nightly build uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly - Network accessline 4563
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly - Access to secretsline 4632
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Unsloth supports fine-tuning of DeepSeek-OCR. Since the default model isn’t fine-tunable, we added changes from the [Stranger Vision HF](https://huggingface.co/strangervisionhf) team, to then enable fine-tuning. As usual, Unsloth trains Dee - Network accessline 4659
* DeepSeek-OCR: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) - Network accessline 4660
* DeepSeek-OCR: [Fine-tuning + Evaluation notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\)-Eval.ipynb) (A100) - Network accessline 4771
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Local training can be complex due to dependency hell or breaking environments. Unsloth’s [Docker image](https://hub.docker.com/r/unsloth/unsloth) can bypass these issues. No setup is needed: pull and run the image and start training. - Network accessline 4780
* **Unsloth official Docker image:** [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) - Network accessline 4784
Unsloth’s Docker image is stable, up-to-date and works in [supported setups](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements#system-requirements) like Windows. - Network accessline 4800
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\ - Network accessline 4801
Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation): - Dangerous shell operationline 4804
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 4811
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[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and 50-series GPUs, use this same im - Network accessline 4828
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 4837
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If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 4850
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* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 4936
Unsloth now supports vision/multimodal RL with [Qwen3-VL](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune), [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune) and more. Due to Unsloth's unique [weigh - Network accessline 4938
Unsloth can train Qwen3-VL-8B with GSPO/GRPO on a free Colab T4 GPU. Other VLMs work too, but may need larger GPUs. Gemma requires newer GPUs than T4 because vLLM [restricts to Bfloat16](https://docs.unsloth.ai/models/gemma-3-how-to-run-and - Network accessline 4940
* **Qwen-3 VL-8B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Network accessline 4941
* **Qwen-2.5 VL-7B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) •[ Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/note - Network accessline 4942
* **Gemma-3-4B** (Unsloth inference): [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Network accessline 4944
We have also added vLLM VLM integration into Unsloth natively, so all you have to do to use vLLM inference is enable the `fast_inference=True` flag when initializing the model. Special thanks to [Sinoué GAD](https://github.com/unslothai/uns - Network accessline 4946
This VLM support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), which un - Access to secretsline 4953
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This was [reported](https://github.com/QwenLM/Qwen2.5-VL/issues/759) as well in Qwen2.5-VL-7B-Instruct output unexpected results "addCriterion". In fact we see this as well! We tried both non Unsloth, bfloat16 and float16 machines and other - Network accessline 5006
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# Fix up addCriterion issues # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 5037
# See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 5050
This update in addition adds GSPO ([Group Sequence Policy Optimization](https://arxiv.org/abs/2507.18071)) which is a variant of GRPO made by the Qwen team at Alibaba. They noticed that GRPO implicitly results in importance weights for each - Network accessline 5054
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[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide "mention") - Network accessline 5107
***Authors:** A huge thank you to* [*Keith*](https://www.linkedin.com/in/keith-truongcao-7bb84a23b/) *and* [*Datta*](https://www.linkedin.com/in/datta0/) *for contributing to this article!* - Network accessline 5112
You can now train OpenAI [gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) with RL and GRPO via [Unsloth](https://github.com/unslothai/unsloth). Unsloth now offers the <mark style="background-color:$success;">**fast - Network accessline 5114
Since reinforcement learning (RL) on gpt-oss isn't yet vLLM compatible, we had to rewrite the inference code from Transformers code to deliver 3x faster inference for gpt-oss at \~21 tokens/s. For BF16, Unsloth also achieves the fastest inf - Network accessline 5116
* **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\ - Network accessline 5119
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Unsloth is the only framework to support 4-bit RL for gpt-oss. All performance gains are due to Unsloth's unique [weight sharing](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#what-unsloth-offers-for-rl), [Flex Attenti - Network accessline 5125
{% hint style="warning" %} Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and - Network accessline 5126
Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt - Network accessline 5133
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Inference is crucial in RL training, since we need it to generate candidate solutions before maximizing some reward function ([see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) for a more detailed explanation). - Network accessline 5137
vLLM does not support RL for gpt-oss since it lacks BF16 training and LoRA support for gpt-oss. Without Unsloth, only training via full precision BF16 works, making <mark style="background-color:$warning;">memory use</mark> <mark style="bac - Network accessline 5145
We had to change our implementation for attention sinks as [described here](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) to allow generation to work with left padding. We had to get the logs - Network accessline 5194
The harder part is batch generation. Sequences differ in length, so padding complicates mask creation. Flex Attention had a lot of [challenges](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) and dynamic mas - Network accessline 5196
> *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) - Network accessline 5210
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It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking). - Network accessline 5227
<div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div> - Network accessline 5229
In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation setting and showcase tangible solutions to - Network accessline 5251
LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs. - Network accessline 5253
RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo - Network accessline 5257
| [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo - Network accessline 5279
LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs. - Network accessline 5281
RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo - Network accessline 5283
| [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo - Network accessline 5311
!uv pip install -qqq \ "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 5312
"torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ "unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 5313
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 5314
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 5315
git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 5326
Load the 20B model in 4‑bit QLoRA for memory efficiency, then wrap it with a LoRA adapter. You can also train it in 16-bit LoRA but it will use 4x more memory. For more settings view our [configuration guide](https://docs.unsloth.ai/get-sta - Network accessline 5576
We will use the **GRPOTrainer**. Set the prompt/completion lengths, then build a `GRPOConfig`. Keep in mind you could also set the RL algorithm type to others such as [GSPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guid - Network accessline 5703
We’re excited to share that Unsloth Dynamic GGUFs shows how it's possible to quantize LLMs like [DeepSeek-V3.1](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) (671B) down to just **1-bit** or **3-bit**, and still be able t - Network accessline 5705
Previously, [we demonstrated](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) how Unsloth Dynamic GGUFs outperform other quantization methods on 5-shot MMLU and KL Divergence. Now, we’re showcasing their performance on independent - Network accessline 5707
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**Why the** [**Aider Polyglot**](https://aider.chat/docs/leaderboards/) **benchmark?** Aider is one of the most comprehensive measures of how well LLMs can write, code, follow instructions, and apply changes without human intervention, maki - Network accessline 5719
{% hint style="success" %} The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalL - Network accessline 5720
The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3 - Network accessline 5729
In Nov 2024, our [4-bit Dynamic](https://unsloth.ai/blog/dynamic-4bit) Quants showcased how you could largely restore QLoRA fine-tuning & model accuracy by just <mark style="background-color:green;">**selectively quantizing layers**</mark>. - Network accessline 5731
Our Dynamic GGUFs are even more effective when paired with our [imatrix calibration dataset](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs#whats-new-in-dynamic-v2.0), designed for chat and coding performance. All of this enabled - Network accessline 5738
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We also showed dynamic benchmarks in <https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs> for Gemma 3 and Llama 4 Scout, showing how effective our methodology is: - Network accessline 5752
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Benchmark experiments were mainly conducted by [David Sluys](https://www.linkedin.com/in/david-sluys-231348208/) (neolithic5452 on [Aider Discord](https://discord.com/channels/1131200896827654144/1408293692074360914)), a trusted community c - Network accessline 5816
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See [here](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?chat_template=default\&format=true) for our fixed chat template or [here](https://huggingface.co/unsloth/DeepSeek-V3.1/raw/main/chat_template.jinja) for a raw jinja file. - Network accessline 5870
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Head over to our [DeepSeek V3.1 guide](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-how-to-run-locally/deepseek-r1-dynamic-1.58-bit) or to quickly get the dynamic 2bit version, do: - Network accessline 5877
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5878
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5879
git clone https://github.com/ggml-org/llama.cpp - Network accessline 5909
Qwen3-VL has vision, video and OCR capabilities as well as 256K context (can be extended to 1M).\ \ [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcem - Network accessline 5911
[Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl). Train Qwen3-VL (8B) for free with our [notebooks](#fine-tuning-qwen3-vl). - Network accessline 5921
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF">2B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF">2B-Thinking</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL - Network accessline 5939
Qwen3-VL also used the below settings for their benchmarking numbers, as mentioned [on GitHub](https://github.com/QwenLM/Qwen3-VL/tree/main?tab=readme-ov-file#generation-hyperparameters). - Network accessline 5982
[Credential-related source omitted] - Network accessline 6004
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6007
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6008
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6009
git clone https://github.com/ggml-org/llama.cpp - Network accessline 6016
2. **Let's first get an image!** You can also upload images as well. We shall use <https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png>, which is just our mini logo showing how finetun - Network accessline 6018
[Credential-related source omitted] - Network accessline 6025
wget https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png -O unsloth.png - Network accessline 6030
4. Let's get the 2nd image at <https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg> - Network accessline 6032
[Credential-related source omitted] - Network accessline 6037
wget https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg -O picture.png - Network accessline 6060
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[Credential-related source omitted] - Access to secretsline 6100
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* [Normal SFT fine-tuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) - Network accessline 6236
* [GRPO/GSPO RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Network accessline 6246
[Credential-related source omitted] - Network accessline 6248
This Qwen3-VL support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), whi - Network accessline 6277
{% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, - Network accessline 6278
[**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc. - Network accessline 6280
We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t - Network accessline 6283
> [**Fine-tune**](#fine-tuning-gpt-oss-with-unsloth) **gpt-oss-20b for free with our** [**Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) - Network accessline 6285
Trained with [RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), **gpt-oss-120b** rivals o4-mini and **gpt-oss-20b** rivals o3-mini. Both excel at function calling and CoT reasoning, surpassing o1 and GPT-4o. - Network accessline 6293
* 20B: [gpt-oss-**20B**](https://huggingface.co/unsloth/gpt-oss-20b-GGUF) - Network accessline 6294
* 120B: [gpt-oss-**120B**](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) - Network accessline 6298
OpenAI released a standalone parsing and tokenization library called [Harmony](https://github.com/openai/harmony) which allows one to tokenize conversations to OpenAI's preferred format for gpt-oss. The official OpenAI [cookbook article](ht - Network accessline 6302
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We made a [MXFP4 inference notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) as well in Tesla T4 Colab! - Network accessline 6355
{% hint style="info" %} [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, - Network accessline 6356
[Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, microscaled lhs/rhs are - Network accessline 6401
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To achieve inference speeds of 6+ tokens per second for our Dynamic 4-bit quant, have at least **14GB of unified memory** (combined VRAM and RAM) or **14GB of system RAM** alone. As a rule of thumb, your available memory should match or exc - Network accessline 6413
> **You can run gpt-oss-20b for free with our** [**Google Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) - Network accessline 6425
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6428
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6429
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6430
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 6450
[Credential-related source omitted] - Network accessline 6461
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To achieve inference speeds of 6+ tokens per second for our 1-bit quant, we recommend at least **66GB of unified memory** (combined VRAM and RAM) or **66GB of system RAM** alone. As a rule of thumb, your available memory should match or exc - Network accessline 6479
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6482
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6483
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6484
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 6515
[Credential-related source omitted] - Network accessline 6556
The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You - Network accessline 6567
{% content-ref url="gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss" %} [tutorial-how-to-fine-tune-gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss) - Network accessline 6568
[tutorial-how-to-fine-tune-gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss) - Network accessline 6571
Currently you cannot load QLoRA fine-tuned gpt-oss models in frameworks other than Unsloth, however you can if you do LoRA fine-tuning and utilize our [bf16 weights](https://huggingface.co/unsloth/gpt-oss-20b-BF16) for fine-tuning. This mea - Network accessline 6575
* gpt-oss-20b [Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) (recommended) - Network accessline 6584
To enable export/usage of the model for use outside of Unsloth but with Hugging Face, llama.cpp, or vLLM, fine-tuning must be done with LoRA while leveraging our [bf16 weights](https://huggingface.co/unsloth/gpt-oss-20b-BF16). Keep in mind - Network accessline 6608
We utilized OpenAI's Triton Kernels library directly to allow MXFP4 inference. For finetuning / training however, the MXFP4 kernels do not yet support training, since the backwards pass is not yet implemented. We're actively working on impl - Network accessline 6618
Though gpt-oss supports only reasoning, you can still fine-tune it with a non-reasoning [dataset](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide), but this may affect its reasoning ability. If you want to maintain - Network accessline 6622
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{% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF - Network accessline 6632
[**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc. - Network accessline 6634
We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t - Network accessline 6637
> **Quickstart:** Fine-tune gpt-oss-20b for free with our: [Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) - Network accessline 6648
This section covers fine-tuning gpt-oss using our Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). You can also save and use the gpt-oss notebook into your favorite code editor and follow our [local gpt-oss g - Network accessline 6657
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We’ll load **`gpt-oss-20b`** using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work).  - Network accessline 6675
[Credential-related source omitted] - Network accessline 6679
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Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 6688
{% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high. - Network accessline 6689
To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.  - Network accessline 6694
[Credential-related source omitted] - Network accessline 6703
[Credential-related source omitted] - Network accessline 6710
For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated - Network accessline 6716
[Credential-related source omitted] - Network accessline 6745
[Credential-related source omitted] - Network accessline 6747
One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` , - Network accessline 6749
{% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 6750
🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 6753
Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide). - Network accessline 6760
We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet - Network accessline 6764
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1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6815
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6816
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6817
git clone https://github.com/ggml-org/llama.cpp - Network accessline 6837
[Credential-related source omitted] - Network accessline 6845
{% hint style="info" %} We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use. - Network accessline 6846
We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use. - Network accessline 6854
Ensure your device is [Unsloth compatible](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and you can read our detailed [installation guide](https://docs.unsloth.ai/get-started/install-and-update). - Network accessline 6863
!uv pip install -qqq \ "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 6864
"torch>=2.8.0" "triton>=3.4.0" {install_numpy} \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ "unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 6865
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 6866
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 6867
torchvision bitsandbytes \ git+https://github.com/huggingface/transformers \ git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 6868
git+https://github.com/huggingface/transformers \ - Network accessline 6869
git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 6878
We’ll load **`gpt-oss-20b`** using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work for QLoRA fine-tuning). Configur - Network accessline 6894
</strong> "unsloth/gpt-oss-20b", # 20B model using MXFP4 format "unsloth/gpt-oss-120b", ] # More models at https://huggingface.co/unsloth - Network accessline 6896
] # More models at https://huggingface.co/unsloth - Network accessline 6915
Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 6917
{% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high. - Network accessline 6918
To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.  - Network accessline 6946
For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated - Network accessline 6990
[Credential-related source omitted] - Network accessline 6992
One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` , - Network accessline 6994
{% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 6995
🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 6998
Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide). - Network accessline 7005
We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet - Network accessline 7035
[Credential-related source omitted] - Network accessline 7064
[Credential-related source omitted] - Network accessline 7091
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7094
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7095
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7096
git clone https://github.com/ggml-org/llama.cpp - Network accessline 7124
As always, feel free to drop by our [Discord](https://discord.com/invite/unsloth) or [Reddit](https://www.reddit.com/r/unsloth/) if you need any help. - Network accessline 7130
Yes you can now [save/export your gpt-oss fine-tuned](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training) model using Unsloth's new update! - Network accessline 7142
Yes! Unsloth now supports RL for gpt-oss with GRPO/GSPO. We made it work on a free Kaggle notebook and achieved the fastest inference for RL. [Read more here](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) - Network accessline 7146
***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 7161
[Credential-related source omitted] - Network accessline 7167
A huge thank you to Rohan Pandey for his [Flex Attention implementation](https://x.com/khoomeik/status/1955693558914310608), which directly inspired the development of Unsloth's Flex Attention implementation. - Network accessline 7173
OpenAI leveraged **Attention Sinks** from the Efficient Streaming Language Models with Attention Sinks [paper](https://arxiv.org/abs/2309.17453) which shows that you can use a small sliding window, except you must add a global attention on - Network accessline 7175
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[Credential-related source omitted] - Network accessline 7183
The paper also shows that the [Attention Is Off By One method](https://www.evanmiller.org/attention-is-off-by-one.html) does partially work, except one must also add a few extra sink tokens to get lower perplexities. **The paper shows that - Network accessline 7185
[Credential-related source omitted] - Network accessline 7189
Flex Attention <https://pytorch.org/blog/flexattention/> is extremely powerful as it provides the practitioner 2 customization routes for the attention mechanism - a **score modifier (f)** and a **masking function (M)**. - Network accessline 7207
By using some visualization utilities from [Flex Attention's Github repo](https://github.com/meta-pytorch/attention-gym), we can visualize this. Assume the sequence length was 16, and a sliding window of 5. On the left is the last sink colu - Network accessline 7213
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**Interesting finding**: The official Flex Attention sliding window implementations considers the window size as the number of last tokens **PLUS ONE** as it includes the current token. The HuggingFace and GPT OSS implementations strictly o - Network accessline 7240
[Credential-related source omitted] - Network accessline 7246
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We also confirmed through OpenAI's official GPT-OSS implementation on whether we attend to the last N or N+1 tokens here: <https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py> - Network accessline 7260
[Credential-related source omitted] - Network accessline 7290
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To run inference on the merged model, you can use vLLM and Llama.cpp among others. OpenAI recommends these [inference settings](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#recommended-settings) for both models: `tempe - Network accessline 7336
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7339
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7340
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7341
git clone https://github.com/ggml-org/llama.cpp - Network accessline 7368
# build from source git clone https://github.com/sgl-project/sglang - Network accessline 7369
git clone https://github.com/sgl-project/sglang - Network accessline 7374
# ROCm 6.3 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 - Network accessline 7375
pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 - Network accessline 7376
git clone https://github.com/triton-lang/triton - Network accessline 7380
# hopper pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 - Network accessline 7381
pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 - Network accessline 7384
# blackwell cu128 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 - Network accessline 7385
pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 - Network accessline 7386
pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl - Network accessline 7388
# blackwell cu129 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 - Network accessline 7389
pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 - Network accessline 7390
pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl - Network accessline 7403
url = f"http://localhost:8000/v1/chat/completions" - Network accessline 7410
response = requests.post(url, json=data) - Network accessline 7436
We [recently collaborated with Hugging Face](https://github.com/huggingface/transformers/pull/40197) to resolve inference issues by using OpenAI’s kernels and ensuring that `swiglu_limit = 7.0` is correctly applied during MXFP4 inference. - Network accessline 7442
[Credential-related source omitted] - Network accessline 7454
[Credential-related source omitted] - Network accessline 7458
OpenAI's sink token implementation is [provided here](https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py). We provide it below: - Network accessline 7487
The HuggingFace transformers implementation is [provided here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py). We also provide it below: - Network accessline 7531
GLM-4.6 is the latest reasoning model from **Z.ai**, achieving SOTA performance on coding and agent benchmarks while offering improved conversational chats. The full 355B parameter model requires **400GB** of disk space, while the Unsloth D - Network accessline 7538
You asked for benchmarks on our quants, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing many full-precision SOTA LLMs. [Read more.](https://docs.unsloth.ai/new/unsloth-dynamic-gg - Network accessline 7541
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and Aider performance, meaning you can run & fine-tune quantized GLM LLMs with minimal accuracy loss. - Network accessline 7551
[Credential-related source omitted] - Network accessline 7583
{% stepper %} {% step %} Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp). - Network accessline 7585
Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp). - Network accessline 7588
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7590
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7590
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7626
{% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus - Network accessline 7628
Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7631
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7632
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7633
git clone https://github.com/ggerganov/llama.cpp - Access to secretsline 7679
[Credential-related source omitted] - Network accessline 7741
import json openai_client = OpenAI( base_url = "http://127.0.0.1:8001/v1", - Network accessline 7742
[Credential-related source omitted] - Network accessline 7743
base_url = "http://127.0.0.1:8001/v1", - Access to secretsline 7744
[Credential-related source omitted] - Network accessline 7759
We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple devices resp - Network accessline 7761
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/GLM-4.6-GGUF?show_file_info=GLM-4.6-UD-TQ1_ - Network accessline 7775
Llama.cpp also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You can also **quantize the KV cache to 4bits** for example to reduce VRAM - Network accessline 7802
<table><thead><tr><th width="249">Dynamic GGUFs</th><th>Dynamic 4-bit + FP8</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/granite-4.0-h-350m-GGUF">H-350M</a></li><li><a href="https:/ - Network accessline 7804
You can also view our [Granite-4.0 collection](https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d) for all uploads including Dynamic Float8 quants etc. - Network accessline 7841
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7843
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7843
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7854
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7857
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7858
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 7859
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 7878
[Credential-related source omitted] - Network accessline 7917
* **Granite-4.0** [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) - Network accessline 7918
* Granite-4.0-350M [fine-tuning notebook](https://github.com/unslothai/notebooks/blob/main/nb/Granite4.0_350M.ipynb) - Network accessline 7924
[Credential-related source omitted] - Network accessline 7953
DeepSeek’s V3.1 and **Terminus** update introduces hybrid reasoning inference, combining 'think' and 'non-think' into one model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic 2-bit version uses 245GB (-75 - Network accessline 7955
{% hint style="success" %} **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\ - Network accessline 7956
**NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\ - Network accessline 7958
[**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing - Network accessline 7963
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss. - Network accessline 7987
1. DeepSeek V3.1 is a hybrid reasoning model, meaning you can change the chat template to enable reasoning. The chat template introduced `thinking = True` , but other models use `enable_thinking = True` . We added the option to use `enable_ - Network accessline 7988
2. llama.cpp's jinja renderer via [minja](https://github.com/google/minja) does not allow the use of extra arguments in the `.split()` command, so using `.split(text, 1)` works in Python, but not in minja. We had to change this to make llam - Network accessline 7995
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3.1), these are the recommended settings for V3.1 inference: - Network accessline 8060
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 8062
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8062
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8095
{% step %} Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant. - Network accessline 8096
Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant. - Network accessline 8102
{% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus - Network accessline 8104
Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8107
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8108
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8109
git clone https://github.com/ggerganov/llama.cpp - Access to secretsline 8156
[Credential-related source omitted] - Network accessline 8231
import json openai_client = OpenAI( base_url = "http://127.0.0.1:8001/v1", - Network accessline 8232
[Credential-related source omitted] - Network accessline 8233
base_url = "http://127.0.0.1:8001/v1", - Access to secretsline 8234
[Credential-related source omitted] - Network accessline 8249
We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple devices resp - Network accessline 8251
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?show_file_info=DeepSeek- - Network accessline 8253
We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-V3.1-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-V3.1). - Network accessline 8267
The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You - Network accessline 8286
Qwen3-Coder is Qwen’s new series of coding agent models, available in 30B (**Qwen3-Coder-Flash**) and 480B parameters. **Qwen3-480B-A35B-Instruct** achieves SOTA coding performance rivalling Claude Sonnet-4, GPT-4.1, and [Kimi K2](https://d - Network accessline 8288
We also uploaded Qwen3-Coder with native <mark style="background-color:purple;">**1M context length**</mark> extended by YaRN and full-precision 8bit and 16bit versions. [Unsloth](https://github.com/unslothai/unsloth) also now supports fine - Network accessline 8296
{% hint style="success" %} **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dy - Network accessline 8297
**Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dynamic quant nearly matched - Network accessline 8302
| Dynamic 2.0 GGUF (to run) | 1M Context Dyn - Network accessline 8304
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF">30B-A3B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF">480B-A35B-Instruct</a></li></ul> | <ul><li><a hre - Network accessline 8383
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 8385
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8385
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8396
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8399
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8400
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8401
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 8421
[Credential-related source omitted] - Network accessline 8448
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8451
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8452
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8453
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 8485
[Credential-related source omitted] - Network accessline 8520
{% hint style="success" %} Also don't forget about the new Qwen3 update. Run [**Qwen3-235B-A22B-Instruct-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) locally with llama.cpp. - Network accessline 8521
Also don't forget about the new Qwen3 update. Run [**Qwen3-235B-A22B-Instruct-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) locally with llama.cpp. - Network accessline 8536
The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You - Network accessline 8548
We also uploaded 1 million context length GGUFs via YaRN scaling [here](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/). - Network accessline 8554
1. Download the first file at <https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/tree/main/UD-Q2\\_K\\_XL> for UD-Q2\_K\_XL, and replace your current file - Network accessline 8555
2. Use `snapshot_download` as usual as in <https://docs.unsloth.ai/basics/qwen3-coder-how-to-run-locally#llama.cpp-run-qwen3-tutorial> which will auto override the old files - Network accessline 8556
3. Use the new chat template via `--chat-template-file`. See [GGUF chat template](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF?chat_template=default) or [chat\_template.jinja](https://huggingface.co/unsloth/Qwen3-Coder - Network accessline 8557
4. As an extra, we also made 1 single 150GB UD-IQ1\_M file (so Ollama works) at <https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/blob/main/Qwen3-Coder-480B-A35B-Instruct-UD-IQ1\\_M.gguf> - Network accessline 8559
This should solve issues like: <https://github.com/ggml-org/llama.cpp/issues/14915> - Network accessline 8635
Google releases Gemma 3 with a new 270M model and the previous 1B, 4B, 12B, and 27B sizes. The 270M and 1B are text-only, while larger models handle both text and vision. We provide GGUFs, and a guide of how to run it effectively, and how t - Network accessline 8637
{% hint style="success" %} **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co - Network accessline 8638
**NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co/collections/unsloth/gemma- - Network accessline 8640
Also see our [Gemma 3n Guide](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune). - Network accessline 8647
* Fine-tune Gemma 3 (4B) with vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - Network accessline 8656
| GGUF - Network accessline 8658
| <ul><li><a href="https://huggingface.co/unsloth/gemma-3-270m-it-GGUF">270M</a> - new</li><li><a href="https://huggingface.co/unsloth/gemma-3-1b-it-GGUF">1B</a></li><li><a href="https://huggingface.co/unsloth/gemma-3-4b-it-GGUF">4B</a></li - Network accessline 8695
To run the models on your phone, we recommend using any mobile app that can run GGUFs locally on edge devices like phones. After fine-tuning you can export it to GGUF then run it locally on your phone. Ensure your phone has enough RAM/power - Network accessline 8706
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 8708
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8708
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8719
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8722
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8723
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8724
git clone https://github.com/ggerganov/llama.cpp - Network accessline 8738
3. **OR** download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/gemma-3-27b-i - Access to secretsline 8743
[Credential-related source omitted] - Network accessline 8791
The full input from our <https://unsloth.ai/blog/deepseekr1-dynamic> 1.58bit blog is: - Network accessline 8819
* Try our new [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) which makes the 270M parameter model very smart at playing chess and can predict the next chess move. - Network accessline 8820
* Fine-tune Gemma 3 (4B) using our notebooks for: [**Text**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) or [**Vision**](https://colab.research.google.com/github/unslothai/notebooks/blob/ma - Network accessline 8821
* Or fine-tune [Gemma 3n (E4B)](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune) with [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversa - Network accessline 8837
[Credential-related source omitted] - Network accessline 8843
[Credential-related source omitted] - Network accessline 8854
Google’s Gemma 3n multimodal model handles image, audio, video, and text inputs. Available in 2B and 4B sizes, it supports 140 languages for text and multimodal tasks. You can now run and fine-tune **Gemma-3n-E4B** and **E2B** locally using - Network accessline 8856
> **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 8864
<table><thead><tr><th width="249">Dynamic 2.0 GGUF (text only)</th><th width="285">Dynamic 4-bit Instruct (to fine-tune)</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/gemma-3n-E2B-it - Network accessline 8866
**See all our Gemma 3n uploads including base and more formats in** [**our collection here**](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339)**.** - Dangerous shell operationline 8917
ollama rm hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL - Network accessline 8927
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 8929
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8929
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 8940
{% hint style="info" %} We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp! - Network accessline 8941
We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp! - Network accessline 8944
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8947
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8948
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8949
git clone https://github.com/ggerganov/llama.cpp - Access to secretsline 8967
[Credential-related source omitted] - Network accessline 9018
Gemma 3n, like [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/..#unsloth-fine-tuning-fixes-for-gemma-3), had issues running on <mark style="background-color:yellow;">**Flotat16 GPUs such as Tesla T4s in Colab**</m - Network accessline 9020
* Fine-tune Gemma 3n-E4B with our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 9021
* **Audio:** Fine-tune Gemma 3n-E4B with our [**Audio only notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - Network accessline 9022
* **Vision**: Fine-tune Gemma 3n-E4B with our [**Vision only notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Vision.ipynb) - Network accessline 9044
* Fine-tune Gemma 3 (4B) with Vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - Network accessline 9046
{% hint style="info" %} If you love Kaggle, Google is holding a competition where the best model fine-tuned with Gemma 3n and Unsloth will win a $10K prize! [See more here](https://www.kaggle.com/competitions/google-gemma-3n-hackathon). - Network accessline 9047
If you love Kaggle, Google is holding a competition where the best model fine-tuned with Gemma 3n and Unsloth will win a $10K prize! [See more here](https://www.kaggle.com/competitions/google-gemma-3n-hackathon). - Network accessline 9054
Thanks to discussions from [Michael](https://github.com/mxyng) from the Ollama team and also [Xuan](https://x.com/ngxson) from Hugging Face, there were 2 issues we had to fix specifically for GGUFs: - Network accessline 9056
1. The `add_shared_kv_layers` parameter was accidentally encoded in `float32` which is fine, but becomes slightly complicated to decode on Ollama's side - a simple change to `uint32` solves the issue. [Pull request](https://github.com/ggml- - Network accessline 9058
1. As an [update](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF/discussions/4), [Matt](https://huggingface.co/WBB2500) mentioned we can also use Q4\_0, Q4\_1, Q5\_0, Q5\_1 for the embeddings - and we confirmed it does also work in Oll - Network accessline 9066
Our previous fixes for Gemma 3 is [discussed here](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune). For Gemma 3, we found that activations exceed float16's maximum range of **65504.** - Network accessline 9073
[Credential-related source omitted] - Network accessline 9079
[Credential-related source omitted] - Network accessline 9103
We found Gemma 3N's vision encoder to be quite unique as well since it re-uses hidden states. This unfortunately limits the usage of [Unsloth's gradient checkpointing](https://unsloth.ai/blog/long-context), which could have reduced VRAM usa - Network accessline 9114
[Credential-related source omitted] - Network accessline 9116
{% hint style="success" %} **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 9117
**Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 9124
So what is so special about Gemma 3n you ask? It is based on [Matryoshka Transformer or MatFormer](https://arxiv.org/abs/2310.07707) architecture meaning that each transformer layer/block embeds/nests FFNs of progressively smaller sizes. Th - Network accessline 9132
[Credential-related source omitted] - Network accessline 9134
{% hint style="info" %} **Fine-tune and try multimodal Gemma 3n inference with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 9135
**Fine-tune and try multimodal Gemma 3n inference with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 9145
{% hint style="success" %} **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) - Network accessline 9146
**NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) - Network accessline 9149
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Qwen LLMs with minimal accuracy loss. - Network accessline 9153
[Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3 and Qwen3 MOE models — 2x faster, with 70% less VR - Network accessline 9159
| Dynamic 2.0 GGUF (to run) - Network accessline 9161
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-0.6B-GGUF">0.6B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-1.7B-GGUF">1.7B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-4B-GGUF">4B</a></li><li><a href="htt - Network accessline 9208
Qwen3 models come with built-in "thinking mode" to boost reasoning and improve response quality - similar to how [QwQ-32B](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/qwq-32b-how-to-run-effectively) worked. Instru - Network accessline 9275
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 9277
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9277
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9298
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9301
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9302
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9303
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 9315
[Credential-related source omitted] - Access to secretsline 9359
[Credential-related source omitted] - Network accessline 9405
* [Qwen3 (14B) Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) (recommended) - Network accessline 9406
* [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **- Advanced GRPO LoRA** - Network accessline 9407
* [Qwen3 (14B) Alpaca notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Alpaca.ipynb) (for Base models) - Network accessline 9440
[Credential-related source omitted] - Network accessline 9450
If you'd like to read a full end-to-end guide on how to use Unsloth notebooks for fine-tuning or just learn about fine-tuning, creating [datasets](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) etc., view our [co - Network accessline 9452
{% content-ref url="../get-started/fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 9453
[fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 9456
{% content-ref url="../get-started/fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9457
[datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9465
[**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **notebook - Advanced GRPO LoRA** - Network accessline 9475
[Credential-related source omitted] - Network accessline 9482
Qwen released 2507 (July 2025) updates for their [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune) 4B, 30B and 235B models, introducing both "thinking" and "non-thinking" variants. The non-thinking '**Qwen3-30B-A3B-Inst - Network accessline 9486
[Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3-2507 models — 2x faster, with 70% less VRAM, and 8 - Network accessline 9490
**Unsloth** [**Dynamic 2.0**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **GGUFs:** - Network accessline 9492
| Model | GGUFs to run: | | ------------------------ | ---------------------- - Network accessline 9493
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Qwen3-**4B-2507** | [Instruct](https://hug - Network accessline 9494
| Qwen3-**4B-2507** | [Instruct](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF) • [Thinking ](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF) | - Network accessline 9495
| Qwen3-**30B-A3B**-2507 | [Instruct](#llama.cpp-run-qwen3-30b-a3b-instruct-2507-tutorial) • [Thinking](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF) | - Network accessline 9496
| Qwen3-**235B-A22B**-2507 | [Instruct](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF) • [Thinking](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF) | - Network accessline 9555
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 9557
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9557
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9568
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9571
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9572
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9573
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 9593
[Credential-related source omitted] - Network accessline 9623
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 9625
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9625
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9636
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9639
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9640
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9641
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 9661
[Credential-related source omitted] - Network accessline 9697
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9700
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9701
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9702
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 9730
[Credential-related source omitted] - Network accessline 9791
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9794
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9795
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9796
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 9824
[Credential-related source omitted] - Network accessline 9870
The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You - Network accessline 9884
Unsloth makes [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/..#fine-tuning-qwen3-with-unsloth) and Qwen3-2507 fine-tuning 2x faster, use 70% less VRAM and supports 8x longer context lengths. Because Qwen3-2507 was o - Network accessline 9888
* [Qwen3 (14B) Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)  - Network accessline 9900
**Qwen3-2507-4B notebooks for:** [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) and [Instruct](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen - Network accessline 9921
[Credential-related source omitted] - Network accessline 9928
[Credential-related source omitted] - Network accessline 9935
DeepSeek-R1-0528 is DeepSeek's new update to their R1 reasoning model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic **1.66-bit** version uses 162GB (-80% reduction in size). GGUF: [DeepSeek-R1-0528-GGUF] - Network accessline 9937
DeepSeek also released a R1-0528 distilled version by fine-tuning Qwen3 (8B). The distill achieves similar performance to Qwen3 (235B). ***You can also*** [***fine-tune Qwen3 Distill***](#fine-tuning-deepseek-r1-0528-with-unsloth) ***with U - Network accessline 9939
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss. - Network accessline 9946
NEW: Huge improvements to tool calling and chat template fixes.\ \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM - Network accessline 9947
\ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/ - Network accessline 9948
New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/De - Network accessline 9967
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528), these are the recommended settings for R1 (R1-0528 and Qwen3 distill should use the same settings) inference: - Network accessline 9994
* Qwen3 (8B) distill: [DeepSeek-R1-0528-Qwen3-8B-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) - Network accessline 9997
We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple device - Network accessline 9999
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSe - Network accessline 10001
We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-R1-0528). - Network accessline 10010
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 10012
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10012
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10031
Open WebUI has made an step-by-step tutorial on how to run R1 here and for R1-0528, you will just need to replace R1 with the new 0528 quant: [docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/](https://docs.openwebui.com/tutoria - Network accessline 10051
1. <mark style="background-color:yellow;">**To run the full 720GB R1-0528 model,**</mark> [<mark style="background-color:yellow;">**see here**</mark>](#run-full-r1-0528-on-llama.cpp)<mark style="background-color:yellow;">**.**</mark> Obtain - Network accessline 10054
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10055
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10056
git clone https://github.com/ggml-org/llama.cpp - Network accessline 10071
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 10074
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10075
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10076
git clone https://github.com/ggerganov/llama.cpp - Network accessline 10113
3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . We <mark style="background-color:green;">**recommend using - Access to secretsline 10120
[Credential-related source omitted] - Network accessline 10157
You can also test our dynamic quants via [r/Localllama](https://www.reddit.com/r/LocalLLaMA/comments/1j7r47l/i_just_made_an_animation_of_a_ball_bouncing/) which tests the model on creating a basic physics engine to simulate balls rotating i - Network accessline 10159
[Credential-related source omitted] - Network accessline 10192
* [**DeepSeek-R1-0528-Qwen3-8B notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) **- new** - Network accessline 10209
**Magistral-Small-2509** is a reasoning LLM developed by Mistral AI. It excels at coding and mathematics and supports multiple languages. Magistral supports a 128k token context window and was finetuned from [**Mistral-Small-3.2**](https:/ - Network accessline 10221
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Mistral LLMs with minimal accuracy loss. - Network accessline 10225
<table><thead><tr><th width="255.64999389648438">Dynamic 2.0 GGUF (to run)</th><th width="305.25">Dynamic 4-bit (to finetune/deploy)</th><th>Dynamic Float8</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/Magi - Network accessline 10310
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 10312
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10312
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10315
2. Run the model with our dynamic quant. We did not set the context length automatically, so it will just use Ollama's default set context length.\ Note you can call `ollama serve &`in another terminal if it fails! We include all suggest - Network accessline 10316
Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Magistral supports 40K context lengths, so best to enable [**KV cache q - Network accessline 10317
3. Also Magistral supports 40K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves - Network accessline 10318
4. **Ollama also sets the default context length to 4096**, as [mentioned here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size). Use `OLLAMA_CONTEXT_LENGTH=8192` to change it to 8192. Magist - Network accessline 10328
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 10331
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10332
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10333
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 10359
[Credential-related source omitted] - Network accessline 11514
For Magistral versions before September 2025, [Xuan-Son](https://x.com/ngxson) from HuggingFace showed in their [GGUF repo](https://huggingface.co/ngxson/Devstral-Small-Vision-2505-GGUF) how it is actually possible to "graft" the vision enc - Network accessline 11537
* **Magistral 2509 Kaggle (2x Tesla T4s) free** [**finetuning notebook**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator= - Network accessline 11538
* Magistral 2509 Colab L4 (24GB) [finetuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Magistral_\(24B\)-Reasoning-Conversational.ipynb) - Network accessline 11540
Magistral slightly exceeds the memory limits of a 16GB VRAM, so fine-tuning it for free on Google Colab isn't possible for now. However, you *can* fine-tune the model for free using [Kaggle](https://www.kaggle.com/danielhanchen/code), which - Network accessline 11542
**To finetune on new reasoning traces, you can use our free** [**Kaggle notebook for Magistral**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversati - Network accessline 11569
* [vLLM's Float8 format](https://huggingface.co/unsloth/Magistral-Small-2509-FP8-Dynamic) - Network accessline 11570
* [TorchAO's Float8 format](https://huggingface.co/unsloth/Magistral-Small-2509-FP8-torchao) - Network accessline 11572
Both are fantastic to deploy via vLLM. Read up on using TorchAO based FP8 quants in vLLM [here](https://docs.vllm.ai/en/latest/features/quantization/torchao.html). - Network accessline 11601
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td>33.8GB</td><td><a href="https://huggingface.co/unsloth/Llama-4- - Network accessline 11609
| MoE Bits | Type | Disk Size | HF Link | | -------- | --------- | --------- | ---------------------------------------------------------------- - Network accessline 11610
| -------- | --------- | --------- | --------------------------------------------------------------------------------------------------- | | 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E- - Network accessline 11611
| 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_S) | - Network accessline 11612
| 1.93bit | IQ1\_M | 128GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_M) | - Network accessline 11613
| 2.42-bit | IQ2\_XXS | 140GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ2_XXS) | - Network accessline 11614
| 2.71-bit | Q2\_K\_XL | 151B | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q2_K_XL) | - Network accessline 11615
| 3.5-bit | Q3\_K\_XL | 193GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q3_K_XL) | - Network accessline 11616
| 4.5-bit | Q4\_K\_XL | 243GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q4_K_XL) | - Network accessline 11635
* A BOS token of `<|begin_of_text|>` is auto added during tokenization (do NOT add it manually!)  * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow - Network accessline 11636
* According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow;">**suggested optional system prompt**</mark>, which is listed below: - Network accessline 11654
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 11657
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11658
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11659
git clone https://github.com/ggml-org/llama.cpp - Network accessline 11666
2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/Llama-4-Scout-17B-16 - Access to secretsline 11671
[Credential-related source omitted] - Access to secretsline 11718
[Credential-related source omitted] - Network accessline 11753
[Credential-related source omitted] - Network accessline 11755
For Llama 4 Scout, we found we should not quantize the vision layers, and leave the MoE router and some other layers as unquantized - we upload these to <https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-dynamic-bnb-4bit - Network accessline 11757
[Credential-related source omitted] - Network accessline 11759
We also had to convert `torch.nn.Parameter` to `torch.nn.Linear` for the MoE layers to allow 4bit quantization to occur. This also means we had to rewrite and patch over the generic Hugging Face implementation. We upload our quantized versi - Network accessline 11761
[Credential-related source omitted] - Network accessline 11770
Kimi-K2-Instruct-0905 the new version of K2 achieves SOTA performance in knowledge, reasoning, coding, and agentic tasks. The full 1T parameter model from Moonshot AI requires 1.09TB of disk space, while the quantized **Unsloth Dynamic 1.8- - Network accessline 11772
You can now run **Kimi-K2-Instruct-0905** with our new GGUFs. Use our same settings below but ensure you change the model name from 'Kimi-K2-Instruct' to 'Kimi-K2-Instruct-0905': [K2-0905 GGUFs](https://huggingface.co/unsloth/Kimi-K2-Instru - Network accessline 11774
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized LLMs with minimal accuracy loss. - Network accessline 11776
<a href="https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally#run-kimi-k2-tutorials" class="button primary">Run in llama.cpp</a> - Network accessline 11800
According to [Moonshot AI](https://huggingface.co/moonshotai/Kimi-K2-Instruct), these are the recommended settings for Kimi K2 inference: - Network accessline 11846
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/Kimi-K2-Instruct-GGUF/tree/main/UD-TQ1_0">U - Network accessline 11848
We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/Kimi-K2-Instruct-BF16). - Network accessline 11852
{% hint style="success" %} You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model: - Network accessline 11853
You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model: - Network accessline 11858
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 11861
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11862
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11863
git clone https://github.com/ggml-org/llama.cpp - Network accessline 11898
3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-TQ1_0`(dynamic 1.8bit quant) or other quantized versions like `Q2_K_XL` . We <mark style="background-color:green;">**recommend using - Access to secretsline 11905
[Credential-related source omitted] - Network accessline 11916
{% hint style="info" %} If you find that downloads get stuck at 90 to 95% or so, please see <https://docs.unsloth.ai/basics/troubleshooting-and-faqs#downloading-gets-stuck-at-90-to-95> - Network accessline 11917
If you find that downloads get stuck at 90 to 95% or so, please see <https://docs.unsloth.ai/basics/troubleshooting-and-faqs#downloading-gets-stuck-at-90-to-95> - Network accessline 11944
**16th July 2025: Kimi K2 updated their tokenizer to enable multiple tool calls** as per <https://x.com/Kimi_Moonshot/status/1945050874067476962> - Network accessline 11946
**18th July 2025: We fixed a system prompt - Kimi tweeted about our fix as well here:** [**https://x.com/Kimi\_Moonshot/status/1946130043446690030**](https://x.com/Kimi_Moonshot/status/1946130043446690030)**. The fix was described here as w - Network accessline 11951
wget https://huggingface.co/unsloth/Kimi-K2-Instruct/raw/main/chat_template.jinja - Network accessline 11955
The Kimi K2 tokenizer was interesting to play around with - <mark style="background-color:green;">**it's mostly similar in action to GPT-4o's tokenizer**</mark>! We first see in the [tokenization\_kimi.py](https://huggingface.co/moonshotai/ - Network accessline 11972
After careful inspection, we find Kimi K2 is nearly identical to GPT-4o's tokenizer regex which can be found in [llama.cpp's source code](https://github.com/ggml-org/llama.cpp/blob/55c509daf51d25bfaee9c8b8ce6abff103d4473b/src/llama-vocab.cp - Network accessline 11982
Both tokenize numbers into groups of 1 to 3 numbers (9, 99, 999), and use similar patterns. The only difference looks to be the handling of "Han" or Chinese characters, which Kimi's tokenizer deals with more. [The PR](https://github.com/ggm - Network accessline 11988
We introduced the Flappy Bird test when our 1.58bit quants for DeepSeek R1 were provided. We found Kimi K2 one of the only models to one-shot all our tasks including this one, [Heptagon ](https://docs.unsloth.ai/models/deepseek-r1-0528-how- - Network accessline 12007
You can also test the dynamic quants via the Heptagon Test as per [r/Localllama](https://www.reddit.com/r/LocalLLaMA/comments/1j7r47l/i_just_made_an_animation_of_a_ball_bouncing/) which tests the model on creating a basic physics engine to - Network accessline 12009
<figure><img src="https://docs.unsloth.ai/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252F2O72oTw5yPUbcxXjDNKS%252Fsnapsh - Network accessline 12026
You can now run **Grok 2** (aka Grok 2.5), the 270B parameter model by xAI. Full precision requires **539GB**, while the Unsloth Dynamic 3-bit version shrinks size down to just **118GB** (a 75% reduction). GGUF: [Grok-2-GGUF](https://huggin - Network accessline 12028
The **3-bit Q3\_K\_XL** model runs on a single **128GB Mac** or **24GB VRAM + 128GB RAM**, achieving **5+ tokens/s** inference. Thanks to the llama.cpp team and community for [supporting Grok 2](https://github.com/ggml-org/llama.cpp/pull/15 - Network accessline 12030
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized Grok LLMs with minimal accuracy loss. - Network accessline 12064
{% stepper %} {% step %} Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if - Network accessline 12066
Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or - Network accessline 12069
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 12070
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 12071
git clone https://github.com/ggml-org/llama.cpp cd llama.cpp && git fetch origin pull/15539/head:MASTER && git checkout MASTER && cd .. cmake llama.cpp -B llama.cpp/build \ - Access to secretsline 12118
[Credential-related source omitted] - Network accessline 12156
| MoE Bits | Type + Link | Disk Size | Details | | -------- | ----------------------------------------------------------------------------------- | ----------- - Network accessline 12157
| -------- | ----------------------------------------------------------------------------------- | ----------- | ------------- | | 1.66bit | [TQ1\_0](https://huggingface.co/unsloth/grok-2-GGUF/blob/main/grok-2-UD-TQ1_0.gguf) | **81.8 GB** - Network accessline 12158
| 1.66bit | [TQ1\_0](https://huggingface.co/unsloth/grok-2-GGUF/blob/main/grok-2-UD-TQ1_0.gguf) | **81.8 GB** | 1.92/1.56bit | - Network accessline 12159
| 1.78bit | [IQ1\_S](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ1_S) | **88.9 GB** | 2.06/1.56bit | - Network accessline 12160
| 1.93bit | [IQ1\_M](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ1_M) | **94.5 GB** | 2.5/2.06/1.56 | - Network accessline 12161
| 2.42bit | [IQ2\_XXS](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ2_XXS) | **99.3 GB** | 2.5/2.06bit | - Network accessline 12162
| 2.71bit | [Q2\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q2_K_XL) | **112 GB** | 3.5/2.5bit | - Network accessline 12163
| 3.12bit | [IQ3\_XXS](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ3_XXS) | **117 GB** | 3.5/2.06bit | - Network accessline 12164
| 3.5bit | [Q3\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q3_K_XL) | **126 GB** | 4.5/3.5bit | - Network accessline 12165
| 4.5bit | [Q4\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q4_K_XL) | **155 GB** | 5.5/4.5bit | - Network accessline 12166
| 5.5bit | [Q5\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q5_K_XL) | **191 GB** | 6.5/5.5bit | - Network accessline 12180
The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You - Network accessline 12201
Finetuned from [**Mistral-Small-3.1**](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF), Devstral supports a 128k context window. Devstral Small 1.1 has improved performance, achieving a score of 53.6% performance on
optional-skills/mlops/training/unsloth/references/llms-txt.md
- Access to secretsline 12
[Credential-related source omitted] - Network accessline 35
{% hint style="success" %} Read more on running Llama 4 here: <https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4> - Network accessline 36
Read more on running Llama 4 here: <https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4> - Network accessline 75
Qwen3-Coder is Qwen’s new series of coding agent models, available in 30B (**Qwen3-Coder-Flash**) and 480B parameters. **Qwen3-480B-A35B-Instruct** achieves SOTA coding performance rivalling Claude Sonnet-4, GPT-4.1, and [Kimi K2](https://d - Network accessline 77
We also uploaded Qwen3-Coder with native <mark style="background-color:purple;">**1M context length**</mark> extended by YaRN and full-precision 8bit and 16bit versions. [Unsloth](https://github.com/unslothai/unsloth) also now supports fine - Network accessline 85
{% hint style="success" %} **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dy - Network accessline 86
**Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dynamic quant nearly matched - Network accessline 91
| Dynamic 2.0 GGUF (to run) | 1M Context Dyn - Network accessline 93
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF">30B-A3B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF">480B-A35B-Instruct</a></li></ul> | <ul><li><a hre - Network accessline 147
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 186
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 188
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 188
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 236
Orpheus supports tags like `<laugh>`, `<chuckle>`, `<sigh>`, `<cough>`, `<sniffle>`, `<groan>`, `<yawn>`, `<gasp>`, etc. For example: `"I missed you <laugh> so much!"`. These tags are enclosed in angle brackets and will be treated as speci - Network accessline 299
Unsloth model catalog for all our [Dynamic](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) GGUF, 4-bit, 16-bit models on Hugging Face. - Network accessline 309
| Model | Variant | GGUF | Instruct (4-bit) - Network accessline 310
| ------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------- | -------------------------------------- - Network accessline 311
| [**gpt-oss** ](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) | 120b | [link](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 312
| | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 313
| [**DeepSeek-V3.1**](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) | Terminus | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF) | — - Network accessline 314
| | V3.1 | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF) | — - Network accessline 315
| [**Qwen3-VL**](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune) | 2B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 316
| | 2B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 317
| | 4B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 318
| | 4B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 319
| | 8B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 320
| | 8B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 321
| | 30B-A3B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Instruct-GGUF) | — - Network accessline 322
| | 30B-A3B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF) | — - Network accessline 323
| | 32B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 324
| | 32B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Thinking-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 325
| | 235B-A22B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Instruct-GGUF) | — - Network accessline 326
| | 235B-A22B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Thinking-GGUF) | — - Network accessline 327
| [**Qwen3-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) | 30B-A3B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF) | — - Network accessline 328
| | 30B-A3B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF) | — - Network accessline 329
| | 235B-A22B-Thinking | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF/) | — - Network accessline 330
| | 235B-A22B-Instruct | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF/) | — - Network accessline 331
| **Qwen3-Coder** | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF) | — - Network accessline 332
| | 480B-A35B | [link](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF) | — - Network accessline 333
| **Granite-4.0 (new)** | H-Small | [link](https://huggingface.co/unsloth/granite-4.0-h-small-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 334
| **GLM (new)** | 4.6 | [link](https://huggingface.co/unsloth/GLM-4.6-GGUF) | — - Network accessline 335
| | 4.5-Air | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF) | — - Network accessline 336
| **Kimi-K2-0905** | 1T | [link](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF) | — - Network accessline 337
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 338
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 339
| **DeepSeek-R1-0528** | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 340
| | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF) | — - Network accessline 341
| **Mistral** | Magistral Small (2509) | [link](https://huggingface.co/unsloth/Magistral-Small-2509-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 342
| | Magistral Small (2507) | [link](https://huggingface.co/unsloth/Magistral-Small-2507-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 343
| | Small 3.2 24B (2506) | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 344
| FLUX.1 | Kontext-dev | [link](https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF) | — - Network accessline 345
| **Qwen3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 346
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 347
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 348
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 349
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 350
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 351
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 352
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF) | — - Network accessline 353
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 354
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | — - Network accessline 355
| **Grok 2** | 270B | [link](https://huggingface.co/unsloth/grok-2-GGUF) | — - Network accessline 356
| **Qwen-2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF) | — - Network accessline 357
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF) | — - Network accessline 358
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 359
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF) | [link](https://huggingface.co/unsloth/ - Network accessline 361
| Model | Variant | GGUF | Instruct (4-bit) | | ----------------- | ------- - Network accessline 362
| ----------------- | ---------------------- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | **DeepSeek-V3.1** | Terminu - Network accessline 363
| **DeepSeek-V3.1** | Terminus | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF) | | - Network accessline 364
| | V3.1 | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF) | | - Network accessline 365
| **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF) | — | - Network accessline 366
| | V3 | [link](https://huggingface.co/unsloth/DeepSeek-V3-GGUF) | — | - Network accessline 367
| **DeepSeek-R1** | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF) | — | - Network accessline 368
| | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit) | - Network accessline 369
| | R1 | [link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF) | — | - Network accessline 370
| | R1 Zero | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero-GGUF) | — | - Network accessline 371
| | Distill Llama 3 8 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit) | - Network accessline 372
| | Distill Llama 3.3 70 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-bnb-4bit) | - Network accessline 373
| | Distill Qwen 2.5 1.5 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit) | - Network accessline 374
| | Distill Qwen 2.5 7 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit) | - Network accessline 375
| | Distill Qwen 2.5 14 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit) | - Network accessline 376
| | Distill Qwen 2.5 32 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit) | - Network accessline 378
| Model | Variant | GGUF | Instruct (4-bit) | | ------------- | ------------ - Network accessline 379
| ------------- | ------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | **Llama 4** | Scout 17 B-1 - Network accessline 380
| **Llama 4** | Scout 17 B-16 E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) | - Network accessline 381
| | Maverick 17 B-128 E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | — | - Network accessline 382
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit) | - Network accessline 383
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit) | - Network accessline 384
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit) | - Network accessline 385
| | 11 B Vision | — | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit) | - Network accessline 386
| | 90 B Vision | — | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit) | - Network accessline 387
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit) | - Network accessline 388
| | 70 B | — | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit) | - Network accessline 389
| | 405 B | — | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct-bnb-4bit) | - Network accessline 390
| **Llama 3** | 8 B | — | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) | - Network accessline 391
| | 70 B | — | [link](https://huggingface.co/unsloth/llama-3-70b-bnb-4bit) | - Network accessline 392
| **Llama 2** | 7 B | — | [link](https://huggingface.co/unsloth/llama-2-7b-chat-bnb-4bit) | - Network accessline 393
| | 13 B | — | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 394
| **CodeLlama** | 7 B | — | [link](https://huggingface.co/unsloth/codellama-7b-bnb-4bit) | - Network accessline 395
| | 13 B | — | [link](https://huggingface.co/unsloth/codellama-13b-bnb-4bit) | - Network accessline 396
| | 34 B | — | [link](https://huggingface.co/unsloth/codellama-34b-bnb-4bit) | - Network accessline 398
| Model | Variant | GGUF | Instruct (4-bit) | | ------------ | ------------- | -------------------------------- - Network accessline 399
| ------------ | ------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------- | | **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/u - Network accessline 400
| **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit) | - Possible obfuscationline 400
| **Gemma 3n** | E2B | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit) | - Network accessline 401
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit) | - Network accessline 402
| **Gemma 3** | 270M | [link](https://huggingface.co/unsloth/gemma-3-270m-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-270m-it) | - Network accessline 403
| | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit) | - Network accessline 404
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) | - Network accessline 405
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-12b-it-unsloth-bnb-4bit) | - Network accessline 406
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3-27b-it-unsloth-bnb-4bit) | - Network accessline 407
| **MedGemma** | 4 B (vision) | [link](https://huggingface.co/unsloth/medgemma-4b-it-GGUF) | [link](https://huggingface.co/unsloth/medgemma-4b-it-unsloth-bnb-4bit) | - Network accessline 408
| | 27 B (vision) | [link](https://huggingface.co/unsloth/medgemma-27b-it-GGUF) | [link](https://huggingface.co/unsloth/medgemma-27b-text-it-unsloth-bnb-4bit) | - Network accessline 409
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit) | - Network accessline 410
| | 9 B | — | [link](https://huggingface.co/unsloth/gemma-2-9b-it-bnb-4bit) | - Network accessline 411
| | 27 B | — | [link](https://huggingface.co/unsloth/gemma-2-27b-it-bnb-4bit) | - Network accessline 413
| Model | Variant | GGUF | Instruct (4-bit) | | -------------------------- | ---- - Network accessline 414
| -------------------------- | ---------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | | **Qwen 3** | 0.6 - Network accessline 415
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-unsloth-bnb-4bit) | - Network accessline 416
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit) | - Network accessline 417
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-4B-unsloth-bnb-4bit) | - Network accessline 418
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit) | - Network accessline 419
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit) | - Network accessline 420
| | 30 B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-bnb-4bit) | - Network accessline 421
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF) | [link](https://huggingface.co/unsloth/Qwen3-32B-unsloth-bnb-4bit) | - Network accessline 422
| | 235 B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF) | — | - Network accessline 423
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF) | — | - Network accessline 424
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF) | — | - Network accessline 425
| **Qwen 2.5 VL** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit) | - Network accessline 426
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit) | - Network accessline 427
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit) | - Network accessline 428
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-unsloth-bnb-4bit) | - Network accessline 429
| **Qwen 2.5** | 0.5 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit) | - Network accessline 430
| | 1.5 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit) | - Network accessline 431
| | 3 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit) | - Network accessline 432
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-bnb-4bit) | - Network accessline 433
| | 14 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct-bnb-4bit) | - Network accessline 434
| | 32 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct-bnb-4bit) | - Network accessline 435
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct-bnb-4bit) | - Network accessline 436
| **Qwen 2.5 Coder (128 K)** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit) | - Network accessline 437
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit) | - Network accessline 438
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit) | - Network accessline 439
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit) | - Network accessline 440
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit) | - Network accessline 441
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit) | - Network accessline 442
| **QwQ** | 32 B | [link](https://huggingface.co/unsloth/QwQ-32B-GGUF) | [link](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit) | - Network accessline 443
| **QVQ (preview)** | 72 B | — | [link](https://huggingface.co/unsloth/QVQ-72B-Preview-bnb-4bit) | - Network accessline 444
| **Qwen 2 (chat)** | 1.5 B | — | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct-bnb-4bit) | - Network accessline 445
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct-bnb-4bit) | - Network accessline 446
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct-bnb-4bit) | - Network accessline 447
| **Qwen 2 VL** | 2 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit) | - Network accessline 448
| | 7 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit) | - Network accessline 449
| | 72 B | — | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct-bnb-4bit) | - Network accessline 451
<table><thead><tr><th width="174">Model</th><th>Variant</th><th>GGUF</th><th>Instruct (4-bit)</th></tr></thead><tbody><tr><td><strong>Mistral Small</strong></td><td>3.2-24 B (2506)</td><td><a href="https://huggingface.co/unsloth/Mistral-Sma - Network accessline 453
| Model | Variant | GGUF | Instruct (4-bit) | | ----------- | ---------------- | ------------------------ - Network accessline 454
| ----------- | ---------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](https://huggingfa - Network accessline 455
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-unsloth-bnb-4bit) | - Network accessline 456
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF) | [link](https://huggingface.co/unsloth/phi-4-reasoning-unsloth-bnb-4bit) | - Network accessline 457
| | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit) | - Network accessline 458
| | Phi-4 (instruct) | [link](https://huggingface.co/unsloth/phi-4-GGUF) | [link](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit) | - Network accessline 459
| | mini (instruct) | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit) | - Network accessline 460
| **Phi-3.5** | mini | — | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct-bnb-4bit) | - Network accessline 461
| **Phi-3** | mini | — | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit) | - Network accessline 462
| | medium | — | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct-bnb-4bit) | - Network accessline 466
| Model | Variant | GGUF | Instruct (4-bit) | | -------------- | ----------------- | ----- - Network accessline 467
| -------------- | ----------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | GLM | 4.5-Air | [link - Network accessline 468
| GLM | 4.5-Air | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF) | | - Network accessline 469
| | 4.5 | [4.5](https://huggingface.co/unsloth/GLM-4.5-GGUF) | | - Network accessline 470
| | 4-32B-0414 | [4-32B-0414](https://huggingface.co/unsloth/GLM-4-32B-0414-GGUF) | | - Network accessline 471
| Hunyuan | A13B | [link](https://huggingface.co/unsloth/Hunyuan-A13B-Instruct-GGUF) | — | - Network accessline 472
| Orpheus | 0.1-ft (3B) | [link](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit) | - Network accessline 473
| **LLava** | 1.5 (7 B) | — | [link](https://huggingface.co/unsloth/llava-1.5-7b-hf-bnb-4bit) | - Network accessline 474
| | 1.6 Mistral (7 B) | — | [link](https://huggingface.co/unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit) | - Network accessline 475
| **TinyLlama** | Chat | — | [link](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit) | - Network accessline 476
| **SmolLM 2** | 135 M | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-bnb-4bit) | - Network accessline 477
| | 360 M | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-bnb-4bit) | - Network accessline 478
| | 1.7 B | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-GGUF) | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-bnb-4bit) | - Network accessline 479
| **Zephyr-SFT** | 7 B | — | [link](https://huggingface.co/unsloth/zephyr-sft-bnb-4bit) | - Network accessline 480
| **Yi** | 6 B (v1.5) | — | [link](https://huggingface.co/unsloth/Yi-1.5-6B-bnb-4bit) | - Network accessline 481
| | 6 B (v1.0) | — | [link](https://huggingface.co/unsloth/yi-6b-bnb-4bit) | - Network accessline 482
| | 34 B (chat) | — | [link](https://huggingface.co/unsloth/yi-34b-chat-bnb-4bit) | - Network accessline 483
| | 34 B (base) | — | [link](https://huggingface.co/unsloth/yi-34b-bnb-4bit) | - Network accessline 489
| Model | Variant | Instruct (16-bit) | | -------------------- | ---------------------- | --------------------------------------------------------------- - Network accessline 490
| -------------------- | ---------------------- | -------------------------------------------------------------------------- | | **gpt-oss** (new) | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b) - Network accessline 491
| **gpt-oss** (new) | 20b | [link](https://huggingface.co/unsloth/gpt-oss-20b) | - Network accessline 492
| | 120b | [link](https://huggingface.co/unsloth/gpt-oss-120b) | - Network accessline 493
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | - Network accessline 494
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) | - Network accessline 495
| **DeepSeek-R1-0528** | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B) | - Network accessline 496
| | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528) | - Network accessline 497
| **Mistral** | Small 3.2 24B (2506) | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506) | - Network accessline 498
| | Small 3.1 24B (2503) | [link](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503) | - Network accessline 499
| | Small 3.0 24B (2501) | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501) | - Network accessline 500
| | Magistral Small (2506) | [link](https://huggingface.co/unsloth/Magistral-Small-2506) | - Network accessline 501
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | - Network accessline 502
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B) | - Network accessline 503
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B) | - Network accessline 504
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B) | - Network accessline 505
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B) | - Network accessline 506
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B) | - Network accessline 507
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B) | - Network accessline 508
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B) | - Network accessline 509
| **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | - Network accessline 510
| | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) | - Network accessline 511
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B) | - Network accessline 512
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B) | - Network accessline 513
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | - Network accessline 514
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning) | - Network accessline 516
| Model | Variant | Instruct (16-bit) | | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSe - Network accessline 517
| --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324) | | - Network accessline 518
| **DeepSeek-V3** | V3-0324 | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324) | - Network accessline 519
| | V3 | [link](https://huggingface.co/unsloth/DeepSeek-V3) | - Network accessline 520
| **DeepSeek-R1** | R1-0528 | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528) | - Network accessline 521
| | R1-0528-Qwen3-8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B) | - Network accessline 522
| | R1 | [link](https://huggingface.co/unsloth/DeepSeek-R1) | - Network accessline 523
| | R1 Zero | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero) | - Network accessline 524
| | Distill Llama 3 8B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) | - Network accessline 525
| | Distill Llama 3.3 70B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B) | - Network accessline 526
| | Distill Qwen 2.5 1.5B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B) | - Network accessline 527
| | Distill Qwen 2.5 7B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B) | - Network accessline 528
| | Distill Qwen 2.5 14B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B) | - Network accessline 529
| | Distill Qwen 2.5 32B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B) | - Network accessline 531
| Family | Variant | Instruct (16-bit) | | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4* - Network accessline 532
| ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | | - Network accessline 533
| **Llama 4** | Scout 17B-16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct) | - Network accessline 534
| | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) | - Network accessline 535
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct) | - Network accessline 536
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) | - Network accessline 537
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) | - Network accessline 538
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct) | - Network accessline 539
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct) | - Network accessline 540
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) | - Network accessline 541
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct) | - Network accessline 542
| | 405 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct) | - Network accessline 543
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct) | - Network accessline 544
| | 70 B | [link](https://huggingface.co/unsloth/llama-3-70b-Instruct) | - Network accessline 545
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b-chat) | - Network accessline 547
| Model | Variant | Instruct (16-bit) | | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3 - Network accessline 548
| ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | | | E4B | [link](https://huggingface.co/unsloth/gemma-3 - Network accessline 549
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | - Network accessline 550
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) | - Network accessline 551
| **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-it) | - Network accessline 552
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-it) | - Network accessline 553
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-it) | - Network accessline 554
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-it) | - Network accessline 555
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2b-it) | - Network accessline 556
| | 9 B | [link](https://huggingface.co/unsloth/gemma-9b-it) | - Network accessline 557
| | 27 B | [link](https://huggingface.co/unsloth/gemma-27b-it) | - Network accessline 559
| Family | Variant | Instruct (16-bit) | | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3 - Network accessline 560
| ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | | - Network accessline 561
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B) | - Network accessline 562
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B) | - Network accessline 563
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B) | - Network accessline 564
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B) | - Network accessline 565
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B) | - Network accessline 566
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B) | - Network accessline 567
| | 32 B | [link](https://huggingface.co/unsloth/Qwen3-32B) | - Network accessline 568
| | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B) | - Network accessline 569
| **Qwen 2.5 Omni** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B) | - Network accessline 570
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B) | - Network accessline 571
| **Qwen 2.5 VL** | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct) | - Network accessline 572
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct) | - Network accessline 573
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct) | - Network accessline 574
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct) | - Network accessline 575
| **Qwen 2.5** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct) | - Network accessline 576
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) | - Network accessline 577
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct) | - Network accessline 578
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct) | - Network accessline 579
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) | - Network accessline 580
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct) | - Network accessline 581
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct) | - Network accessline 582
| **Qwen 2.5 Coder 128 K** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K) | - Network accessline 583
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K) | - Network accessline 584
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K) | - Network accessline 585
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K) | - Network accessline 586
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K) | - Network accessline 587
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K) | - Network accessline 588
| **QwQ** | 32 B | [link](https://huggingface.co/unsloth/QwQ-32B) | - Network accessline 589
| **QVQ (preview)** | 72 B | — | | **Qwen 2 (Chat)** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) | | - Network accessline 590
| **Qwen 2 (Chat)** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) | - Network accessline 591
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct) | - Network accessline 592
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct) | - Network accessline 593
| **Qwen 2 VL** | 2 B | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct) | - Network accessline 594
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct) | - Network accessline 595
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct) | - Network accessline 597
| Model | Variant | Instruct (16-bit) | | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral** | Small - Network accessline 598
| ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral** | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | | **Mistral** | Large - Network accessline 599
| **Mistral** | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | - Network accessline 600
| **Mistral** | Large 2407 | [link](https://huggingface.co/unsloth/Mistral-Large-Instruct-2407) | - Network accessline 601
| **Mistral** | 7B v0.3 | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3) | - Network accessline 602
| **Mistral** | 7B v0.2 | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) | - Network accessline 603
| **Pixtral** | 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-2409) | - Network accessline 604
| **Mixtral** | 8×7B | [link](https://huggingface.co/unsloth/Mixtral-8x7B-Instruct-v0.1) | - Network accessline 605
| **Mistral NeMo** | 12B 2407 | [link](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) | - Network accessline 606
| **Devstral** | Small 2505 | [link](https://huggingface.co/unsloth/Devstral-Small-2505) | - Network accessline 608
| Model | Variant | Instruct (16-bit) | | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](ht - Network accessline 609
| ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | | | Reasoning | [link](ht - Network accessline 610
| **Phi-4** | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus) | - Network accessline 611
| | Reasoning | [link](https://huggingface.co/unsloth/Phi-4-reasoning) | - Network accessline 612
| | Phi-4 (core) | [link](https://huggingface.co/unsloth/Phi-4) | - Network accessline 613
| | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning) | - Network accessline 614
| | Mini | [link](https://huggingface.co/unsloth/Phi-4-mini) | - Network accessline 615
| **Phi-3.5** | Mini | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) | - Network accessline 616
| **Phi-3** | Mini | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) | - Network accessline 617
| | Medium | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) | - Network accessline 621
| Model | Instruct (16-bit) | | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft) | [link](https://huggingfac - Network accessline 622
| ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft) | | Orpheus-3B (v0.1 pt) | [link](https://huggingfac - Network accessline 623
| Orpheus-3B (v0.1 ft) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft) | - Network accessline 624
| Orpheus-3B (v0.1 pt) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | - Network accessline 625
| Sesame-CSM 1B | [link](https://huggingface.co/unsloth/csm-1b) | - Network accessline 626
| Whisper Large V3 (STT) | [link](https://huggingface.co/unsloth/whisper-large-v3) | - Network accessline 627
| Llasa-TTS 1B | [link](https://huggingface.co/unsloth/Llasa-1B) | - Network accessline 628
| Spark-TTS 0.5B | [link](https://huggingface.co/unsloth/Spark-TTS-0.5B) | - Network accessline 629
| Oute-TTS 1B | [link](https://huggingface.co/unsloth/Llama-OuteTTS-1.0-1B) | - Network accessline 635
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------ | ----------------- | ---------- - Network accessline 636
| ------------ | ----------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | **Gemma 3n** | E2B | [link](htt - Network accessline 637
| **Gemma 3n** | E2B | [link](https://huggingface.co/unsloth/gemma-3n-E2B) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-unsloth-bnb-4bit) | - Network accessline 638
| | E4B | [link](https://huggingface.co/unsloth/gemma-3n-E4B) | [link](https://huggingface.co/unsloth/gemma-3n-E4B-unsloth-bnb-4bit) | - Network accessline 639
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit) | - Network accessline 640
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit) | - Network accessline 641
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-Base) | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit) | - Network accessline 642
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-Base) | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit) | - Network accessline 643
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-Base) | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit) | - Network accessline 644
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-bnb-4bit) | - Network accessline 645
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) | - Network accessline 646
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 650
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------- | ----------------- | ----------------------------------- - Network accessline 651
| ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unslo - Network accessline 652
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | — | - Network accessline 653
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 654
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B) | — | - Network accessline 655
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B) | — | - Network accessline 656
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B) | — | - Network accessline 657
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision) | — | - Network accessline 658
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision) | — | - Network accessline 659
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) | — | - Network accessline 660
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B) | — | - Network accessline 661
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b) | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) | - Network accessline 662
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b) | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit) | - Network accessline 663
| | 13 B | [link](https://huggingface.co/unsloth/llama-2-13b) | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 665
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------ | ------- | ------------------------------------------------- - Network accessline 666
| ------------ | ------- | --------------------------------------------------------- | -------------------------------------------------------------------------- | | **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B- - Network accessline 667
| **Qwen 3** | 0.6 B | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base) | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit) | - Network accessline 668
| | 1.7 B | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base) | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit) | - Network accessline 669
| | 4 B | [link](https://huggingface.co/unsloth/Qwen3-4B-Base) | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit) | - Network accessline 670
| | 8 B | [link](https://huggingface.co/unsloth/Qwen3-8B-Base) | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit) | - Network accessline 671
| | 14 B | [link](https://huggingface.co/unsloth/Qwen3-14B-Base) | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit) | - Network accessline 672
| | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-unsloth-bnb-4bit) | - Network accessline 673
| **Qwen 2.5** | 0.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B) | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-bnb-4bit) | - Network accessline 674
| | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B) | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-bnb-4bit) | - Network accessline 675
| | 3 B | [link](https://huggingface.co/unsloth/Qwen2.5-3B) | [link](https://huggingface.co/unsloth/Qwen2.5-3B-bnb-4bit) | - Network accessline 676
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2.5-7B) | [link](https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit) | - Network accessline 677
| | 14 B | [link](https://huggingface.co/unsloth/Qwen2.5-14B) | [link](https://huggingface.co/unsloth/Qwen2.5-14B-bnb-4bit) | - Network accessline 678
| | 32 B | [link](https://huggingface.co/unsloth/Qwen2.5-32B) | [link](https://huggingface.co/unsloth/Qwen2.5-32B-bnb-4bit) | - Network accessline 679
| | 72 B | [link](https://huggingface.co/unsloth/Qwen2.5-72B) | [link](https://huggingface.co/unsloth/Qwen2.5-72B-bnb-4bit) | - Network accessline 680
| **Qwen 2** | 1.5 B | [link](https://huggingface.co/unsloth/Qwen2-1.5B) | [link](https://huggingface.co/unsloth/Qwen2-1.5B-bnb-4bit) | - Network accessline 681
| | 7 B | [link](https://huggingface.co/unsloth/Qwen2-7B) | [link](https://huggingface.co/unsloth/Qwen2-7B-bnb-4bit) | - Network accessline 685
| Model | Variant | Base (16-bit) | Base (4-bit) | | ------------- | ----------------- | ----------------------------------- - Network accessline 686
| ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unslo - Network accessline 687
| **Llama 4** | Scout 17B 16E | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E) | — | - Network accessline 688
| | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | — | - Network accessline 689
| **Llama 3.3** | 70 B | [link](https://huggingface.co/unsloth/Llama-3.3-70B) | — | - Network accessline 690
| **Llama 3.2** | 1 B | [link](https://huggingface.co/unsloth/Llama-3.2-1B) | — | - Network accessline 691
| | 3 B | [link](https://huggingface.co/unsloth/Llama-3.2-3B) | — | - Network accessline 692
| | 11 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision) | — | - Network accessline 693
| | 90 B Vision | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision) | — | - Network accessline 694
| **Llama 3.1** | 8 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) | — | - Network accessline 695
| | 70 B | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B) | — | - Network accessline 696
| **Llama 3** | 8 B | [link](https://huggingface.co/unsloth/llama-3-8b) | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) | - Network accessline 697
| **Llama 2** | 7 B | [link](https://huggingface.co/unsloth/llama-2-7b) | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit) | - Network accessline 698
| | 13 B | [link](https://huggingface.co/unsloth/llama-2-13b) | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) | - Network accessline 700
| Model | Variant | Base (16-bit) | Base (4-bit) | | ----------- | ------- | ----------------------------------------------------- | --- - Network accessline 701
| ----------- | ------- | ----------------------------------------------------- | ---------------------------------------------------------------------- | | **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-pt) | [li - Network accessline 702
| **Gemma 3** | 1 B | [link](https://huggingface.co/unsloth/gemma-3-1b-pt) | [link](https://huggingface.co/unsloth/gemma-3-1b-pt-unsloth-bnb-4bit) | - Network accessline 703
| | 4 B | [link](https://huggingface.co/unsloth/gemma-3-4b-pt) | [link](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-bnb-4bit) | - Network accessline 704
| | 12 B | [link](https://huggingface.co/unsloth/gemma-3-12b-pt) | [link](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit) | - Network accessline 705
| | 27 B | [link](https://huggingface.co/unsloth/gemma-3-27b-pt) | [link](https://huggingface.co/unsloth/gemma-3-27b-pt-unsloth-bnb-4bit) | - Network accessline 706
| **Gemma 2** | 2 B | [link](https://huggingface.co/unsloth/gemma-2-2b) | — | - Network accessline 707
| | 9 B | [link](https://huggingface.co/unsloth/gemma-2-9b) | — | - Network accessline 708
| | 27 B | [link](https://huggingface.co/unsloth/gemma-2-27b) | — | - Network accessline 712
| Model | Variant | Base (16-bit) | Base (4-bit) | | ----------- | ---------------- | ----------------------------------- - Network accessline 713
| ----------- | ---------------- | ------------------------------------------------------------------ | --------------------------------------------------------------- | | **Mistral** | Small 24B 2501 | [link](https://huggingface.co/unslo - Network accessline 714
| **Mistral** | Small 24B 2501 | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) | — | - Network accessline 715
| | NeMo 12B 2407 | [link](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) | — | - Network accessline 716
| | 7B v0.3 | [link](https://huggingface.co/unsloth/mistral-7b-v0.3) | [link](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) | - Network accessline 717
| | 7B v0.2 | [link](https://huggingface.co/unsloth/mistral-7b-v0.2) | [link](https://huggingface.co/unsloth/mistral-7b-v0.2-bnb-4bit) | - Network accessline 718
| | Pixtral 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-Base-2409) | — | - Network accessline 722
| Model | Variant | Base (16-bit) | Base (4-bit) | | -------------- | -------------- | ----------------- - Network accessline 723
| -------------- | -------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **TinyLlama** | 1.1 B (Base) | [link](https://hu - Network accessline 724
| **TinyLlama** | 1.1 B (Base) | [link](https://huggingface.co/unsloth/tinyllama) | [link](https://huggingface.co/unsloth/tinyllama-bnb-4bit) | - Network accessline 725
| **Orpheus-3b** | 0.1-pretrained | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained-unsloth-bnb-4bit) | - Network accessline 749
Docker might be the easiest way for Windows users to get started with Unsloth as there is no setup needed or dependency issues. [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell - Network accessline 751
For installation instructions, please follow our [Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker), otherwise here is a quickstart guide: - Network accessline 758
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other). Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/ins - Dangerous shell operationline 761
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 770
[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. - Network accessline 774
Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. Access the `unsloth-notebooks` tabs to see Unsloth notebooks. - Network accessline 779
If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 793
You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx) - Network accessline 801
* Launch the Installer here: [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/) - Network accessline 825
Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive). - Network accessline 827
Then install Miniconda (which has Python) here: [https://www.anaconda.com/docs/getting-started/miniconda/install](https://www.anaconda.com/docs/getting-started/miniconda/install#quickstart-install-instructions) - Network accessline 833
You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully. [Install PyTorch](https://pytorch.org/get-started/locally/) - Network accessline 850
* Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12) - Network accessline 857
1. Install `torch` and `triton`. Go to <https://pytorch.org> to install it. For example `pip install torch torchvision torchaudio triton` - Network accessline 858
2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers. 3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succee - Network accessline 859
3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to <https://github.com/facebookresearch/xformers>. Another option is to install `fl - Network accessline 860
4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-m - Network accessline 867
1. **Install NVIDIA CUDA Toolkit**: * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 868
* Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 870
* **Note**: No additional setup is required after installation for Unsloth. 2. **Install Microsoft C++ Build Tools**: * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.mic - Network accessline 871
2. **Install Microsoft C++ Build Tools**: * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). * During installation, select the ** - Network accessline 872
* Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). - Network accessline 886
* Verify: Open a new terminal and type `cl`. It should show version info. 4. **Install Conda** 1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 887
4. **Install Conda** 1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) 2. Follow installation instruction from the website - Network accessline 888
1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 894
1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**. - Network accessline 911
1. Install python though [Python's official site](https://www.python.org/downloads/windows/). - Network accessline 958
pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1014
{% hint style="info" %} Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> (May 28th 2025 update) to learn on how to run DeepSeek faster and more efficiently! - Network accessline 1015
Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> (May 28th 2025 update) to learn on how to run DeepSeek faster and more efficiently! - Network accessline 1022
<table data-full-width="true"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Accuracy</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td><strong>173GB</strong></td><td>Ok</td><td><a hr - Network accessline 1030
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324), these are the recommended settings for inference: - Network accessline 1032
* <mark style="background-color:blue;">**Temperature of 0.3**</mark> (Maybe 0.0 for coding as [seen here](https://api-docs.deepseek.com/quick_start/parameter_settings)) - Network accessline 1041
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 1047
2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . <mark style="background-color:green;">**I recommend using - Network accessline 1055
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1056
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1057
git clone https://github.com/ggml-org/llama.cpp - Network accessline 1081
> **Try QAT with our free** [**Qwen3 (4B) notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb) - Network accessline 1096
[Credential-related source omitted] - Network accessline 1100
Dequantizing back to 16bits simply does the reverse operation by `float16(qW) / a` . Post-training quantization (PTQ) can greatly reduce storage and inference costs, but quite often degrades accuracy when representing high-precision values - Network accessline 1104
[Credential-related source omitted] - Network accessline 1106
To enable smarter quantization, we collaborated with the [TorchAO](https://github.com/pytorch/ao) team to add **Quantization-Aware Training (QAT)** directly inside of Unsloth - so now you can fine-tune models in Unsloth and then export them - Network accessline 1116
[Credential-related source omitted] - Network accessline 1176
Qwen released 2507 (July 2025) updates for their [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune) 4B, 30B and 235B models, introducing both "thinking" and "non-thinking" variants. The non-thinking '**Qwen3-30B-A3B-Inst - Network accessline 1180
[Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3-2507 models — 2x faster, with 70% less VRAM, and 8 - Network accessline 1184
**Unsloth** [**Dynamic 2.0**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **GGUFs:** - Network accessline 1186
| Model | GGUFs to run: | | ------------------------ | ---------------------- - Network accessline 1187
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Qwen3-**4B-2507** | [Instruct](https://hug - Network accessline 1188
| Qwen3-**4B-2507** | [Instruct](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF) • [Thinking ](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF) | - Network accessline 1189
| Qwen3-**30B-A3B**-2507 | [Instruct](#llama.cpp-run-qwen3-30b-a3b-instruct-2507-tutorial) • [Thinking](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF) | - Network accessline 1190
| Qwen3-**235B-A22B**-2507 | [Instruct](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF) • [Thinking](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF) | - Network accessline 1240
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 1261
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 1263
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 1263
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 1273
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1274
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1275
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 1331
[Credential-related source omitted] - Access to secretsline 1352
[Credential-related source omitted] - Network accessline 1395
curl -X POST http://localhost:8000/v1/load_lora_adapter \ - Network accessline 1405
curl -X POST http://localhost:8000/v1/unload_lora_adapter \ - Network accessline 1436
* E.g. For image-based training, select a vision model such as *Llama 3.2 Vision*. For code datasets, opt for a specialized model like *Qwen Coder 2.5*. * **Licensing and Requirements**: Different models may have specific licensing terms an - Network accessline 1437
* **Licensing and Requirements**: Different models may have specific licensing terms and [system requirements](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#system-requirements). Be sure to review these carefully to avoid - Network accessline 1442
* Use our [VRAM guideline](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#approximate-vram-requirements-based-on-model-parameters) to determine the VRAM requirements for the model you’re considering. - Network accessline 1448
* We recommend using the latest model for the best performance and capabilities. For instance, as of January 2025, the leading 70B model is *Llama 3.3*. * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get- - Network accessline 1449
* You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-started/all-our-models) to find the newest and relevant options. - Network accessline 1464
Base models, on the other hand, are the original pre-trained versions without instruction fine-tuning. These are specifically designed for customization through fine-tuning, allowing you to adapt them to your unique needs. Base models are c - Network accessline 1471
* **300–1,000 Rows of High-Quality Data**: With a medium-sized, high-quality dataset, fine-tuning the base or instruct model are both viable options. * **Less than 300 Rows**: For smaller datasets, the instruct model is typically the better - Network accessline 1473
* For information how how big your dataset should be, [see here](https://docs.unsloth.ai/get-started/datasets-guide#how-big-should-my-dataset-be) - Network accessline 1481
* Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi - Network accessline 1507
[Credential-related source omitted] - Network accessline 1538
* You can use our [Magistral-2509 Kaggle notebook](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/magistral-how-to-run-and-fine-tune#fine-tuning-magistral-with-unsloth) as an example which utilizes multi-GPU Unsloth - Network accessline 1551
* [unsloth-5090-multiple](https://github.com/thad0ctor/unsloth-5090-multiple): A fork enabling Unsloth to run efficiently on multi-GPU systems, particularly for the NVIDIA [RTX 5090](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blac - Network accessline 1552
* [opensloth](https://github.com/anhvth/opensloth): Unsloth with support for multi-GPU training including experimental features. - Network accessline 1554
**Stay tuned for our official announcement!**\ For more details, check out our ongoing [Pull Request](https://github.com/unslothai/unsloth/issues/2435) discussing multi-GPU support. - Network accessline 1555
For more details, check out our ongoing [Pull Request](https://github.com/unslothai/unsloth/issues/2435) discussing multi-GPU support. - Network accessline 1581
If you're a beginner, here might be the first questions you'll ask before your first fine-tune. You can also always ask our community by joining our [Reddit page](https://www.reddit.com/r/unsloth/). - Network accessline 1585
[Credential-related source omitted] - Network accessline 1593
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly - Network accessline 1661
Unsloth supports natively 2x faster inference. For our inference only notebook, click [here](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing). - Network accessline 1679
Sometimes when you execute a cell [this error](https://github.com/googlecolab/colabtools/issues/3409) can appear. To solve this, in a new cell, run the below: - Network accessline 1700
{% hint style="success" %} Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> for an updated DeepSeek R1-0528 (May 28th 2025 version) - Network accessline 1701
Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> for an updated DeepSeek R1-0528 (May 28th 2025 version) - Network accessline 1706
1. Do not forget about `<|User|>` and `<|Assistant|>` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at: [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp). You can follow the build instruc - Network accessline 1707
2. Obtain the latest `llama.cpp` at: [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp). You can follow the build instructions below as well: - Network accessline 1716
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1717
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1718
git clone https://github.com/ggerganov/llama.cpp - Network accessline 1770
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This means the memory space for inference and training can in theory be re-used, since inference and training are separate modes - this is where [vLLM's sleep mode feature](https://docs.vllm.ai/en/latest/features/sleep_mode.html#rlhf-weight - Network accessline 1828
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<table data-full-width="true"><thead><tr><th>Experiments</th><th>Config</th><th>Status</th><th>GPU Memory usage</th><th>Comments</th></tr></thead><tbody><tr><td><ol><li><a href="https://colab.research.google.com/drive/18CssBY5C0mStnLvu2Hlt4 - Network accessline 1850
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We now select better compilation flags and reduce compile times by 50% or more. We also managed to dynamically patch any vLLM version to handle `gc.collect` better for backwards compatibility reasons, as inspired from this [vLLM pull reques - Network accessline 1866
All our GRPO notebooks have Unsloth Standby on by default and all optimizations! See <https://docs.unsloth.ai/get-started/unsloth-notebooks> for all our GRPO notebooks, or try the below: - Network accessline 1868
* [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA - Network accessline 1869
* [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecases) - Network accessline 1870
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 1871
* [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA - Network accessline 1872
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 1873
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb) - Network accessline 1874
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) - Network accessline 1875
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Access to secretsline 1882
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{% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew - Network accessline 2028
If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should - Network accessline 2033
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You can then save your model to GGUF, Ollama etc. by following our [guide here](https://docs.unsloth.ai/fine-tuning-llms-guide#id-7.-running--saving-the-model). - Network accessline 2051
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We have multiple options for saving your fine-tuned model, but we’ll focus on the easiest and most popular approaches which you can read more about [here](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 2095
Install the latest PyTorch, TorchAO, Xformers from <https://pytorch.org/> - Network accessline 2113
And that's it! Try some examples in our [**Unsloth Notebooks**](https://docs.unsloth.ai/get-started/unsloth-notebooks) page! - Network accessline 2117
You can use our :ledger:[gpt-oss RL auto win 2048](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game_BF16.ipynb) example on a MI300X (192GB) GPU. The goal is to play the 2048 game automatic - Network accessline 2122
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You can also use our :ledger:[automatic kernel gen RL notebook](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_GRPO_BF16.ipynb) also with gpt-oss to auto create matrix multiplication kernels in Python. The notebook also - Network accessline 2159
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AMD GPUs also need the bitsandbytes `blocksize` to be 128 and not 64 - this also means our pre-quantized models (for example [unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)) fr - Network accessline 2183
pip install --upgrade torch==2.8.0 pytorch-triton-rocm torchvision torchaudio torchao==0.13.0 xformers --index-url https://download.pytorch.org/whl/rocm6.4 - Network accessline 2188
pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 2189
pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 2190
pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth" - Network accessline 2394
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bash NOTEBOOK_URL="https://raw.githubusercontent.com/unslothai/notebooks/refs/heads/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb" wget -O "gpt_oss_20B_RL_2048_Game.ipynb" "$NOTEBOOK_URL" - Network accessline 2429
NOTEBOOK_URL="https://raw.githubusercontent.com/unslothai/notebooks/refs/heads/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb" - Network accessline 2430
wget -O "gpt_oss_20B_RL_2048_Game.ipynb" "$NOTEBOOK_URL" - Network accessline 2435
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Don't forget Unsloth also allows you to [save and run](https://docs.unsloth.ai/basics/running-and-saving-models) your models after fine-tuning so you can locally deploy them directly on your DGX Spark after. - Network accessline 2441
Many thanks to [Lakshmi Ramesh](https://www.linkedin.com/in/rlakshmi24/) and [Barath Anandan](https://www.linkedin.com/in/barathsa/) from NVIDIA for helping Unsloth’s DGX Spark launch and building the Docker image. - Network accessline 2447
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Thanks to Tim from [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) for providing a great fine-tuning tutorial with Unsloth on DGX Spark: - Network accessline 2453
{% embed url="<https://www.youtube.com/watch?t=962s&v=zs-J9sKxvoM>" %} - Network accessline 2471
[Credential-related source omitted] - Network accessline 2478
Inside the container, start Jupyter and run the required notebook. You can use the Reinforcement Learning gpt-oss 20b to win 2048 [notebook here](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_204 - Network accessline 2480
[Credential-related source omitted] - Network accessline 2504
</strong> "unsloth/gpt-oss-20b", # 20B model using MXFP4 format "unsloth/gpt-oss-120b", ] # More models at https://huggingface.co/unsloth - Network accessline 2506
] # More models at https://huggingface.co/unsloth - Network accessline 2523
Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 2525
{% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high. - Network accessline 2526
To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.  - Network accessline 2531
For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated - Network accessline 2541
[Credential-related source omitted] - Network accessline 2543
One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` , - Network accessline 2545
{% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 2546
🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 2549
Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide). - Network accessline 2552
We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet - Network accessline 2558
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1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 2599
As always, feel free to drop by our [Discord](https://discord.com/invite/unsloth) or [Reddit](https://www.reddit.com/r/unsloth/) if you need any help. - Network accessline 2605
Yes you can now [save/export your gpt-oss fine-tuned](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training) model using Unsloth's new update! - Network accessline 2617
Yes! Unsloth now supports RL for gpt-oss with GRPO/GSPO. We made it work on a free Kaggle notebook and achieved the fastest inference for RL. [Read more here](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) - Network accessline 2619
***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 2686
* The [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb) is for continued pretraining/raw text. - Network accessline 2687
* The [continued pretraining notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) is for learning another language. - Network accessline 2689
You can read more about continued pretraining and our release in our [blog post](https://unsloth.ai/blog/contpretraining). - Network accessline 2996
1. We find using lower KV cache quantization (4bit) seems to degrade generation quality via empirical tests - more tests need to be done, but we suggest using `q8_0` cache quantization. The goal of quantization is to support longer context - Network accessline 2997
2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dynamic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices. 3. Using `llama.cpp` - Network accessline 2998
3. Using `llama.cpp` 's Flash Attention backend does result in somewhat faster decoding speeds. Use `-DGGML_CUDA_FA_ALL_QUANTS=ON` when compiling. Note it's also best to set your CUDA architecture as found in <https://developer.nvidia.com/c - Network accessline 3023
Kimi-K2-Instruct-0905 the new version of K2 achieves SOTA performance in knowledge, reasoning, coding, and agentic tasks. The full 1T parameter model from Moonshot AI requires 1.09TB of disk space, while the quantized **Unsloth Dynamic 1.8- - Network accessline 3025
You can now run **Kimi-K2-Instruct-0905** with our new GGUFs. Use our same settings below but ensure you change the model name from 'Kimi-K2-Instruct' to 'Kimi-K2-Instruct-0905': [K2-0905 GGUFs](https://huggingface.co/unsloth/Kimi-K2-Instru - Network accessline 3027
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized LLMs with minimal accuracy loss. - Network accessline 3029
<a href="https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally#run-kimi-k2-tutorials" class="button primary">Run in llama.cpp</a> - Network accessline 3053
According to [Moonshot AI](https://huggingface.co/moonshotai/Kimi-K2-Instruct), these are the recommended settings for Kimi K2 inference: - Network accessline 3078
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/Kimi-K2-Instruct-GGUF/tree/main/UD-TQ1_0">U - Network accessline 3080
We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/Kimi-K2-Instruct-BF16). - Network accessline 3084
{% hint style="success" %} You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model: - Network accessline 3085
You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model: - Network accessline 3090
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 3106
3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-TQ1_0`(dynamic 1.8bit quant) or other quantized versions like `Q2_K_XL` . We <mark style="background-color:green;">**recommend using - Network accessline 3146
Also see our GitHub repo for our notebooks: [github.com/unslothai/notebooks](https://github.com/unslothai/notebooks/) - Network accessline 3152
* [**gpt-oss (20b)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) • [Inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\) - Network accessline 3153
* [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new** - Network accessline 3154
* [Qwen3 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) • [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL - Network accessline 3155
* [**Qwen3-2507-4B**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) • [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) • [Instruct](https://colab.r - Network accessline 3156
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversation - Network accessline 3157
* [IBM Granite-4.0-H](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) - new - Network accessline 3158
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Vision](https://colab - Network accessline 3159
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)  - Network accessline 3160
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Alpaca.ipynb) • [Llama 3.2 (1B + 3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3 - Network accessline 3164
* [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) (automatic kernels creation) - new - Network accessline 3165
* [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) (auto win 2048 game) - new - Network accessline 3166
* [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision **GSPO** - new - Network accessline 3167
* [Qwen3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA - Network accessline 3168
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 3169
* [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecase) - Network accessline 3170
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 3171
* [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA - Network accessline 3172
* [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 3173
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)  - Network accessline 3174
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) - Network accessline 3178
* [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) - new - Network accessline 3179
* [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb) - Network accessline 3180
* [Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Speech-to-Text (STT) - Network accessline 3181
* [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb) - Network accessline 3182
* [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb) - Network accessline 3183
* [Oute-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_\(1B\).ipynb) - Network accessline 3187
* [Whisper-Large-V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Network accessline 3188
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - Audio - Network accessline 3192
* [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) **- new** - Network accessline 3193
* [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new** - Network accessline 3194
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - vision - Network accessline 3195
* [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - vision - Network accessline 3196
* [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb) - Network accessline 3197
* [Qwen2.5-VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb) - Network accessline 3198
* [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb) - Network accessline 3199
* [Qwen3-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 3200
* [Qwen2.5-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) - Vision GSPO - Network accessline 3201
* [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new - Network accessline 3211
* [gpt-oss-120b ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(120B\)_A100-Fine-tuning.ipynb)- new - Network accessline 3212
* [Qwen3 (32B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(32B\)_A100-Reasoning-Conversational.ipynb) - new - Network accessline 3213
* [Llama 3.3 (70B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.3_\(70B\)_A100-Conversational.ipynb) - new - Network accessline 3214
* [Gemma 3 (27B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(27B\)_A100-Conversational.ipynb) - new - Network accessline 3218
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 3219
* [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new** - Network accessline 3220
* [**ModernBERT-large**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) **- new** as of Aug 19 - Network accessline 3221
* [**Synthetic Data Generation Llama 3.2 (3B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_\(3B\).ipynb) - new - Network accessline 3222
* [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new** - Network accessline 3223
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 3224
* [Mistral v0.3 Instruct (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 3225
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) - Network accessline 3226
* [ORPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb) - Network accessline 3227
* [Continued Pretraining](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 3228
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 3229
* [***Inference only***](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Inference.ipynb) - Network accessline 3230
* [Llama 3 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Alpaca.ipynb) - Network accessline 3234
* [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new** - Network accessline 3235
* [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new** - Network accessline 3236
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 3237
* [**BERT - Text Classification**](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) **- new as of Aug 19** - Network accessline 3238
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) - Network accessline 3239
* [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new** - Network accessline 3240
* [Continued Pretraining (CPT)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 3241
* [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail - Network accessline 3242
* [KTO](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) by Jeffrey - Network accessline 3243
* [Inference chat UI](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb) - Network accessline 3244
* [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 3245
* [ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing) - Network accessline 3246
* [Text Completion](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb) - Network accessline 3250
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 3251
* [Gemma 2 (9B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(9B\)-Alpaca.ipynb) - Network accessline 3252
* [Mistral NeMo (12B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Nemo_\(12B\)-Alpaca.ipynb) - Network accessline 3253
* [Phi-3.5 (mini)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) - Network accessline 3254
* [Phi-3 (medium)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3_Medium-Conversational.ipynb) - Network accessline 3255
* [Gemma 2 (2B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(2B\)-Alpaca.ipynb) - Network accessline 3256
* [Qwen 2.5 Coder (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(14B\)-Conversational.ipynb) - Network accessline 3257
* [Mistral Small (22B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_\(22B\)-Alpaca.ipynb) - Network accessline 3258
* [TinyLlama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/TinyLlama_\(1.1B\)-Alpaca.ipynb) - Network accessline 3259
* [CodeGemma (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_\(7B\)-Conversational.ipynb) - Network accessline 3260
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Alpaca.ipynb) - Network accessline 3261
* [Qwen2 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_\(7B\)-Alpaca.ipynb) - Network accessline 3265
* [**gpt-oss (20B)**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-gpt-oss-\(20B\)-Fine-tuning.ipynb\&accelerator=nvidiaTeslaT4) **- new** - Network accessline 3266
* [Gemma 3n (E4B)](https://www.kaggle.com/code/danielhanchen/gemma-3n-4b-multimodal-finetuning-inference) - Network accessline 3267
* [Qwen3 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(14B\).ipynb) - Network accessline 3268
* [Magistral-2509 (24B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=nvidiaTeslaT4) - new - Network accessline 3269
* [Gemma 3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(4B\).ipynb) - Network accessline 3270
* [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4-Conversational.ipynb) - Network accessline 3271
* [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Alpaca.ipynb) - Network accessline 3272
* [Llama 3.2 (1B + 3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 3273
* [Qwen 2.5 (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(7B\)-Alpaca.ipynb) - Network accessline 3277
* [**Qwen2.5-VL**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_5_7B_VL_GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Vision GRPO - new - Network accessline 3278
* [Qwen3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(4B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Network accessline 3279
* [Gemma 3 (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 3280
* [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-GRPO.ipynb) - Network accessline 3281
* [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4_\(14B\)-GRPO.ipynb) - Network accessline 3282
* [Qwen 2.5 (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 3286
* [Sesame-CSM (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Sesame_CSM_\(1B\)-TTS.ipynb) - Network accessline 3287
* [Orpheus-TTS (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Orpheus_\(3B\)-TTS.ipynb) - Network accessline 3288
* [Whisper Large V3](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Whisper.ipynb) – Speech-to-Text - Network accessline 3289
* [Llasa-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llasa_TTS_\(1B\).ipynb) - Network accessline 3290
* [Spark-TTS (0.5B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Spark_TTS_\(0_5B\).ipynb) - Network accessline 3291
* [Oute-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Oute_TTS_\(1B\).ipynb) - Network accessline 3295
* [Llama 3.2 Vision (11B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(11B\)-Vision.ipynb) - Network accessline 3296
* [Qwen 2.5-VL (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_VL_\(7B\)-Vision.ipynb) - Network accessline 3297
* [Pixtral (12B) 2409](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Pixtral_\(12B\)-Vision.ipynb) - Network accessline 3301
* [Tool Calling](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb\&accelerator=nvidiaTeslaT4) - Network accessline 3302
* [ORPO](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-ORPO.ipynb) - Network accessline 3303
* [Continued Pretraining](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_v0.3_\(7B\)-CPT.ipynb) - Network accessline 3304
* [DPO Zephyr](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Zephyr_\(7B\)-DPO.ipynb) - Network accessline 3305
* [Inference only](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Inference.ipynb) - Network accessline 3306
* [Ollama](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-Ollama.ipynb) - Network accessline 3307
* [Text Completion](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_\(7B\)-Text_Completion.ipynb) - Network accessline 3308
* [CodeForces-cot (Reasoning)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb) - Network accessline 3309
* [Unsloth Studio (chat UI)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Unsloth_Studio.ipynb) - Network accessline 3313
* [Gemma 2 (9B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(9B\)-Alpaca.ipynb) - Network accessline 3314
* [Gemma 2 (2B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(2B\)-Alpaca.ipynb) - Network accessline 3315
* [CodeGemma (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_\(7B\)-Conversational.ipynb) - Network accessline 3316
* [Mistral NeMo (12B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Nemo_\(12B\)-Alpaca.ipynb) - Network accessline 3317
* [Mistral Small (22B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Small_\(22B\)-Alpaca.ipynb) - Network accessline 3318
* [TinyLlama (1.1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-TinyLlama_\(1.1B\)-Alpaca.ipynb) - Network accessline 3320
To view a complete list of all our Kaggle notebooks, [click here](https://github.com/unslothai/notebooks#-kaggle-notebooks). - Network accessline 3322
{% hint style="info" %} Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)! - Network accessline 3323
Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)! - Network accessline 3334
{% hint style="warning" %} Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install). - Network accessline 3335
Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install). - Network accessline 3340
If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below: - Network accessline 3358
mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Network accessline 3359
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Dangerous shell operationline 3361
rm -rf ~/miniconda3/miniconda.sh - Network accessline 3392
You can also run your fine-tuned models by using [Unsloth's 2x faster inference](https://docs.unsloth.ai/basics/running-and-saving-models/unsloth-inference). - Network accessline 3404
Unsloth now supports vision/multimodal RL with [Qwen3-VL](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune), [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune) and more. Due to Unsloth's unique [weigh - Network accessline 3406
Unsloth can train Qwen3-VL-8B with GSPO/GRPO on a free Colab T4 GPU. Other VLMs work too, but may need larger GPUs. Gemma requires newer GPUs than T4 because vLLM [restricts to Bfloat16](https://docs.unsloth.ai/models/gemma-3-how-to-run-and - Network accessline 3408
* **Qwen-3 VL-8B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Network accessline 3409
* **Qwen-2.5 VL-7B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) •[ Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/note - Network accessline 3410
* **Gemma-3-4B** (Unsloth inference): [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Network accessline 3412
We have also added vLLM VLM integration into Unsloth natively, so all you have to do to use vLLM inference is enable the `fast_inference=True` flag when initializing the model. Special thanks to [Sinoué GAD](https://github.com/unslothai/uns - Network accessline 3414
This VLM support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), which un - Access to secretsline 3427
[Credential-related source omitted] - Network accessline 3454
<pre class="language-bash"><code class="lang-bash">pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 3455
<strong>pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 3460
'2025.1.5' is one of the previous old versions of Unsloth. Change it to a specific release listed on our [Github here](https://github.com/unslothai/unsloth/releases). - Access to secretsline 3510
[Credential-related source omitted] - Network accessline 3536
pip install ninja export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3537
export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive cd xformers && python setup.py install && cd .. - Network accessline 3538
git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3541
uv pip install unsloth bash curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env - Network accessline 3542
bash curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env bash - Dangerous shell operationline 3543
curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env - Network accessline 3543
curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env - Network accessline 3552
bash uv pip install -qqq \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 3553
uv pip install -qqq \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ "unsloth[base] @ git+https://github.com/unslothai/unsloth" - Network accessline 3554
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 3555
"unsloth[base] @ git+https://github.com/unslothai/unsloth" - Network accessline 3561
pip install ninja export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3562
export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive cd xformers && python setup.py install && cd .. - Network accessline 3563
git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3566
uv pip install -U transformers bash curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" - Network accessline 3567
bash curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" bash - Network accessline 3568
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" - Network accessline 3574
conda activate unsloth-blackwell bash pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128 - Network accessline 3575
bash pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128 bash - Network accessline 3576
pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128 - Network accessline 3584
pip install ninja export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3585
export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive cd xformers && python setup.py install && cd .. - Network accessline 3586
git clone --depth=1 https://github.com/facebookresearch/xformers --recursive - Network accessline 3608
# Install xformers from source with optimized build flags pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers - Network accessline 3609
pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers - Network accessline 3622
[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate image needed. - Network accessline 3624
For installation instructions, please follow our [Unsloth Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker). - Network accessline 3664
Google’s Gemma 3n multimodal model handles image, audio, video, and text inputs. Available in 2B and 4B sizes, it supports 140 languages for text and multimodal tasks. You can now run and fine-tune **Gemma-3n-E4B** and **E2B** locally using - Network accessline 3666
> **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - Network accessline 3674
<table><thead><tr><th width="249">Dynamic 2.0 GGUF (text only)</th><th width="285">Dynamic 4-bit Instruct (to fine-tune)</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/gemma-3n-E2B-it - Network accessline 3676
**See all our Gemma 3n uploads including base and more formats in** [**our collection here**](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339)**.** - Network accessline 3720
{% hint style="info" %} We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp! - Network accessline 3721
We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp! - Network accessline 3724
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Dangerous shell operationline 3745
ollama rm hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL - Network accessline 3752
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 3754
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 3754
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 3781
* It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no - Network accessline 3782
* <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark> * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/ - Network accessline 3783
* Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) - Network accessline 3784
* Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) - Network accessline 3785
* Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 3786
* Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) - Network accessline 3787
* Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 3788
* **More notebooks in our** [**notebooks repo**](https://github.com/unslothai/notebooks)**.** - Network accessline 3806
RUN git clone --depth=1 https://github.com/facebookresearch/xformers --recursive && \ - Network accessline 3824
!uv pip install -qqq \ "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 3825
"torch>=2.8.0" "triton>=3.4.0" {install_numpy} \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ "unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 3826
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 3827
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 3828
torchvision bitsandbytes \ git+https://github.com/huggingface/transformers \ git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 3829
git+https://github.com/huggingface/transformers \ - Network accessline 3830
git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 3835
We’ll load **`gpt-oss-20b`** using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work for QLoRA fine-tuning). Configur - Network accessline 3862
* [GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-rl-notebooks) - Network accessline 3863
* [ORPO notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb) - Network accessline 3864
* [DPO Zephyr notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 3865
* [KTO notebook](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) - Network accessline 3866
* [SimPO notebook](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing) - Network accessline 3868
We're also in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#acceler - Access to secretsline 3873
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Finetuned from [**Mistral-Small-3.1**](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF), Devstral supports a 128k context window. Devstral Small 1.1 has improved performance, achieving a score of 53.6% performance on - Network accessline 3917
All Devstral uploads use our Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology, delivering the best performance on 5-shot MMLU and KL Divergence benchmarks. This means, you can run and fine-tune qua - Network accessline 3921
| Devstral 2507 (new) | Devstral 2505 | | ----- - Network accessline 3922
| ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | GGUF: - Network accessline 3923
| GGUF: [Devstral-Small-2507-GGUF](https://huggingface.co/unsloth/Devstral-Small-2507-GGUF) | [Devstral-Small-2505-GGUF](https://huggingface.co/unsloth/Devstral-Small-2505-GGUF) | - Network accessline 3924
| 4-bit BnB: [Devstral-Small-2507-unsloth-bnb-4bit](https://huggingface.co/unsloth/Devstral-Small-2507-unsloth-bnb-4bit) | [Devstral-Small-2505-unsloth-bnb-4bit](https://huggingface.co/unsloth/Devstral-Small-2505-unsloth-bnb-4bit) | - Network accessline 3936
**A system prompt is recommended**, and is a derivative of Open Hand's system prompt. The full system prompt is provided [here](https://huggingface.co/unsloth/Devstral-Small-2505/blob/main/SYSTEM_PROMPT.txt). - Network accessline 3946
2. Run the model with our dynamic quant. Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Devstral supports 128K context - Network accessline 3947
3. Also Devstral supports 128K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves - Network accessline 3951
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 3973
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 3975
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 3975
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 3986
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 3987
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 3988
git clone https://github.com/ggerganov/llama.cpp - Network accessline 4001
RUN git clone https://github.com/triton-lang/triton.git && \ - Network accessline 4084
**Not necessarily.** Many assume RAG will consistently outperform a fine-tuned model, but that’s not the case when fine-tuning is done properly. In fact, a well-tuned model often matches or even surpasses RAG-based systems on specialized ta - Network accessline 4090
**Not at all!** While full fine-tuning or pretraining can be costly, these are not necessary (pretraining is especially not necessary). In most cases, LoRA or QLoRA fine-tuning can be done for minimal cost. In fact, with Unsloth’s [free not - Network accessline 4107
We recommend starting with **QLoRA**, as it’s one of the most efficient and accessible methods available. Thanks to Unsloth’s [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss compared to standard 16-bit LoRA f - Network accessline 4113
{% content-ref url="../fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 4114
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Access to secretsline 4127
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* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 4193
Read our full DeepSeek-R1 blogpost here: [unsloth.ai/blog/deepseekr1-dynamic](https://unsloth.ai/blog/deepseekr1-dynamic) - Network accessline 4209
{% tabs %} {% tab title="IQ1\_S code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FqpBdpW55h5mNAzVoTxPI%2Finference_UD-IQ1_S_3407.txt?alt=media&toke - Network accessline 4211
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{% tabs %} {% tab title="IQ1\_S basic code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FFtAMzAucSfKMkkmXItTj%2Finference_basic-IQ1_S_3407.txt?alt=m - Network accessline 4239
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{% tab title="IQ2\_XXS basic code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FajsVHsVqlWpwHk7mY32t%2Finference_basic-IQ2_XXS_3407.txt?alt=media&to - Network accessline 4253
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If you're still encountering any issues with versions or dependencies, please use our [Docker image](https://docs.unsloth.ai/get-started/install-and-update/docker) which will have everything pre-installed. - Network accessline 4288
* It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no - Network accessline 4289
* <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark> * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/ - Network accessline 4290
* Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) - Network accessline 4291
* Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) - Network accessline 4292
* Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 4293
* Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) - Network accessline 4294
* Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 4295
* **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 4320
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 4321
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 4322
git clone https://github.com/ggerganov/llama.cpp - Network accessline 4355
DeepSeek-R1-0528 is DeepSeek's new update to their R1 reasoning model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic **1.66-bit** version uses 162GB (-80% reduction in size). GGUF: [DeepSeek-R1-0528-GGUF] - Network accessline 4357
DeepSeek also released a R1-0528 distilled version by fine-tuning Qwen3 (8B). The distill achieves similar performance to Qwen3 (235B). ***You can also*** [***fine-tune Qwen3 Distill***](#fine-tuning-deepseek-r1-0528-with-unsloth) ***with U - Network accessline 4359
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss. - Network accessline 4366
NEW: Huge improvements to tool calling and chat template fixes.\ \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM - Network accessline 4367
\ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/ - Network accessline 4368
New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/De - Network accessline 4387
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528), these are the recommended settings for R1 (R1-0528 and Qwen3 distill should use the same settings) inference: - Network accessline 4404
* Qwen3 (8B) distill: [DeepSeek-R1-0528-Qwen3-8B-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF) - Network accessline 4407
We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple device - Network accessline 4409
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSe - Network accessline 4411
We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-R1-0528). - Network accessline 4425
Open WebUI has made an step-by-step tutorial on how to run R1 here and for R1-0528, you will just need to replace R1 with the new 0528 quant: [docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/](https://docs.openwebui.com/tutoria - Network accessline 4433
1. <mark style="background-color:yellow;">**To run the full 720GB R1-0528 model,**</mark> [<mark style="background-color:yellow;">**see here**</mark>](#run-full-r1-0528-on-llama.cpp)<mark style="background-color:yellow;">**.**</mark> Obtain - Network accessline 4439
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 4455
3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . We <mark style="background-color:green;">**recommend using - Network accessline 4473
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 4475
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4475
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4499
GLM-4.6 is the latest reasoning model from **Z.ai**, achieving SOTA performance on coding and agent benchmarks while offering improved conversational chats. The full 355B parameter model requires **400GB** of disk space, while the Unsloth D - Network accessline 4506
You asked for benchmarks on our quants, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing many full-precision SOTA LLMs. [Read more.](https://docs.unsloth.ai/new/unsloth-dynamic-gg - Network accessline 4509
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and Aider performance, meaning you can run & fine-tune quantized GLM LLMs with minimal accuracy loss. - Network accessline 4519
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{% stepper %} {% step %} Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp). - Network accessline 4553
Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp). - Network accessline 4566
{% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus - Network accessline 4568
Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 4592
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 4594
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4594
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4632
Unsloth Docker image: [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) - Network accessline 4643
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\ - Network accessline 4644
Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation): - Dangerous shell operationline 4647
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 4654
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[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate one needed. - Network accessline 4661
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 4668
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If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 4679
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One of the key parts of creating a dataset is your [chat template](https://docs.unsloth.ai/basics/chat-templates) and how you are going to design it. Tokenization is also important as it breaks text into tokens, which can be words, sub-word - Network accessline 4759
* Chat-based dialogues (Q\&A, learn a new language, customer support, conversations). * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification. - Network accessline 4760
* Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb), summarization, generation tasks). - Network accessline 4789
For [**continued pretraining**](https://docs.unsloth.ai/basics/continued-pretraining), we use raw text format without specific structure: - Network accessline 4793
If we are adapting a model to a new task, and intend for the model to output text in a single turn based on a specific set of instructions, we can use **Instruction** format in [Alpaca style](https://docs.unsloth.ai/basics/tutorial-how-to-f - Network accessline 4828
<mark style="color:green;">**A:**</mark> If your dataset is already formatted in the Alpaca format, then follow the formatting steps as shown in the Llama3.1 [notebook ](https://colab.research.google.com/github/unslothai/notebooks/blob/mai - Network accessline 4836
<mark style="color:green;">**A:**</mark> The `chat_template` attribute when a model is first uploaded by the original model owners sometimes contains errors and may take time to be updated. In contrast, at Unsloth, we thoroughly check and - Network accessline 4840
<mark style="color:green;">**A:**</mark> Submit a feature request on the unsloth github issues [forum](https://github.com/unslothai/unsloth). As a temporary workaround, you could also use the tokenizer's own apply\_chat\_template function - Network accessline 4846
* Produce entirely new data - either from scratch or from your existing dataset * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) - Network accessline 4847
* Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) and become too specific - Network accessline 4852
We collaborated with Meta to launch a free notebook for creating Synthetic Datasets automatically using local models like Llama 3.2. [Access the notebook here.](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_ - Network accessline 4889
If you want to fine-tune a model that already has reasoning capabilities like the distilled versions of DeepSeek-R1 (e.g. DeepSeek-R1-Distill-Llama-8B), you will need to still follow question/task and answer pairs however, for your answer y - Network accessline 4891
For a model that does not have reasoning and you want to train it so that it later encompasses reasoning capabilities, you will need to utilize a standard dataset but this time without reasoning in its answers. This is training process is k - Network accessline 4897
* Standardize the format of all datasets, combine them into a single dataset, and fine-tune on this unified dataset. * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebo - Network accessline 4898
* Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebook to fine-tune on multiple datasets directly. - Network accessline 4908
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You can access the GPT4 version of the Alpaca dataset [here](https://huggingface.co/datasets/vicgalle/alpaca-gpt4.). Below shows some examples of the dataset: - Network accessline 4914
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The dataset for fine-tuning a vision or multimodal model also includes image inputs. For example, the [Llama 3.2 Vision Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scroll - Network accessline 4990
We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and - Network accessline 4994
| Image - Network accessline 4996
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For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX). - Network accessline 5082
* **Operating System**: Works on Linux and Windows. * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.u - Network accessline 5083
* Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-sp - Network accessline 5084
Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20 & 50, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow. - Network accessline 5085
* The official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker Hub. - Network accessline 5086
* Unsloth works on [AMD](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) and [Intel](https://github.com/unslothai/unsloth/pull/2621) GPUs! Apple/Silicon/MLX is in the works. - Network accessline 5101
**For context length benchmarks, see** [**here**](https://docs.unsloth.ai/basics/unsloth-benchmarks#context-length-benchmarks)**.** - Network accessline 5134
<table><thead><tr><th width="212.9000244140625">Argument</th><th>Example and use-case</th></tr></thead><tbody><tr><td><strong><code>--gpu-memory-utilization</code></strong></td><td>Default 0.9. How much VRAM usage vLLM can use. Reduce if go - Network accessline 5181
curl -X POST http://localhost:8000/v1/load_lora_adapter \ - Network accessline 5204
Qwen released QwQ-32B - a reasoning model with performance comparable to DeepSeek-R1 on many [benchmarks](https://qwenlm.github.io/blog/qwq-32b/). However, people have been experiencing **infinite generations**, **many repetitions**, \<thin - Network accessline 5212
| [GGUF](https://huggingface.co/unsloth/QwQ-32B-GGUF) | [Dynamic 4-bit](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit) | [BnB 4-bit](https://huggingface.co/unsloth/QwQ-32B-bnb-4bit) | [16-bit](https://huggingface.co/unsloth/QwQ-32 - Network accessline 5217
According to [Qwen](https://huggingface.co/Qwen/QwQ-32B), these are the recommended settings for inference: - Network accessline 5244
Courtesy to [@krist486](https://x.com/krist486/status/1897885598196654180) for bringing llama.cpp sampling directions to my attention. - Network accessline 5248
We investigated usage of `dry penalty` as suggested in <https://github.com/ggml-org/llama.cpp/blob/master/examples/main/README.md> using a value of 0.8, but we actually found this to **rather cause syntax issues especially for coding**. If - Network accessline 5260
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 5262
2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/QwQ-32B-GGUF> - Network accessline 5289
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 5291
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5291
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5310
Qwen3-VL has vision, video and OCR capabilities as well as 256K context (can be extended to 1M).\ \ [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcem - Network accessline 5312
[Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl). Train Qwen3-VL (8B) for free with our [notebooks](#fine-tuning-qwen3-vl). - Network accessline 5322
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF">2B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF">2B-Thinking</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL - Network accessline 5340
Qwen3-VL also used the below settings for their benchmarking numbers, as mentioned [on GitHub](https://github.com/QwenLM/Qwen3-VL/tree/main?tab=readme-ov-file#generation-hyperparameters). - Network accessline 5359
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1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 5373
2. **Let's first get an image!** You can also upload images as well. We shall use <https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png>, which is just our mini logo showing how finetun - Network accessline 5375
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4. Let's get the 2nd image at <https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg> - Network accessline 5383
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apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5455
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5456
git clone https://github.com/ggml-org/llama.cpp - Network accessline 5532
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We uploaded dynamic 4-bit quants to: <https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit> - Network accessline 5536
Since vLLM 0.7.3 (2025 February 20th) <https://github.com/vllm-project/vllm/releases/tag/v0.7.3>, vLLM now supports loading Unsloth dynamic 4bit quants! - Network accessline 5538
All our GGUFs are at <https://huggingface.co/unsloth/QwQ-32B-GGUF>! - Network accessline 5548
According to the Min\_p paper <https://arxiv.org/pdf/2407.01082>, for more creative and diverse outputs, and if you still see repetitions, try disabling top\_p and top\_k! - Network accessline 5607
{% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %} - Network accessline 5611
{% embed url="<https://www.youtube.com/watch?t=3289s&v=bbFEYPx9Hpo>" %} - Network accessline 5615
{% embed url="<https://www.youtube.com/watch?v=juOh1afy-IE>" %} - Network accessline 5617
{% embed url="<https://www.youtube.com/watch?v=oF0_eMhzRaQ>" %} - Network accessline 5650
You can then run the merged QAT lower precision model in vLLM, Unsloth and other systems for inference! These are all in the [Qwen3-4B QAT Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\ - Access to secretsline 5696
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<table data-full-width="true"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Accuracy</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.58bit</td><td>UD-IQ1_S</td><td><strong>131GB</strong></td><td>Fair</td><td> - Network accessline 5843
5. To test our Flappy Bird example as mentioned in our blog post here: <https://unsloth.ai/blog/deepseekr1-dynamic>, we can produce the 2nd example like below using our 1.58bit dynamic quant: - Network accessline 5845
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<table><thead><tr><th width="249">Dynamic GGUFs</th><th>Dynamic 4-bit + FP8</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/granite-4.0-h-350m-GGUF">H-350M</a></li><li><a href="https:/ - Network accessline 5876
You can also view our [Granite-4.0 collection](https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d) for all uploads including Dynamic Float8 quants etc. - Network accessline 5908
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 5925
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 5927
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5927
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5937
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5938
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5939
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 5965
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* **LoRA:** 16-bit fine-tuning. It's slightly faster and slightly more accurate, but consumes significantly more VRAM (4× more than QLoRA). Recommended for 16-bit environments and scenarios where maximum accuracy is required. * **QLoRA:** 4 - Network accessline 6103
:sloth: *70B LLaMA fits in <48GB VRAM with QLoRA in Unsloth -* [*more details here*](https://unsloth.ai/blog/llama3-3)*.* - Network accessline 6147
[Read our blog post](https://unsloth.ai/blog/gradient) for more details. - Network accessline 6151
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A regularization technique that helps [prevent overfitting](#overfitting-poor-generalization-too-specialized) by randomly setting a fraction of the LoRA activations to zero during each training step. [Recent research suggests](https://arxiv - Network accessline 6174
Options are `True`, `False`, and `"unsloth"`. \ 🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*]( - Network accessline 6175
🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](https://unsloth.ai/blog/long-context)*.*\\ - Network accessline 6179
An advanced feature that implements [**Rank-Stabilized LoRA**](https://arxiv.org/abs/2312.03732). If set to `True`, the effective scaling becomes `lora_alpha / sqrt(r)` instead of the standard `lora_alpha / r`. This can sometimes improve st - Network accessline 6181
An advanced technique, as proposed in [**LoftQ**](https://arxiv.org/abs/2310.08659), initializes LoRA matrices with the top 'r' singular vectors from the pretrained weights. This can improve accuracy but may cause a significant memory spike - Network accessline 6185
When validating that **LoRA** adapter weights have been updated after fine-tuning, avoid using **np.allclose()** for comparison. This method can miss subtle but meaningful changes, particularly in **LoRA A**, which is initialized with small - Network accessline 6206
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According to the [rsLoRA (rank stabilized lora) paper](https://arxiv.org/abs/2312.03732), we should instead scale alpha by the sqrt of the rank. Other options exist, but theoretically this is the optimum. The left plot shows other ranks and - Network accessline 6233
According to empirical experiments and research papers like the original [QLoRA paper](https://arxiv.org/pdf/2305.14314), it's best to apply LoRA to both attention and MLP layers. - Network accessline 6238
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The [QLoRA paper](https://arxiv.org/pdf/2305.14314) shows that masking out inputs and **training only on completions** (outputs or assistant messages) can further **increase accuracy** by a few percentage points (*1%*). Below demonstrates h - Network accessline 6278
The QLoRA paper states that **training on completions only** increases accuracy by quite a bit, especially for multi-turn conversational finetunes! We do this in our [conversational notebooks here](https://colab.research.google.com/github/u - Network accessline 6280
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***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 6374
{% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 6375
For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 6383
This article covers everything (from beginner to advanced) you need to know about GRPO, Reinforcement Learning (RL) and reward functions, along with tips, and the basics of using GRPO with [Unsloth](https://github.com/unslothai/unsloth). If - Network accessline 6404
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{% column %} OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**< - Network accessline 6434
OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**</mark> to pro - Network accessline 6443
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{% column %} In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agen - Network accessline 6451
In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agent**</mark> is - Network accessline 6459
The formula for PPO looks quite complicated because it was designed to be stable. Visit our [AI Engineer talk](https://docs.unsloth.ai/ai-engineers-2025) we gave in 2025 about RL for more in depth maths derivations about PPO. - Network accessline 6466
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{% column %} DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are: - Network accessline 6470
DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are: - Network accessline 6482
2. Code output can be verified as having executed correctly or not. 3. Designing verifiable reward functions can be tough, and so most examples are math or code. 4. Use-cases for GRPO isn’t just for code or math—its reasoning process can en - Network accessline 6484
4. Use-cases for GRPO isn’t just for code or math—its reasoning process can enhance tasks like email automation, database retrieval, law, and medicine, greatly improving accuracy based on your dataset and reward function - the trick is to d - Network accessline 6494
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* With 15GB VRAM, Unsloth allows you to transform any model up to 17B parameters like Llama 3.1 (8B), Phi-4 (14B), Mistral (7B) or Qwen2.5 (7B) into a reasoning model * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.un - Network accessline 6545
* **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!** - Network accessline 6548
{% content-ref url="reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo" %} [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train - Network accessline 6549
[tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo) - Network accessline 6552
| [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **GSPO -** new | [**Qwen3-VL-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_ - Network accessline 6553
| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- - Network accessline 6554
| [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/mai - Network accessline 6555
| [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) | [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\) - Network accessline 6556
| [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) | [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\ - Network accessline 6558
{% hint style="success" %} **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in - Network accessline 6559
**NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in GRPOConfig to enable: - Network accessline 6591
{% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, - Network accessline 6592
[**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc. - Network accessline 6594
We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t - Network accessline 6597
> [**Fine-tune**](#fine-tuning-gpt-oss-with-unsloth) **gpt-oss-20b for free with our** [**Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) - Network accessline 6599
Trained with [RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), **gpt-oss-120b** rivals o4-mini and **gpt-oss-20b** rivals o3-mini. Both excel at function calling and CoT reasoning, surpassing o1 and GPT-4o. - Network accessline 6607
* 20B: [gpt-oss-**20B**](https://huggingface.co/unsloth/gpt-oss-20b-GGUF) - Network accessline 6608
* 120B: [gpt-oss-**120B**](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) - Network accessline 6612
OpenAI released a standalone parsing and tokenization library called [Harmony](https://github.com/openai/harmony) which allows one to tokenize conversations to OpenAI's preferred format for gpt-oss. The official OpenAI [cookbook article](ht - Network accessline 6616
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We made a [MXFP4 inference notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) as well in Tesla T4 Colab! - Network accessline 6645
{% hint style="info" %} [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, - Network accessline 6646
[Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, microscaled lhs/rhs are - Network accessline 6683
[Credential-related source omitted] - Network accessline 6685
To achieve inference speeds of 6+ tokens per second for our Dynamic 4-bit quant, have at least **14GB of unified memory** (combined VRAM and RAM) or **14GB of system RAM** alone. As a rule of thumb, your available memory should match or exc - Network accessline 6695
> **You can run gpt-oss-20b for free with our** [**Google Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) - Network accessline 6703
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6823
**Magistral-Small-2509** is a reasoning LLM developed by Mistral AI. It excels at coding and mathematics and supports multiple languages. Magistral supports a 128k token context window and was finetuned from [**Mistral-Small-3.2**](https:/ - Network accessline 6835
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Mistral LLMs with minimal accuracy loss. - Network accessline 6839
<table><thead><tr><th width="255.64999389648438">Dynamic 2.0 GGUF (to run)</th><th width="305.25">Dynamic 4-bit (to finetune/deploy)</th><th>Dynamic Float8</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/Magi - Network accessline 6880
2. Run the model with our dynamic quant. We did not set the context length automatically, so it will just use Ollama's default set context length.\ Note you can call `ollama serve &`in another terminal if it fails! We include all suggest - Network accessline 6881
Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Magistral supports 40K context lengths, so best to enable [**KV cache q - Network accessline 6882
3. Also Magistral supports 40K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves - Network accessline 6883
4. **Ollama also sets the default context length to 4096**, as [mentioned here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size). Use `OLLAMA_CONTEXT_LENGTH=8192` to change it to 8192. Magist - Network accessline 6887
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 6942
## From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html - Network accessline 6944
**URL:** llms-txt#from-https://mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html - Network accessline 6981
bash apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6982
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp - Network accessline 6983
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6984
git clone https://github.com/ggerganov/llama.cpp - Network accessline 6993
bash apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6994
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp - Network accessline 6995
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6996
git clone https://github.com/ggerganov/llama.cpp - Network accessline 7024
Or follow the steps at <https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model> using the model name "merged\_model" to merge to GGUF. - Network accessline 7034
* It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no - Network accessline 7035
* <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark> * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/ - Network accessline 7036
* Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) - Network accessline 7037
* Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) - Network accessline 7038
* Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 7039
* Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) - Network accessline 7040
* Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 7041
* **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 7074
Microsoft's new Phi-4 reasoning models are now supported in Unsloth. The 'plus' variant performs on par with OpenAI's o1-mini, o3-mini and Sonnet 3.7. The 'plus' and standard reasoning models are 14B parameters while the 'mini' has 4B param - Network accessline 7076
All Phi-4 reasoning uploads use our [Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology. - Network accessline 7080
| Dynamic 2.0 GGUF (to run) - Network accessline 7082
| <ul><li><a href="https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF/">Reasoning-plus</a> (14B)</li></ul><ul><li><a href="https://huggingface.co/unsloth/Phi-4-reasoning-GGUF">Reasoning</a> (14B)</li></ul><ul><li><a href="https://hugg - Network accessline 7121
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7139
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7141
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7141
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7161
Fine-tuning vision models enables model to excel at certain tasks normal LLMs won't be as good as such as object/movement detection. **You can also train** [**VLMs with RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) - Network accessline 7163
* **NEW: Qwen3-VL (8B) Vision:** [**Notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) - Network accessline 7164
* **Gemma 3 (4B) Vision:** [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - Network accessline 7165
* **Llama 3.2 Vision** fine-tuning for radiography: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb)\ - Network accessline 7166
How can we assist medical professionals in analyzing Xrays, CT Scans & ultrasounds faster. * **Qwen2.5 VL** fine-tuning for converting handwriting to LaTeX: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/n - Network accessline 7167
* **Qwen2.5 VL** fine-tuning for converting handwriting to LaTeX: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb)\ - Network accessline 7168
This allows complex math formulas to be easily transcribed as LaTeX without manually writing it. * **Pixtral 12B 2409** vision fine-tuning for general Q\&A: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/n - Network accessline 7169
* **Pixtral 12B 2409** vision fine-tuning for general Q\&A: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb)\ - Network accessline 7180
The dataset for fine-tuning a vision or multimodal model is similar to standard question & answer pair [datasets ](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide), but this time, they also includes image inputs. F - Network accessline 7182
We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and - Network accessline 7186
| Image - Network accessline 7188
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For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX). - Network accessline 7307
[Reward Functions/Verifiers](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#reward-functions-verifier) lets us know if the model is doing well or not according to the dataset you have provided. Each generation run wi - Network accessline 7309
You can input your generations into an LLM like ChatGPT 4o or Llama 3.1 (8B) and design a reward function and verifier to evaluate it. For example, feed your generations into a LLM of your choice and set a rule: "If the answer sounds too ro - Network accessline 7322
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We have pre-selected hyperparameters for the most optimal results however you could change them. Read all about [parameters here](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide). For **advanced GRPO** - Network accessline 7331
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LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs. - Network accessline 7366
RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo - Network accessline 7368
| [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo - Network accessline 7392
Load the 20B model in 4‑bit QLoRA for memory efficiency, then wrap it with a LoRA adapter. You can also train it in 16-bit LoRA but it will use 4x more memory. For more settings view our [configuration guide](https://docs.unsloth.ai/get-sta - Network accessline 7601
!uv pip install -qqq \ "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 7602
"torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \ "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ "unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 7603
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 7604
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 7605
git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 7674
DeepSeek’s V3.1 and **Terminus** update introduces hybrid reasoning inference, combining 'think' and 'non-think' into one model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic 2-bit version uses 245GB (-75 - Network accessline 7676
{% hint style="success" %} **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\ - Network accessline 7677
**NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\ - Network accessline 7679
[**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing - Network accessline 7684
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss. - Network accessline 7708
1. DeepSeek V3.1 is a hybrid reasoning model, meaning you can change the chat template to enable reasoning. The chat template introduced `thinking = True` , but other models use `enable_thinking = True` . We added the option to use `enable_ - Network accessline 7709
2. llama.cpp's jinja renderer via [minja](https://github.com/google/minja) does not allow the use of extra arguments in the `.split()` command, so using `.split(text, 1)` works in Python, but not in minja. We had to change this to make llam - Network accessline 7716
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3.1), these are the recommended settings for V3.1 inference: - Network accessline 7774
{% step %} Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant. - Network accessline 7775
Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant. - Network accessline 7781
{% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus - Network accessline 7783
Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 7812
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7814
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7814
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7837
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" - Access to secretsline 7935
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year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} - Network accessline 7964
publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} } - Network accessline 7965
howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} - Network accessline 7971
year = {2025}, publisher = {Github}, howpublished = {\url{https://github.com/unslothai/unsloth}} - Network accessline 7972
publisher = {Github}, howpublished = {\url{https://github.com/unslothai/unsloth}} } - Network accessline 7973
howpublished = {\url{https://github.com/unslothai/unsloth}} - Network accessline 8028
* For more detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).  - Network accessline 8029
* Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl). - Network accessline 8031
Tested on H100 and [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) GPUs. We tested using the Alpaca Dataset, a batch size of 2, gradient accumulation steps of 4, rank = 32, and applied Q - Network accessline 8037
{% hint style="info" %} The more data you have, the less VRAM Unsloth uses due to our [gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm + Apple's CCE algorithm! - Network accessline 8038
The more data you have, the less VRAM Unsloth uses due to our [gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm + Apple's CCE algorithm! - Network accessline 8077
As shown at [OpenAI DevDay](https://x.com/UnslothAI/status/1976284209842118714), gpt-oss-20b was trained with RL and Unsloth on DGX Spark to auto-win 2048. You can train using Unsloth in a Docker container or virtual environment on DGX Spar - Network accessline 8079
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First, build the Docker image using the DGX Spark Dockerfile which can be [found here](https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark). You can also run the below in a Terminal in the DGX Spark: - Network accessline 8098
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sudo apt update && sudo apt install -y wget - Network accessline 8109
sudo apt update && sudo apt install -y wget wget -O Dockerfile "https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark" - Network accessline 8110
wget -O Dockerfile "https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark" - Network accessline 8135
You can fine-tune DeepSeek-OCR to enhance its vision or language performance. In our Unsloth [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb), we demonstrat - Network accessline 8139
> **Our model upload that enables fine-tuning + more inference support:** [**DeepSeek-OCR**](https://huggingface.co/unsloth/DeepSeek-OCR) - Network accessline 8184
{% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF - Network accessline 8185
[**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc. - Network accessline 8187
We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t - Network accessline 8190
> **Quickstart:** Fine-tune gpt-oss-20b for free with our: [Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) - Network accessline 8201
This section covers fine-tuning gpt-oss using our Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). You can also save and use the gpt-oss notebook into your favorite code editor and follow our [local gpt-oss g - Network accessline 8210
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We’ll load **`gpt-oss-20b`** using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work).  - Network accessline 8226
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Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 8237
{% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high. - Network accessline 8238
To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.  - Network accessline 8243
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For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated - Network accessline 8257
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One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` , - Network accessline 8269
{% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 8270
🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 8273
Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide). - Network accessline 8276
We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet - Network accessline 8280
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1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 8322
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{% hint style="info" %} We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use. - Network accessline 8331
We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use. - Network accessline 8339
Ensure your device is [Unsloth compatible](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and you can read our detailed [installation guide](https://docs.unsloth.ai/get-started/install-and-update). - Network accessline 8364
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One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` , - Network accessline 8368
{% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 8369
🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix. - Network accessline 8372
Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide). - Network accessline 8379
We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet - Network accessline 8383
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This can zero out all `completion_mask` entries when many completions are truncated, making `n_mask_per_reward = 0` and causing KL to become NaN. [See](https://github.com/unslothai/unsloth-zoo/blob/e705f7cb50aa3470a0b6e36052c61b7486a39133/u - Network accessline 8632
In our GitHub, we have a list of every chat template Unsloth uses including for Llama, Mistral, Phi-4 etc. So if you need any pointers on the formatting or use case, you can view them here: [github.com/unslothai/unsloth/blob/main/unsloth/ch - Network accessline 8636
* [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 8637
* [ChatML](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) - Network accessline 8638
* [Ollama](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing) - Network accessline 8639
* [Text Classification](https://github.com/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) by Timotheeee - Network accessline 8640
* [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail - Network accessline 8646
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Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin - Network accessline 8682
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We’re excited to share that Unsloth Dynamic GGUFs shows how it's possible to quantize LLMs like [DeepSeek-V3.1](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) (671B) down to just **1-bit** or **3-bit**, and still be able t - Network accessline 8764
Previously, [we demonstrated](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) how Unsloth Dynamic GGUFs outperform other quantization methods on 5-shot MMLU and KL Divergence. Now, we’re showcasing their performance on independent - Network accessline 8766
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**Why the** [**Aider Polyglot**](https://aider.chat/docs/leaderboards/) **benchmark?** Aider is one of the most comprehensive measures of how well LLMs can write, code, follow instructions, and apply changes without human intervention, maki - Network accessline 8776
{% hint style="success" %} The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalL - Network accessline 8777
The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3 - Network accessline 8786
In Nov 2024, our [4-bit Dynamic](https://unsloth.ai/blog/dynamic-4bit) Quants showcased how you could largely restore QLoRA fine-tuning & model accuracy by just <mark style="background-color:green;">**selectively quantizing layers**</mark>. - Network accessline 8788
Our Dynamic GGUFs are even more effective when paired with our [imatrix calibration dataset](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs#whats-new-in-dynamic-v2.0), designed for chat and coding performance. All of this enabled - Network accessline 8795
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We also showed dynamic benchmarks in <https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs> for Gemma 3 and Llama 4 Scout, showing how effective our methodology is: - Network accessline 8809
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Benchmark experiments were mainly conducted by [David Sluys](https://www.linkedin.com/in/david-sluys-231348208/) (neolithic5452 on [Aider Discord](https://discord.com/channels/1131200896827654144/1408293692074360914)), a trusted community c - Network accessline 8863
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See [here](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?chat_template=default\&format=true) for our fixed chat template or [here](https://huggingface.co/unsloth/DeepSeek-V3.1/raw/main/chat_template.jinja) for a raw jinja file. - Network accessline 8902
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Head over to our [DeepSeek V3.1 guide](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-how-to-run-locally/deepseek-r1-dynamic-1.58-bit) or to quickly get the dynamic 2bit version, do: - Network accessline 8925
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8926
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8927
git clone https://github.com/ggml-org/llama.cpp - Possible obfuscationline 9050
* <U+200B>**DeepSeek-R1-Distill-Llama-8B** is a fine-tuned version of Llama-3.1-8B. DeepSeek utilized data generated by DeepSeek-R1, to fine-tune Llama-3.1-8B. This process, known as distillation (a subcategory of fine-tuning), injects the data in - Network accessline 9052
With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune for free on Colab, Kaggle, or locally with just 3GB VRAM by using our [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). By fine-tuning a pre-trained m - Network accessline 9068
You may have heard that fine-tuning does not make a model learn new knowledge or RAG performs better than fine-tuning. That is **false**. Read more FAQ + misconceptions [here](https://docs.unsloth.ai/beginner-start-here/faq-+-is-fine-tuning - Network accessline 9070
{% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 9071
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 9081
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We recommend starting with **Instruct models**, as they allow direct fine-tuning using conversational chat templates (ChatML, ShareGPT etc.) and require less data compared to **Base models** (which uses Alpaca, Vicuna etc). Learn more about - Network accessline 9087
* Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi - Network accessline 9099
We recommend starting with QLoRA, as it is one of the most accessible and effective methods for training models. Our [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss for QLoRA compared to LoRA is now largely r - Network accessline 9101
You can also do [Text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [reasoning (GRPO)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [vision](https://docs.unsloth.ai/basics/vision-f - Network accessline 9105
{% content-ref url="fine-tuning-llms-guide/what-model-should-i-use" %} [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 9106
[what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 9111
* You will need to create a dataset usually with 2 columns - question and answer. The quality and amount will largely reflect the end result of your fine-tune so it's imperative to get this part right. * You can [synthetically generate data - Network accessline 9112
* You can [synthetically generate data](https://docs.unsloth.ai/get-started/datasets-guide#synthetic-data-generation) and structure your dataset (into QA pairs) using ChatGPT or local LLMs. - Network accessline 9113
* You can also use our new Synthetic Dataset notebook which automatically parses documents (PDFs, videos etc.), generates QA pairs and auto cleans data using local models like Llama 3.2. [Access the notebook here.](https://colab.research.go - Network accessline 9119
{% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9120
[datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9123
For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which - Network accessline 9127
Learn how to choose the right [hyperparameters](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) using best practices from research and real-world experiments - and understand how each one affects your - Network accessline 9131
{% content-ref url="fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 9132
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 9137
We would recommend beginners to utilise our pre-made [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) first as it's the easiest way to get started with guided steps. However, if installing locally is a must, you can instal - Network accessline 9139
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 9140
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 9145
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 9146
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 9155
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For testing, you can also take 20% of your training data and use that for testing. If you already used all of the training data, then you have to manually evaluate it. You can also use automatic eval tools like EleutherAI’s [lm-evaluation- - Network accessline 9170
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{% content-ref url="../basics/running-and-saving-models" %} [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 9181
[running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 9184
We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingfac - Network accessline 9186
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To learn more about fine-tuning tips and tricks, head over to our blogs which provide tremendous and educational value: <https://unsloth.ai/blog/> - Network accessline 9196
If you need any help on fine-tuning, you can also join our Discord server [here](https://discord.gg/unsloth) or [Reddit r/unsloth](https://www.reddit.com/r/unsloth/). Thanks for reading and hopefully this was helpful! - Network accessline 9198
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# Fix up addCriterion issues # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 9246
# See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 9292
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide "mention") - Network accessline 9294
***Authors:** A huge thank you to* [*Keith*](https://www.linkedin.com/in/keith-truongcao-7bb84a23b/) *and* [*Datta*](https://www.linkedin.com/in/datta0/) *for contributing to this article!* - Network accessline 9311
This was [reported](https://github.com/QwenLM/Qwen2.5-VL/issues/759) as well in Qwen2.5-VL-7B-Instruct output unexpected results "addCriterion". In fact we see this as well! We tried both non Unsloth, bfloat16 and float16 machines and other - Network accessline 9320
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This update in addition adds GSPO ([Group Sequence Policy Optimization](https://arxiv.org/abs/2507.18071)) which is a variant of GRPO made by the Qwen team at Alibaba. They noticed that GRPO implicitly results in importance weights for each - Network accessline 9343
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See our guide below for the complete process on how to save to [Ollama](https://github.com/ollama/ollama): - Network accessline 9373
{% content-ref url="../../get-started/fine-tuning-llms-guide/tutorial-how-to-finetune-llama-3-and-use-in-ollama" %} [tutorial-how-to-finetune-llama-3-and-use-in-ollama](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/tutorial-how - Network accessline 9374
[tutorial-how-to-finetune-llama-3-and-use-in-ollama](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/tutorial-how-to-finetune-llama-3-and-use-in-ollama) - Network accessline 9379
You can save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingface.c - Network accessline 9381
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Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf> - Network accessline 9403
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* It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no - Network accessline 9434
* <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark> * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/ - Network accessline 9435
* Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) - Network accessline 9436
* Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) - Network accessline 9437
* Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb) - Network accessline 9438
* Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) - Network accessline 9439
* Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb) - Network accessline 9440
* **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 9463
{% hint style="success" %} [**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF s - Network accessline 9464
[**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing - Network accessline 9466
The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3 - Network accessline 9471
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| **Qwen3:** [0.6B](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) • [1.7B](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) • [4B](https://huggingface.co/unsloth/Qwen3-4B-GGUF) • [8B](https://huggingface.co/unsloth/Qwen3-8B-GGUF) • [14B](h - Network accessline 9486
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - Network accessline 9487
| **DeepSeek:** [R1-0528](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-0528-how-to-run-locally#model-uploads) • [V3-0324](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF-UD) • [R1-Distill-Llama](ht - Network accessline 9488
| **Gemma 3:** [4B](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) • [12B](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF) • [27B](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF) • [QAT](https://huggingface.co/unsloth/gemma-3-12 - Network accessline 9494
[Accuracy is Not All You Need](https://arxiv.org/pdf/2407.09141) showcases how pruning layers, even by selecting unnecessary ones still yields vast differences in terms of "flips". A "flip" is defined as answers changing from incorrect to c - Network accessline 9496
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Most frameworks report perplexity and KL Divergence using a test set of Wikipedia articles. However, we noticed using the calibration dataset which is also Wikipedia related causes quants to overfit, and attain lower perplexity scores. We u - Network accessline 9514
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* Llama 3.1 (8B) Instruct has a MMLU 5 shot accuracy of 67.8% using a naive MMLU implementation. We find however Llama **tokenizes "A" and "\_A" (A with a space in front) as different token ids**. If we consider both spaced and non spaced t - Network accessline 9517
* Interestingly Llama 3 as per Eleuther AI's [LLM Harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/llama3/instruct/mmlu/_continuation_template_yaml) also appends <mark style="background-color:purple;">**" - Network accessline 9518
* There are many other subtle issues, and so to benchmark everything in a controlled environment, we designed our own MMLU implementation from scratch by investigating [github.com/hendrycks/test](https://github.com/hendrycks/test) directly, - Network accessline 9524
1. Q4\_0 GGUF - Quantizes all layers to Q4\_0 via the formula `w = q * block_scale` with each block having 32 weights. See [llama.cpp wiki ](https://github.com/ggml-org/llama.cpp/wiki/Tensor-Encoding-Schemes)for more details. - Network accessline 9525
2. int4 version - presumably [TorchAO int4 style](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md)? - Network accessline 9555
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* Llama 4 Scout changed the RoPE Scaling configuration in their official repo. We helped resolve issues in llama.cpp to enable this [change here](https://github.com/ggml-org/llama.cpp/pull/12889) - Network accessline 9598
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* Llama 4's QK Norm's epsilon for both Scout and Maverick should be from the config file - this means using 1e-05 and not 1e-06. We helped resolve these in [llama.cpp](https://github.com/ggml-org/llama.cpp/pull/12889) and [transformers](htt - Network accessline 9600
* The Llama 4 team and vLLM also independently fixed an issue with QK Norm being shared across all heads (should not be so) [here](https://github.com/vllm-project/vllm/pull/16311). MMLU Pro increased from 68.58% to 71.53% accuracy. - Network accessline 9601
* [Wolfram Ravenwolf](https://x.com/WolframRvnwlf/status/1909735579564331016) showcased how our GGUFs via llama.cpp attain much higher accuracy than third party inference providers - this was most likely a combination of the issues explaine - Network accessline 9603
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apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9618
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9619
git clone https://github.com/ggml-org/llama.cpp - Network accessline 9652
[Credential-related source omitted] - Network accessline 9658
A huge thank you to Rohan Pandey for his [Flex Attention implementation](https://x.com/khoomeik/status/1955693558914310608), which directly inspired the development of Unsloth's Flex Attention implementation. - Network accessline 9664
OpenAI leveraged **Attention Sinks** from the Efficient Streaming Language Models with Attention Sinks [paper](https://arxiv.org/abs/2309.17453) which shows that you can use a small sliding window, except you must add a global attention on - Network accessline 9666
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The paper also shows that the [Attention Is Off By One method](https://www.evanmiller.org/attention-is-off-by-one.html) does partially work, except one must also add a few extra sink tokens to get lower perplexities. **The paper shows that - Network accessline 9676
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Flex Attention <https://pytorch.org/blog/flexattention/> is extremely powerful as it provides the practitioner 2 customization routes for the attention mechanism - a **score modifier (f)** and a **masking function (M)**. - Network accessline 9692
By using some visualization utilities from [Flex Attention's Github repo](https://github.com/meta-pytorch/attention-gym), we can visualize this. Assume the sequence length was 16, and a sliding window of 5. On the left is the last sink colu - Network accessline 9698
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**Interesting finding**: The official Flex Attention sliding window implementations considers the window size as the number of last tokens **PLUS ONE** as it includes the current token. The HuggingFace and GPT OSS implementations strictly o - Network accessline 9716
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We also confirmed through OpenAI's official GPT-OSS implementation on whether we attend to the last N or N+1 tokens here: <https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py> - Network accessline 9728
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To run inference on the merged model, you can use vLLM and Llama.cpp among others. OpenAI recommends these [inference settings](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#recommended-settings) for both models: `tempe - Network accessline 9774
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 9794
We [recently collaborated with Hugging Face](https://github.com/huggingface/transformers/pull/40197) to resolve inference issues by using OpenAI’s kernels and ensuring that `swiglu_limit = 7.0` is correctly applied during MXFP4 inference. - Network accessline 9800
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OpenAI's sink token implementation is [provided here](https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py). We provide it below: - Network accessline 9820
The HuggingFace transformers implementation is [provided here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py). We also provide it below: - Access to secretsline 9868
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[Credential-related source omitted] - Dangerous shell operationline 9887
* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 9949
{% columns %} {% column %} With Unsloth and TorchAO’s QAT support, you can also fine-tune a model in Unsloth and seamlessly export it to [ExecuTorch](https://github.com/pytorch/executorch) (PyTorch’s solution for on-device inference) and de - Network accessline 9951
With Unsloth and TorchAO’s QAT support, you can also fine-tune a model in Unsloth and seamlessly export it to [ExecuTorch](https://github.com/pytorch/executorch) (PyTorch’s solution for on-device inference) and deploy it directly on mobile. - Network accessline 9958
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Then **try QAT with our free** [**Qwen3 (4B) notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb) - Network accessline 9986
DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. - Network accessline 9988
These instructions are for our pre-made Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). If you are installing Unsloth locally, you can also copy our notebooks inside your favorite code editor. We'll be using - Network accessline 9990
| [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **-** GSPO | [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_ - Network accessline 9992
| [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb - Network accessline 9997
If you're using our Colab notebook, click **Runtime > Run all**. We'd highly recommend you checking out our [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) before getting started. - Network accessline 9999
If installing locally, ensure you have the correct [requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and use `pip install unsloth` on Linux or follow our [Windows install ](https://docs.unsloth.ai/ - Network accessline 10001
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Before we get started, it is recommended to learn more about GRPO, reward functions and how they work. Read more about them including [tips & tricks](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#basics-tips)[ here] - Network accessline 10013
We have pre-selected optimal settings for the best results for you already and you can change the model to whichever you want listed in our [supported models](https://docs.unsloth.ai/get-started/all-our-models). Would not recommend changing - Network accessline 10015
{% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 10016
For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) - Network accessline 10019
[Credential-related source omitted] - Network accessline 10022
We have pre-selected OpenAI's [GSM8K](https://huggingface.co/datasets/openai/gsm8k) dataset which contains grade school math problems but you could change it to your own or any public one on Hugging Face. You can read more about [datasets h - Network accessline 10026
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{% hint style="success" %} **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) - Network accessline 10048
**NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) - Network accessline 10051
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Qwen LLMs with minimal accuracy loss. - Network accessline 10055
[Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3 and Qwen3 MOE models — 2x faster, with 70% less VR - Network accessline 10061
| Dynamic 2.0 GGUF (to run) - Network accessline 10063
| <ul><li><a href="https://huggingface.co/unsloth/Qwen3-0.6B-GGUF">0.6B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-1.7B-GGUF">1.7B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-4B-GGUF">4B</a></li><li><a href="htt - Network accessline 10098
Qwen3 models come with built-in "thinking mode" to boost reasoning and improve response quality - similar to how [QwQ-32B](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/qwq-32b-how-to-run-effectively) worked. Instru - Network accessline 10136
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 10183
## Go to https://docs.unsloth.ai for advanced tips like - Network accessline 10185
**URL:** llms-txt#go-to-https://docs.unsloth.ai-for-advanced-tips-like - Network accessline 10195
We're introducing GSPO which is a variant of [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#from-rlhf-ppo-to-grpo-and-rlvr) made by the Qwen team at Alibaba. They noticed the observation that when GRPO takes i - Network accessline 10197
* Use our free GSPO notebooks for: [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) and [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blo - Network accessline 10201
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With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune TTS models 1.5x faster with 50% less memory than other implementations with Flash Attention 2. This support includes Sesame CSM, Orpheus, and models supported by transf - Network accessline 10272
We've uploaded TTS models (original and quantized variants) to our [Hugging Face page](https://huggingface.co/collections/unsloth/text-to-speech-tts-models-68007ab12522e96be1e02155). - Network accessline 10276
| [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) | [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipy - Network accessline 10278
| [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb) | [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb) - Network accessline 10312
At minimum, a TTS fine-tuning dataset consists of **audio clips and their corresponding transcripts** (text). Let’s use the [*Elise* dataset](https://huggingface.co/datasets/MrDragonFox/Elise) which is \~3 hour single-speaker English speech - Network accessline 10314
* [`MrDragonFox/Elise`](https://huggingface.co/datasets/MrDragonFox/Elise) – an augmented version with **emotion tags** (e.g. \<sigh>, \<laughs>) embedded in the transcripts. These tags in angle brackets indicate expressions (laughter, sigh - Network accessline 10315
* [`Jinsaryko/Elise`](https://huggingface.co/datasets/Jinsaryko/Elise) – base version with transcripts without special tags. - Network accessline 10361
You can now run **Grok 2** (aka Grok 2.5), the 270B parameter model by xAI. Full precision requires **539GB**, while the Unsloth Dynamic 3-bit version shrinks size down to just **118GB** (a 75% reduction). GGUF: [Grok-2-GGUF](https://huggin - Network accessline 10363
The **3-bit Q3\_K\_XL** model runs on a single **128GB Mac** or **24GB VRAM + 128GB RAM**, achieving **5+ tokens/s** inference. Thanks to the llama.cpp team and community for [supporting Grok 2](https://github.com/ggml-org/llama.cpp/pull/15 - Network accessline 10365
All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized Grok LLMs with minimal accuracy loss. - Network accessline 10399
{% stepper %} {% step %} Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if - Network accessline 10401
Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or - Network accessline 10425
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10426
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10427
git clone https://github.com/ggml-org/llama.cpp cd llama.cpp && git fetch origin pull/15539/head:MASTER && git checkout MASTER && cd .. cmake llama.cpp -B llama.cpp/build \ - Network accessline 10481
See <https://docs.sglang.ai/get_started/install.html> for more details - Network accessline 10546
<table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td>33.8GB</td><td><a href="https://huggingface.co/unsloth/Llama-4- - Network accessline 10554
| MoE Bits | Type | Disk Size | HF Link | | -------- | --------- | --------- | ---------------------------------------------------------------- - Network accessline 10555
| -------- | --------- | --------- | --------------------------------------------------------------------------------------------------- | | 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E- - Network accessline 10556
| 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_S) | - Network accessline 10557
| 1.93bit | IQ1\_M | 128GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_M) | - Network accessline 10558
| 2.42-bit | IQ2\_XXS | 140GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ2_XXS) | - Network accessline 10559
| 2.71-bit | Q2\_K\_XL | 151B | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q2_K_XL) | - Network accessline 10560
| 3.5-bit | Q3\_K\_XL | 193GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q3_K_XL) | - Network accessline 10561
| 4.5-bit | Q4\_K\_XL | 243GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q4_K_XL) | - Network accessline 10574
* A BOS token of `<|begin_of_text|>` is auto added during tokenization (do NOT add it manually!)  * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow - Network accessline 10575
* According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow;">**suggested optional system prompt**</mark>, which is listed below: - Network accessline 10579
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 10581
2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/Llama-4-Scout-17B-16 - Network accessline 10607
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10608
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10609
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 10634
[Credential-related source omitted] - Network accessline 10741
[Credential-related source omitted] - Network accessline 10763
Unsloth supports fine-tuning of DeepSeek-OCR. Since the default model isn’t fine-tunable, we added changes from the [Stranger Vision HF](https://huggingface.co/strangervisionhf) team, to then enable fine-tuning. As usual, Unsloth trains Dee - Network accessline 10767
* DeepSeek-OCR: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) - Network accessline 10768
* DeepSeek-OCR: [Fine-tuning + Evaluation notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\)-Eval.ipynb) (A100) - Network accessline 10800
You can now train OpenAI [gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) with RL and GRPO via [Unsloth](https://github.com/unslothai/unsloth). Unsloth now offers the <mark style="background-color:$success;">**fast - Network accessline 10802
Since reinforcement learning (RL) on gpt-oss isn't yet vLLM compatible, we had to rewrite the inference code from Transformers code to deliver 3x faster inference for gpt-oss at \~21 tokens/s. For BF16, Unsloth also achieves the fastest inf - Network accessline 10804
* **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\ - Network accessline 10807
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Unsloth is the only framework to support 4-bit RL for gpt-oss. All performance gains are due to Unsloth's unique [weight sharing](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#what-unsloth-offers-for-rl), [Flex Attenti - Network accessline 10813
{% hint style="warning" %} Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and - Network accessline 10814
Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt - Network accessline 10821
[Credential-related source omitted] - Network accessline 10823
Inference is crucial in RL training, since we need it to generate candidate solutions before maximizing some reward function ([see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) for a more detailed explanation). - Network accessline 10825
vLLM does not support RL for gpt-oss since it lacks BF16 training and LoRA support for gpt-oss. Without Unsloth, only training via full precision BF16 works, making <mark style="background-color:$warning;">memory use</mark> <mark style="bac - Network accessline 10833
We had to change our implementation for attention sinks as [described here](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) to allow generation to work with left padding. We had to get the logs - Network accessline 10855
The harder part is batch generation. Sequences differ in length, so padding complicates mask creation. Flex Attention had a lot of [challenges](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) and dynamic mas - Network accessline 10857
> *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) - Network accessline 10871
[Credential-related source omitted] - Network accessline 10886
It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking). - Network accessline 10888
<div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div> - Network accessline 10890
In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation setting and showcase tangible solutions to - Network accessline 10908
LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs. - Network accessline 10910
RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo - Network accessline 10914
| [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo - Network accessline 10974
Unsloth now supports NVIDIA’s Blackwell architecture GPUs, including RTX 50-series GPUs (5060–5090), RTX PRO 6000, and GPUS such as B200, B40, GB100, GB102 and more! You can read the official [NVIDIA blogpost here](https://developer.nvidia. - Network accessline 10976
Unsloth is now compatible with every NVIDIA GPU from 2018+ including the [DGX Spark](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). - Network accessline 10978
> **Our new** [**Docker image**](#docker) **supports Blackwell. Run the Docker image and start training!** [**Guide**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) - Network accessline 11024
By the end of this tutorial, you will create a custom chatbot by **finetuning Llama-3** with [**Unsloth**](https://github.com/unslothai/unsloth) for free. It can run locally via [**Ollama**](https://github.com/ollama/ollama) on your PC, or - Network accessline 11026
[Credential-related source omitted] - Network accessline 11028
**Unsloth** makes finetuning much easier, and can automatically export the finetuned model to **Ollama** with integrated automatic `Modelfile` creation! If you need help, you can join our Discord server: <https://discord.com/invite/unsloth> - Network accessline 11030
{% hint style="warning" %} **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You c - Network accessline 11031
**If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You can use it directly there or - Network accessline 11036
[Unsloth](https://github.com/unslothai/unsloth) makes finetuning LLMs like Llama-3, Mistral, Phi-3 and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy! We will be using Google Colab which provides a free GPU during - Network accessline 11038
* [Ollama Llama-3 Alpaca](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) (notebook which we will be using) - Network accessline 11039
* [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing) - Network accessline 11043
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[Ollama ](https://github.com/ollama/ollama)allows you to run language models from your own computer in a quick and simple way! It quietly launches a program which can run a language model like Llama-3 in the background. If you suddenly want - Network accessline 11049
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Options include `True`, `False` and `"unsloth"`. We suggest `"unsloth"` since we reduce memory usage by an extra 30% and support extremely long context finetunes.You can read up here: <https://unsloth.ai/blog/long-context> for more details. - Network accessline 11121
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You can access the GPT4 version of the Alpaca dataset here: <https://huggingface.co/datasets/vicgalle/alpaca-gpt4>. An older first version of the dataset is here: <https://github.com/tatsu-lab/stanford_alpaca>. Below shows some examples of - Network accessline 11127
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To access the Titanic finetuning notebook or if you want to upload a CSV or Excel file, go here: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing> - Network accessline 11153
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Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin - Network accessline 11225
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We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via <https://huggingface - Network accessline 11264
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Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf> - Network accessline 11286
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If you want to learn how to do reward modelling, do continued pretraining, export to vLLM or GGUF, do text completion, or learn more about finetuning tips and tricks, head over to our [Github](https://github.com/unslothai/unsloth#-finetune- - Network accessline 11330
If you need any help on finetuning, you can also join our Discord server [here](https://discord.gg/unsloth). If you want help with Ollama, you can also join their server [here](https://discord.gg/ollama). - Network accessline 11334
To access our Alpaca dataset example click [here](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing), and our CSV / Excel finetuning guide is [here](https://colab.research.google.com/drive/1VYkncZMfGFkeCE - Network accessline 11369
## https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp#L19 - Network accessline 11371
**URL:** llms-txt#https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp#l19 - Network accessline 11396
Google releases Gemma 3 with a new 270M model and the previous 1B, 4B, 12B, and 27B sizes. The 270M and 1B are text-only, while larger models handle both text and vision. We provide GGUFs, and a guide of how to run it effectively, and how t - Network accessline 11398
{% hint style="success" %} **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co - Network accessline 11399
**NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co/collections/unsloth/gemma- - Network accessline 11401
Also see our [Gemma 3n Guide](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune). - Network accessline 11408
* Fine-tune Gemma 3 (4B) with vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - Network accessline 11417
| GGUF - Network accessline 11419
| <ul><li><a href="https://huggingface.co/unsloth/gemma-3-270m-it-GGUF">270M</a> - new</li><li><a href="https://huggingface.co/unsloth/gemma-3-1b-it-GGUF">1B</a></li><li><a href="https://huggingface.co/unsloth/gemma-3-4b-it-GGUF">4B</a></li - Network accessline 11444
To run the models on your phone, we recommend using any mobile app that can run GGUFs locally on edge devices like phones. After fine-tuning you can export it to GGUF then run it locally on your phone. Ensure your phone has enough RAM/power - Network accessline 11458
1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. - Network accessline 11462
3. **OR** download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/gemma-3-27b-i - Network accessline 11479
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 11481
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 11481
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 11491
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11492
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 11493
git clone https://github.com/ggerganov/llama.cpp - Network accessline 11514
At [Unsloth](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/), our mission is to make AI as accurate and accessible as possible. Train, run, evaluate and save gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma LLMs 2 - Network accessline 11518
<a href="beginner-start-here" class="button primary">Get started</a> <a href="https://github.com/unslothai/unsloth" class="button secondary">Our GitHub</a> - Network accessline 11520
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{% column %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 11524
{% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) {% endcontent-ref %} - Network accessline 11525
[fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 11528
{% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 11529
[unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 11532
{% column %} {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 11533
{% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) {% endcontent-ref %} - Network accessline 11534
[all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 11537
{% content-ref url="../models/tutorials-how-to-fine-tune-and-run-llms" %} [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms) - Network accessline 11538
[tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms) - Network accessline 11543
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* Unsloth streamlines model training locally and on Colab/Kaggle, covering loading, quantization, training, evaluation, saving, exporting, and integration with inference engines like Ollama, llama.cpp, and vLLM. * We directly collaborate wi - Network accessline 11546
* We directly collaborate with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_con - Network accessline 11547
* Unsloth is the only training framework to support all model types: [vision](https://docs.unsloth.ai/basics/vision-fine-tuning), [text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), BERT, [reinforcement l - Network accessline 11549
* Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **8-bit** training. * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speec - Network accessline 11550
* The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-n - Network accessline 11551
* Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more. Any model that works in transform - Network accessline 11552
* **0% loss in accuracy** - no approximation methods - all exact. * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! * Unsloth supports Linux, Windows, Colab, Kagg - Network accessline 11553
* [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! - Network accessline 11554
* Unsloth supports Linux, Windows, Colab, Kaggle, **NVIDIA** and [**AMD**](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) & **Intel**. See: - Network accessline 11556
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 11557
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 11562
Use our official **Docker image**: `unsloth/unsloth`. Read our [**Docker guide**](https://docs.unsloth.ai/get-started/install-and-update/docker)**.** - Network accessline 11564
For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/install-and-update/windows-installation). - Network accessline 11566
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 11567
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 11572
[**Fine-tuning** an LLM](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) customizes its behavior, enhances domain knowledge, and optimizes performance for specific tasks. By fine-tuning a pre-trained model (e.g. Llama-3.1-8B) on - Network accessline 11578
[**Reinforcement Learning (RL)**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) is where an "agent" learns to make decisions by interacting with an environment and receiving **feedback** in the form of **rewards** or - Network accessline 11592
{% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 11593
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 11596
{% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 11597
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 11600
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Local training can be complex due to dependency hell or breaking environments. Unsloth’s [Docker image](https://hub.docker.com/r/unsloth/unsloth) can bypass these issues. No setup is needed: pull and run the image and start training. - Network accessline 11708
* **Unsloth official Docker image:** [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) - Network accessline 11712
Unsloth’s Docker image is stable, up-to-date and works in [supported setups](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements#system-requirements) like Windows. - Network accessline 11728
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\ - Network accessline 11729
Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation): - Dangerous shell operationline 11732
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 11739
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[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and 50-series GPUs, use this same im - Network accessline 11746
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 11753
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If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no - Network accessline 11764
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It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking). - Network accessline 11850
<div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div> - Network accessline 11852
**Can you counter reward hacking? Yes!** In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation s - Network accessline 11876
Unsloth works on Linux, Windows, NVIDIA, AMD, Google Colab and more. See our [system requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements). - Network accessline 11918
See <https://docs.vllm.ai/en/stable/getting_started/installation> for more details - Access to secretsline 11960
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{% hint style="warning" %} Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install). - Network accessline 12005
Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install). - Network accessline 12029
pip uninstall unsloth unsloth_zoo -y && pip install --no-deps git+https://github.com/unslothai/unsloth_zoo.git && pip install --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 12034
pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git - Network accessline 12035
pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 12040
pip install --upgrade pip pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 12041
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
optional-skills/payments/mpp-agent/SKILL.md
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Wraps the Machine Payments Protocol (MPP, https://mpp.dev) clients so Hermes can pay for per-request API access against servers that respond with `HTTP 402 Payment Required`. - Network accessline 41
- A funded wallet (Tempo / Privy / AgentCash) OR an `mppx` account - For Tempo / Privy / AgentCash: follow their respective onboarding skills: - `https://tempo.xyz/SKILL.md` - Network accessline 42
- For Tempo / Privy / AgentCash: follow their respective onboarding skills: - `https://tempo.xyz/SKILL.md` - `https://agents.privy.io/skill.md` - Network accessline 43
- `https://tempo.xyz/SKILL.md` - Network accessline 44
- `https://agents.privy.io/skill.md` - Network accessline 45
- `https://agentcash.dev/skill.md` - Network accessline 67
curl -i <url> - Network accessline 101
The Tempo Wallet skill at https://tempo.xyz/SKILL.md is the canonical reference; fetch it with `web_extract` and follow it. Headline: - Network accessline 108
Spend controls and service discovery live in the wallet UI at https://wallet.tempo.xyz. - Network accessline 114
- **Zero-amount challenges.** Some MPP endpoints charge `$0.00` and just want a proof credential. These work without a funded wallet. Don't refuse them as "broken." - **Wallet keys never enter agent context.** All four clients store keys un - Network accessline 116
- **Server-side MPP is a different skill.** If the user wants to ADD 402 to their own API, this skill is wrong — point them at https://mpp.dev/quickstart/server and the `mppx/nextjs` / `mppx/hono` / `mppx/express` / `mppx/elysia` middleware
optional-skills/payments/stripe-link-cli/SKILL.md
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Wraps [@stripe/link-cli](https://github.com/stripe/link-cli) so Hermes can complete purchases on the user's behalf using one-time-use virtual cards or Shared Payment Tokens (SPT). Every spend is gated by an in-app approval in the Link mobil - Network accessline 38
- A Link account at https://app.link.com — created/linked during first `link-cli` auth - Network accessline 39
- At least one payment method — added during first run at https://app.link.com/wallet - Dangerous shell operationline 156
rm -f ~/.hermes/cache/scratch/link-card.json
optional-skills/payments/stripe-projects/SKILL.md
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Wraps the [Stripe Projects](https://projects.dev) CLI plugin so Hermes can provision SaaS services (Neon, Twilio, Vercel, etc.), generate and sync credentials into the user's `.env`, and manage billing across providers from one place. - Network accessline 33
- Stripe CLI installed (Homebrew on macOS, package manager on Linux, or download from https://docs.stripe.com/stripe-cli/install) - Network accessline 46
Linux: follow the platform-specific install at https://docs.stripe.com/stripe-cli/install, then:
optional-skills/productivity/airtable/SKILL.md
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hermes: tags: [Airtable, Productivity, Database, API] homepage: https://airtable.com/developers/web/api/introduction - Network accessline 13
tags: [Airtable, Productivity, Database, API] homepage: https://airtable.com/developers/web/api/introduction --- - Network accessline 14
homepage: https://airtable.com/developers/web/api/introduction - Network accessline 23
1. Create a **Personal Access Token (PAT)** at https://airtable.com/create/tokens (tokens start with `pat...`). - Access to secretsline 31
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Base curl pattern: - Dangerous shell operationline 46
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| Date | `"Due": "2026-04-01"` | | DateTime (UTC) | `"At": "2026-04-01T14:30:00.000Z"` | | URL / Email / Phone | `"Link": "https://…"` | - Network accessline 63
| DateTime (UTC) | `"At": "2026-04-01T14:30:00.000Z"` | | URL / Email / Phone | `"Link": "https://…"` | | Attachment | `"Files": [{"url": "https://…"}]` (Airtable fetches + rehosts) | - Network accessline 64
| URL / Email / Phone | `"Link": "https://…"` | - Network accessline 65
| Attachment | `"Files": [{"url": "https://…"}]` (Airtable fetches + rehosts) | - Dangerous shell operationline 75
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FORMULA="{Status}='Todo'" ENC=$(python -c 'import sys, urllib.parse; print(urllib.parse.quote(sys.argv[1], safe=""))' "$FORMULA") curl -s "https://api.airtable.com/v0/$BASE_ID/$TABLE?filterByFormula=$ENC&maxRecords=20" \ - Network accessline 102
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[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted] - Dangerous shell operationline 133
[Credential-related source omitted] - Network accessline 133
[Credential-related source omitted] - Access to secretsline 133
[Credential-related source omitted] - Access to secretsline 134
[Credential-related source omitted] - Network accessline 141
[Credential-related source omitted] - Access to secretsline 141
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Dangerous shell operationline 156
[Credential-related source omitted] - Network accessline 156
[Credential-related source omitted] - Access to secretsline 156
[Credential-related source omitted] - Access to secretsline 157
[Credential-related source omitted] - Network accessline 164
[Credential-related source omitted] - Access to secretsline 164
[Credential-related source omitted] - Access to secretsline 165
[Credential-related source omitted] - Dangerous shell operationline 178
[Credential-related source omitted] - Network accessline 178
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted] - Access to secretsline 179
[Credential-related source omitted] - Dangerous shell operationline 184
[Credential-related source omitted] - Network accessline 184
[Credential-related source omitted] - Access to secretsline 184
[Credential-related source omitted] - Access to secretsline 185
[Credential-related source omitted] - Network accessline 193
OFFSET="" while :; do URL="https://api.airtable.com/v0/$BASE_ID/$TABLE?pageSize=100" - Network accessline 194
while :; do URL="https://api.airtable.com/v0/$BASE_ID/$TABLE?pageSize=100" [ -n "$OFFSET" ] && URL="$URL&offset=$OFFSET" - Network accessline 195
[Credential-related source omitted] - Network accessline 196
[Credential-related source omitted] - Network accessline 197
[Credential-related source omitted] - Access to secretsline 197
[Credential-related source omitted] - Network accessline 206
[Credential-related source omitted] - Access to secretsline 206
[Credential-related source omitted] - Network accessline 217
- **PATCH vs PUT.** `PATCH` merges supplied fields into the record. `PUT` replaces the record entirely and clears any field you didn't include. Default to `PATCH`. - **Single-select options must exist.** Writing `"Status": "Shipping"` when - Network accessline 218
- **Single-select options must exist.** Writing `"Status": "Shipping"` when `Shipping` isn't in the field's option list errors with `INVALID_MULTIPLE_CHOICE_OPTIONS` unless you pass `"typecast": true` (which auto-creates the option). - **Pe - Network accessline 219
- **Per-base token scoping.** A `403` on one base while another works means the token's Access list doesn't include that base — not a scope or auth issue. Send the user to https://airtable.com/create/tokens to grant it. - Access to secretsline 225
[Credential-related source omitted]
optional-skills/productivity/box/SKILL.md
- Network accessline 12
tags: [Box, Productivity, Cloud Storage, Collaboration, Metadata, Content Extraction, CLI, SDK] related_skills: [google-workspace] homepage: https://developer.box.com/ - Network accessline 13
related_skills: [google-workspace] homepage: https://developer.box.com/ --- - Network accessline 14
homepage: https://developer.box.com/ - Network accessline 109
- File: `https://app.box.com/file/<FILE_ID>` - Network accessline 110
- Folder: `https://app.box.com/folder/<FOLDER_ID>` - Network accessline 111
- Hub: `https://app.box.com/hubs/<HUB_ID>`
optional-skills/productivity/box/references/content-workflows.md
optional-skills/productivity/box/references/hubs.md
- Network accessline 19
For a known Hub URL or ID, run `box hubs:get <HUB_ID>` directly even if the list is empty. Report each Hub as `https://app.box.com/hubs/<HUB_ID>`. Check `is_ai_enabled` before asking a question, then make one bounded Hub Ask request to veri - Network accessline 72
- [Box Hubs API overview](https://developer.box.com/guides/hubs-api/) - Network accessline 73
- [Box Free Developer Plan](https://developer.box.com/guides/getting-started/free-developer-plan/) - Network accessline 74
- [Box AI Ask API](https://developer.box.com/reference/post-ai-ask/) - Network accessline 75
- [Ask questions about a Hub](https://developer.box.com/guides/box-ai/ai-tutorials/ask-questions/) - Network accessline 76
- [Box AI for Hubs](https://support.box.com/hc/en-us/articles/29347206309395-Box-AI-for-Hubs) - Network accessline 77
- [Box Hubs limits](https://support.box.com/hc/en-us/articles/28323495455123-Box-Hubs-Known-Issues-and-Limitations)
optional-skills/productivity/box/references/oauth-setup.md
- Network accessline 58
Use this path only when the requested operation needs a scope unavailable through the official CLI app, such as **Manage webhooks**. Open the [Box Developer Console](https://app.box.com/developers/console), create or select a Platform App w - Network accessline 60
Use the same topology decision as the official app. For a same-host browser, add `http://localhost:3000/callback` as an OAuth redirect URI in the app's **Configuration** tab, save it, then run: - Network accessline 66
If port 3000 cannot bind, choose another free local port, add the exact `http://localhost:<PORT>/callback` URI to the Platform App, save the configuration, stop the failed login process, and retry with the matching `--port`. Unlike the offi - Network accessline 80
- [Box CLI quick start](https://developer.box.com/guides/cli/quick-start/) - Network accessline 81
- [Box CLI headless login](https://developer.box.com/guides/cli/headless-login/) - Network accessline 82
- [OAuth 2.0 guide](https://developer.box.com/guides/authentication/oauth2/) - Network accessline 83
- [Box OAuth scopes](https://developer.box.com/guides/api-calls/permissions-and-errors/scopes/)
optional-skills/productivity/box/references/rest-api.md
- Network accessline 35
- [Box API reference](https://developer.box.com/reference/) - Network accessline 36
- [Box Notes API: create a note from Markdown](https://developer.box.com/guides/box-notes/convert-markdown/) - Network accessline 37
- [OAuth 2.0](https://developer.box.com/guides/authentication/oauth2/)
optional-skills/productivity/box/references/sdk-development.md
- Network accessline 19
- [Python SDK Gen](https://github.com/box/box-python-sdk-gen) - Network accessline 20
- [The `box` npm package](https://developer.box.com/guides/tooling/box-npm-package) - Network accessline 21
- [Existing Node SDK projects](https://github.com/box/box-node-sdk) - Network accessline 22
- [Other Box SDKs](https://developer.box.com/guides/tooling/sdks/)
optional-skills/productivity/box/references/search-and-ai.md
- Network accessline 141
- [Box AI API](https://developer.box.com/ai/box-ai-api/) - Network accessline 142
- [Structured metadata extraction](https://developer.box.com/guides/box-ai/ai-tutorials/extract-metadata-structured/) - Network accessline 143
- [Metadata template scopes](https://developer.box.com/guides/metadata/scopes/) - Network accessline 144
- [Global metadata query limitation](https://developer.box.com/guides/metadata/queries/limitations/) - Network accessline 145
- [Box AI trust](https://www.box.com/ai/trust/) - Network accessline 146
- [AI units and plan access](https://support.box.com/hc/en-us/articles/45612941554835-Expanded-AI-API-Access-and-AI-Units-for-Business-Business-Plus-and-Enterprise-Plans) - Network accessline 147
- [Metadata template permissions](https://developer.box.com/guides/metadata/templates/create/)
optional-skills/productivity/box/references/webhooks-and-events.md
- Network accessline 9
box webhooks:create folder <FOLDER_ID> \ --triggers FILE.UPLOADED,FILE.VERSION_UPLOADED \ --address https://example.com/box/webhook --json - Network accessline 11
--address https://example.com/box/webhook --json - Network accessline 41
- [Box webhooks](https://developer.box.com/guides/webhooks/) - Network accessline 42
- [Events resource](https://developer.box.com/reference/resources/event/) - Network accessline 43
- [User Events](https://developer.box.com/guides/events/user-events/for-user/)
optional-skills/productivity/canvas/SKILL.md
- Access to secretsline 9
[Credential-related source omitted] - Network accessline 32
[Credential-related source omitted] - Network accessline 33
CANVAS_BASE_URL=https://yourschool.instructure.com - Network accessline 73
[Credential-related source omitted] - Network accessline 74
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Network accessline 77
[Credential-related source omitted] - Network accessline 78
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted]
optional-skills/productivity/canvas/scripts/canvas_api.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Network accessline 49
results = [] while url and len(results) < max_items: resp = requests.get(url, headers=_headers(), params=params, timeout=30) - Network accessline 50
while url and len(results) < max_items: resp = requests.get(url, headers=_headers(), params=params, timeout=30) resp.raise_for_status() - Network accessline 51
resp = requests.get(url, headers=_headers(), params=params, timeout=30)
optional-skills/productivity/here-now/SKILL.md
- Network accessline 11
hermes: tags: [here.now, herenow, publish, deploy, hosting, static-site, web, share, URL, drive, storage] homepage: https://here.now - Network accessline 12
tags: [here.now, herenow, publish, deploy, hosting, static-site, web, share, URL, drive, storage] homepage: https://here.now requires_toolsets: [terminal] - Network accessline 13
homepage: https://here.now - Network accessline 30
→ **https://here.now/docs** - Access to secretsline 59
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Network accessline 73
Outputs the live URL (e.g. `https://bright-canvas-a7k2.here.now/`). - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Network accessline 138
curl -sS https://here.now/api/auth/agent/request-code \ - Network accessline 147
curl -sS https://here.now/api/auth/agent/verify-code \ - Access to secretsline 155
[Credential-related source omitted] - Network accessline 164
"publishes": { "bright-canvas-a7k2": { "siteUrl": "https://bright-canvas-a7k2.here.now/", - Network accessline 165
"bright-canvas-a7k2": { "siteUrl": "https://bright-canvas-a7k2.here.now/", "claimToken": "abc123", - Network accessline 166
"siteUrl": "https://bright-canvas-a7k2.here.now/", - Network accessline 167
[Credential-related source omitted] - Network accessline 168
[Credential-related source omitted] - Network accessline 184
- Read and follow `publish_result.*` lines from script stderr to determine auth mode. - When `publish_result.auth_mode=authenticated`: tell the user the site is **permanent** and saved to their account. No claim URL is needed. - When `publi - Network accessline 185
- When `publish_result.auth_mode=authenticated`: tell the user the site is **permanent** and saved to their account. No claim URL is needed. - When `publish_result.auth_mode=anonymous`: tell the user the site **expires in 24 hours**. Share - Network accessline 186
- When `publish_result.auth_mode=anonymous`: tell the user the site **expires in 24 hours**. Share the claim URL (if `publish_result.claim_url` is non-empty and starts with `https://`) so they can keep it permanently. Warn that claim tokens - Network accessline 203
| `--ttl {seconds}` | Set expiry (authenticated only) | | `--client {name}` | Agent name for attribution (e.g. `hermes`) | | `--base-url {url}` | API base URL (default: `https://here.now`) | - Network accessline 204
| `--client {name}` | Agent name for attribution (e.g. `hermes`) | | `--base-url {url}` | API base URL (default: `https://here.now`) | | `--allow-nonherenow-base-url` | Allow sending auth to non-default `--base-url` | - Network accessline 205
| `--base-url {url}` | API base URL (default: `https://here.now`) | - Network accessline 215
→ **https://here.now/docs** - Network accessline 217
Full docs: https://here.now/docs
optional-skills/productivity/here-now/scripts/drive.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 4
BASE_URL="https://here.now" - Access to secretsline 6
[Credential-related source omitted] - Access to secretsline 7
[Credential-related source omitted] - Network accessline 16
[Credential-related source omitted] - Access to secretsline 16
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[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Network accessline 18
--base-url <url> API base (default: https://here.now) - Dangerous shell operationline 30
rm <drive> <path> [--recursive --confirm <path>] - Network accessline 53
for cmd in curl file; do - Access to secretsline 59
[Credential-related source omitted] - Access to secretsline 73
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Network accessline 77
BASE_URL="${BASE_URL%/}" if [[ "$BASE_URL" != "https://here.now" && "$ALLOW_NON_HERENOW_BASE_URL" -ne 1 ]]; then - Network accessline 78
if [[ "$BASE_URL" != "https://here.now" && "$ALLOW_NON_HERENOW_BASE_URL" -ne 1 ]]; then - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted] - Access to secretsline 86
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[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Network accessline 126
local code if [[ -n "$body" ]]; then code=$(curl -sS -o "$tmp" -w "%{http_code}" -X "$method" "$url" "${auth_header[@]}" -H "content-type: application/json" -d "$body") - Network accessline 127
if [[ -n "$body" ]]; then code=$(curl -sS -o "$tmp" -w "%{http_code}" -X "$method" "$url" "${auth_header[@]}" -H "content-type: application/json" -d "$body") else - Network accessline 128
code=$(curl -sS -o "$tmp" -w "%{http_code}" -X "$method" "$url" "${auth_header[@]}" -H "content-type: application/json" -d "$body") - Network accessline 129
else code=$(curl -sS -o "$tmp" -w "%{http_code}" -X "$method" "$url" "${auth_header[@]}") fi - Network accessline 130
code=$(curl -sS -o "$tmp" -w "%{http_code}" -X "$method" "$url" "${auth_header[@]}") - Dangerous shell operationline 136
rm -f "$tmp" - Dangerous shell operationline 140
rm -f "$tmp" - Access to secretsline 165
[Credential-related source omitted] - Network accessline 219
upload_url=$(echo "$upload" | "$JQ_BIN" -r '.uploadUrl') upload_id=$(echo "$upload" | "$JQ_BIN" -r '.uploadId') http_code=$(curl -sS -o /dev/null -w "%{http_code}" -X PUT "$upload_url" -H "Content-Type: $ct" --data-binary "@$local_file" - Network accessline 220
upload_id=$(echo "$upload" | "$JQ_BIN" -r '.uploadId') http_code=$(curl -sS -o /dev/null -w "%{http_code}" -X PUT "$upload_url" -H "Content-Type: $ct" --data-binary "@$local_file") [[ "$http_code" -ge 200 && "$http_code" -lt 300 ]] || d - Network accessline 221
http_code=$(curl -sS -o /dev/null -w "%{http_code}" -X PUT "$upload_url" -H "Content-Type: $ct" --data-binary "@$local_file") - Access to secretsline 244
[Credential-related source omitted] - Network accessline 253
[[ $# -eq 2 ]] || die "usage: drive.sh cat <drive> <path>" id=$(resolve_drive "$1") curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$2")" "${auth_header[@]}" - Network accessline 254
id=$(resolve_drive "$1") curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$2")" "${auth_header[@]}" ;; - Network accessline 255
curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$2")" "${auth_header[@]}" - Network accessline 328
else mkdir -p "$(dirname "$out")" curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$p")" "${auth_header[@]}" -o "$out" - Network accessline 329
mkdir -p "$(dirname "$out")" curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$p")" "${auth_header[@]}" -o "$out" fi - Network accessline 330
curl -fsS "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$p")" "${auth_header[@]}" -o "$out" - Dangerous shell operationline 340
[[ $# -ge 2 ]] || die "usage: drive.sh rm <drive> <path> [--recursive --confirm <path>]" - Dangerous shell operationline 347
*) die "unexpected rm argument: $1" ;; - Network accessline 355
meta=$(file_meta "$id" "$path") etag=$(echo "$meta" | "$JQ_BIN" -r '.etag') curl -fsS -X DELETE "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$path")" "${auth_header[@]}" -H "If-Match: $etag" | "$JQ_BIN" . - Network accessline 356
etag=$(echo "$meta" | "$JQ_BIN" -r '.etag') curl -fsS -X DELETE "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$path")" "${auth_header[@]}" -H "If-Match: $etag" | "$JQ_BIN" . fi - Network accessline 357
curl -fsS -X DELETE "$BASE_URL/api/v1/drives/$id/files/$(urlenc_path "$path")" "${auth_header[@]}" -H "If-Match: $etag" | "$JQ_BIN" .
optional-skills/productivity/here-now/scripts/publish.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 4
BASE_URL="https://here.now" - Access to secretsline 6
[Credential-related source omitted] - Access to secretsline 8
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 38
--from-drive <drv_...> Publish a Drive snapshot instead of local files --version <dv_...> Drive version for --from-drive (default: current head) --base-url <url> API base (default: https://here.now) - Network accessline 39
--version <dv_...> Drive version for --from-drive (default: current head) --base-url <url> API base (default: https://here.now) --allow-nonherenow-base-url - Network accessline 40
--base-url <url> API base (default: https://here.now) --allow-nonherenow-base-url Allow auth requests to non-default API base URL - Network accessline 61
for cmd in curl file; do - Access to secretsline 67
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Network accessline 104
[Credential-related source omitted] - Access to secretsline 104
[Credential-related source omitted] - Access to secretsline 109
[Credential-related source omitted] - Access to secretsline 114
[Credential-related source omitted] - Network accessline 136
echo "publishing from Drive..." >&2 RESPONSE=$(curl -sS -X POST "$BASE_URL/api/v1/publish/from-drive" \ - Network accessline 137
[Credential-related source omitted] - Access to secretsline 137
[Credential-related source omitted] - Access to secretsline 138
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Access to secretsline 265
[Credential-related source omitted] - Access to secretsline 287
[Credential-related source omitted] - Access to secretsline 288
[Credential-related source omitted] - Access to secretsline 292
[Credential-related source omitted] - Network accessline 308
echo "creating publish ($file_count files)..." >&2 RESPONSE=$(curl -sS -X "$METHOD" "$URL" \ - Network accessline 309
RESPONSE=$(curl -sS -X "$METHOD" "$URL" \ - Network accessline 359
http_code=$(curl -sS -o /dev/null -w "%{http_code}" -X PUT "$upload_url" \ - Network accessline 372
echo "finalizing..." >&2 FIN_RESPONSE=$(curl -sS -X POST "$FINALIZE_URL" \ - Network accessline 373
FIN_RESPONSE=$(curl -sS -X POST "$FINALIZE_URL" \ - Access to secretsline 398
[Credential-related source omitted] - Network accessline 415
SAFE_CLAIM_URL="" if [[ -n "$RESPONSE_CLAIM_URL" && "$RESPONSE_CLAIM_URL" == https://* ]]; then - Network accessline 416
if [[ -n "$RESPONSE_CLAIM_URL" && "$RESPONSE_CLAIM_URL" == https://* ]]; then - Access to secretsline 442
[Credential-related source omitted]
optional-skills/productivity/memento-flashcards/SKILL.md
- Network accessline 195
**Step 1:** Extract the video ID from the URL (e.g. `dQw4w9WgXcQ` from `https://www.youtube.com/watch?v=dQw4w9WgXcQ`). - Network accessline 214
Follow the isolated development-home setup in [Package Management](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow) - Network accessline 215
[Package Management](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow)
optional-skills/productivity/memento-flashcards/scripts/memento_cards.py
optional-skills/productivity/memento-flashcards/scripts/youtube_quiz.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 42
api = youtube_transcript_api.YouTubeTranscriptApi() try: raw = api.fetch(video_id, languages=languages) - Network accessline 43
try: raw = api.fetch(video_id, languages=languages) except Exception as exc: - Network accessline 44
raw = api.fetch(video_id, languages=languages)
optional-skills/productivity/notion/SKILL.md
- Access to secretsline 9
[Credential-related source omitted] - Network accessline 11
hermes: tags: [Notion, Productivity, Notes, Database, API, CLI, Workers] homepage: https://developers.notion.com - Network accessline 12
tags: [Notion, Productivity, Notes, Database, API, CLI, Workers] homepage: https://developers.notion.com --- - Network accessline 13
homepage: https://developers.notion.com - Network accessline 27
1. Create an integration at https://notion.so/my-integrations - Access to secretsline 31
[Credential-related source omitted] - Network accessline 38
# Recommended curl -fsSL https://ntn.dev | bash - Dangerous shell operationline 39
curl -fsSL https://ntn.dev | bash - Network accessline 39
curl -fsSL https://ntn.dev | bash - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 60
else # fall back to curl fi - Network accessline 140
ntn files create < photo.png ntn files create --external-url https://example.com/photo.png - Network accessline 141
ntn files create --external-url https://example.com/photo.png - Access to secretsline 150
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Network accessline 154
## Path B — HTTP + curl (cross-platform, default on Windows) - Network accessline 159
[Credential-related source omitted] - Access to secretsline 159
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Network accessline 169
[Credential-related source omitted] - Access to secretsline 169
[Credential-related source omitted] - Access to secretsline 170
[Credential-related source omitted] - Network accessline 178
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted] - Access to secretsline 179
[Credential-related source omitted] - Network accessline 188
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Network accessline 195
[Credential-related source omitted] - Access to secretsline 195
[Credential-related source omitted] - Access to secretsline 196
[Credential-related source omitted] - Network accessline 205
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Access to secretsline 206
[Credential-related source omitted] - Network accessline 218
[Credential-related source omitted] - Access to secretsline 218
[Credential-related source omitted] - Access to secretsline 219
[Credential-related source omitted] - Network accessline 227
[Credential-related source omitted] - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 228
[Credential-related source omitted] - Network accessline 242
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Network accessline 254
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 255
[Credential-related source omitted] - Network accessline 271
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Network accessline 280
[Credential-related source omitted] - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 281
[Credential-related source omitted] - Network accessline 293
# 1. Create upload curl -s -X POST "https://api.notion.com/v1/file_uploads" \ - Network accessline 294
[Credential-related source omitted] - Access to secretsline 294
[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Network accessline 300
# 2. PUT bytes to the upload_url returned above curl -s -X PUT "{upload_url}" --data-binary @photo.png - Network accessline 301
curl -s -X PUT "{upload_url}" --data-binary @photo.png - Network accessline 315
- **Checkbox:** `{"checkbox": true}` - **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}` - Network accessline 316
- **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}` - **Email:** `{"email": "user@example.com"}` - Network accessline 317
- **URL:** `{"url": "https://..."}` - Access to secretsline 389
[Credential-related source omitted] - Network accessline 395
When asked to build a Worker, scaffold with `ntn workers new`, write the code in `src/index.ts`, set any secrets with `ntn workers env set`, and deploy. Notion's docs at https://developers.notion.com/workers cover the full API surface. - Network accessline 423
- Color: `<span color="blue">text</span>` or block-level `{color="blue"}` on the first line - Math: inline `$x^2$`, block `$$ ... $$` - Citations: `[^https://example.com]` - Network accessline 425
- Citations: `[^https://example.com]` - Network accessline 433
| Task | mac / Linux | Windows | |---|---|---| | Read/write pages, search, query databases | `ntn api ...` | curl | - Network accessline 434
|---|---|---| | Read/write pages, search, query databases | `ntn api ...` | curl | | Read a page for an agent to summarize | `ntn api v1/pages/{id}/markdown` | curl `/markdown` endpoint | - Network accessline 435
| Read/write pages, search, query databases | `ntn api ...` | curl | - Network accessline 436
| Read a page for an agent to summarize | `ntn api v1/pages/{id}/markdown` | curl `/markdown` endpoint | - Network accessline 437
| Upload a file | `ntn files create < file` | 3-step HTTP flow | | One-off API exploration | `ntn api ...` | curl | | Build a sync / webhook / agent tool hosted by Notion | `ntn workers ...` | WSL2 + `ntn workers ...` | - Network accessline 438
| One-off API exploration | `ntn api ...` | curl | - Network accessline 445
- The API cannot set database **view** filters — that's UI-only. - Use `"is_inline": true` when creating data sources to embed them in a page. - Always pass `-s` to curl to suppress progress bars (cleaner agent output). - Network accessline 446
- Use `"is_inline": true` when creating data sources to embed them in a page. - Always pass `-s` to curl to suppress progress bars (cleaner agent output). - Pipe JSON through `jq` when reading: `... | jq '.results[0].properties'`. - Network accessline 447
- Always pass `-s` to curl to suppress progress bars (cleaner agent output).
optional-skills/productivity/notion/references/block-types.md
- Network accessline 78
{"type": "bookmark", "bookmark": {"url": "https://example.com"}} - Network accessline 84
{"type": "image", "image": {"type": "external", "external": {"url": "https://example.com/photo.png"}}} - Network accessline 112
*Contributed by [@dogiladeveloper](https://github.com/dogiladeveloper)*
optional-skills/productivity/property-listings/SKILL.md
- Network accessline 57
| `catches` | Array of risks or questions to verify before a tour. | | `images` | Direct HTTPS photo URLs in listing order; the first is the hero. | | `links` | Array of `{ "label": "Source", "url": "https://..." }` detail-page links, not s - Network accessline 59
| `links` | Array of `{ "label": "Source", "url": "https://..." }` detail-page links, not search-result URLs. | - Network accessline 80
"facts": ["12-month lease", "Covered parking"], "catches": ["Verify pet policy and total move-in fees"], "images": ["https://example.com/property/front.jpg", "https://example.com/property/kitchen.jpg"], - Network accessline 81
"catches": ["Verify pet policy and total move-in fees"], "images": ["https://example.com/property/front.jpg", "https://example.com/property/kitchen.jpg"], "links": [{"label": "Listing details", "url": "https://example.com/property/12"}] - Network accessline 82
"images": ["https://example.com/property/front.jpg", "https://example.com/property/kitchen.jpg"], - Network accessline 83
"links": [{"label": "Listing details", "url": "https://example.com/property/12"}]
optional-skills/productivity/shop/SKILL.md
- Network accessline 12
tags: [Shopping, E-commerce, Shop, Products, Orders, Returns, Checkout, Reorder] related_skills: [shopify, maps] homepage: https://shop.app - Network accessline 13
related_skills: [shopify, maps] homepage: https://shop.app upstream: https://shop.app/SKILL.md - Network accessline 14
homepage: https://shop.app - Network accessline 15
upstream: https://shop.app/SKILL.md - Dangerous shell operationline 28
To upgrade: `pnpm add --global @shopify/shop-cli@latest` (or `npm install --global @shopify/shop-cli@latest`). Uninstall: `pnpm rm -g @shopify/shop-cli` (or `npm rm -g @shopify/shop-cli`). - Network accessline 115
Example: > Of course! If you sign in to Shop, I can get shipping rates to your home and past order details. [Sign in here](https://accounts.shop.app/oauth/agents/device?user_code=OIJAOSIJ) and tell me when you're done. Or just say 'continue - Network accessline 116
> Of course! If you sign in to Shop, I can get shipping rates to your home and past order details. [Sign in here](https://accounts.shop.app/oauth/agents/device?user_code=OIJAOSIJ) and tell me when you're done. Or just say 'continue' and I'l - Network accessline 151
[View Product](https://store.com/product) - Network accessline 193
> Tip: if you'd like, you can give me a budget to spend on your behalf so I can complete checkouts without asking each time. Set a spending limit here: https://shop.app/account/settings/connections. Or, tell me *not interested*, and I'll re - Access to secretsline 209
[Credential-related source omitted]
optional-skills/productivity/shop/references/catalog-mcp.md
- Network accessline 8
POST https://catalog.shopify.com/api/ucp/mcp - Access to secretsline 17
[Credential-related source omitted] - Network accessline 20
POST https://shop.app/oauth/token - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Network accessline 43
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 44
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" } - Network accessline 45
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 66
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 67
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" } - Network accessline 68
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 180
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 181
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" } - Network accessline 182
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 209
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" - Network accessline 210
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json" } - Network accessline 211
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/valid-with-capabilities.json"
optional-skills/productivity/shop/references/direct-api.md
- Access to secretsline 9
[Credential-related source omitted] - Network accessline 21
POST https://accounts.shop.app/oauth/device - Network accessline 32
POST https://accounts.shop.app/oauth/token - Access to secretsline 40
[Credential-related source omitted] - Network accessline 45
GET https://accounts.shop.app/oauth/userinfo - Access to secretsline 46
[Credential-related source omitted] - Network accessline 52
POST https://accounts.shop.app/oauth/token - Network accessline 65
POST https://shop.app/oauth/token - Network accessline 69
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 70
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Network accessline 71
resource=https://{shop_domain}/ - Network accessline 80
POST https://{shop_domain}/api/ucp/mcp - Network accessline 91
GET https://api.ipify.org?format=json - Network accessline 106
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 107
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" } - Network accessline 108
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 174
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 175
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" }, - Network accessline 176
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 214
"meta": { "ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 215
"ucp-agent": { "profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" } - Network accessline 216
"profile": "https://shopify.dev/ucp/agent-profiles/2026-04-08/personal_agent.json" - Network accessline 230
When the buyer enables purchasing without approval in [Shop → Settings → Connections](https://shop.app/account/settings/connections), Shop issues a budgeted wallet payment token. Read the remaining budget: - Network accessline 233
GET https://shop.app/pay/agents/payment_tokens - Access to secretsline 234
[Credential-related source omitted] - Network accessline 264
GET https://shop.app/agents/orderSearch?type=recent - Network accessline 265
GET https://shop.app/agents/orderSearch?type=tracking&query=<string>&dateFrom=YYYY-MM-DD&dateTo=YYYY-MM-DD - Access to secretsline 266
[Credential-related source omitted]
optional-skills/productivity/shop/references/legal.md
- Network accessline 3
This skill is for **individual end-users** only. Building commercial services, resale platforms, aggregators, or anything that provides third parties with programmatic access to Shopify's catalog, checkout, delegated payments, or aggregated
optional-skills/productivity/shop/references/safety.md
- Access to secretsline 21
[Credential-related source omitted]
optional-skills/productivity/shopify/SKILL.md
- Access to secretsline 9
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 23
tags: [Shopify, E-commerce, Commerce, API, GraphQL] related_skills: [airtable, xurl] homepage: https://shopify.dev/docs/api/admin-graphql - Network accessline 24
related_skills: [airtable, xurl] homepage: https://shopify.dev/docs/api/admin-graphql --- - Network accessline 25
homepage: https://shopify.dev/docs/api/admin-graphql - Access to secretsline 41
[Credential-related source omitted] - Network accessline 59
- **Endpoint:** `https://$SHOPIFY_STORE_DOMAIN/admin/api/$SHOPIFY_API_VERSION/graphql.json` - Access to secretsline 60
[Credential-related source omitted] - Network accessline 66
Base curl pattern (reusable): - Network accessline 70
local query="$1" local variables="${2:-{}}" curl -sS -X POST \ - Network accessline 71
local variables="${2:-{}}" curl -sS -X POST \ "https://${SHOPIFY_STORE_DOMAIN}/admin/api/${SHOPIFY_API_VERSION:-2026-01}/graphql.json" \ - Network accessline 72
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Network accessline 73
"https://${SHOPIFY_STORE_DOMAIN}/admin/api/${SHOPIFY_API_VERSION:-2026-01}/graphql.json" \ - Network accessline 75
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Network accessline 76
--data "$(jq -nc --arg q "$query" --argjson v "$variables" '{query: $q, variables: $v}')" - Network accessline 107
Query syntax supports `title:`, `sku:`, `vendor:`, `product_type:`, `status:active`, `tag:`, `created_at:>2025-01-01`. Full grammar: https://shopify.dev/docs/api/usage/search-syntax - Network accessline 300
- **Endpoint:** `https://$SHOPIFY_STORE_DOMAIN/api/$SHOPIFY_API_VERSION/graphql.json` - Network accessline 305
[Credential-related source omitted] - Access to secretsline 305
[Credential-related source omitted] - Network accessline 306
"https://${SHOPIFY_STORE_DOMAIN}/api/${SHOPIFY_API_VERSION:-2026-01}/graphql.json" \ - Access to secretsline 308
[Credential-related source omitted] - Network accessline 331
# 3. When status=COMPLETED, download the JSONL file curl -sS "$URL" > products.jsonl - Network accessline 332
curl -sS "$URL" > products.jsonl - Network accessline 346
userErrors { field message } } }' '{"topic":"ORDERS_CREATE","sub":{"callbackUrl":"https://example.com/webhook","format":"JSON"}}' - Network accessline 348
}' '{"topic":"ORDERS_CREATE","sub":{"callbackUrl":"https://example.com/webhook","format":"JSON"}}' - Access to secretsline 354
[Credential-related source omitted]
optional-skills/productivity/siyuan/SKILL.md
- Network accessline 10
tags: [SiYuan, Notes, Knowledge Base, PKM, API] related_skills: [obsidian, notion] homepage: https://github.com/siyuan-note/siyuan - Network accessline 11
related_skills: [obsidian, notion] homepage: https://github.com/siyuan-note/siyuan prerequisites: - Network accessline 12
homepage: https://github.com/siyuan-note/siyuan - Access to secretsline 14
[Credential-related source omitted] - Network accessline 19
help: "Settings > About in SiYuan desktop app" - name: SIYUAN_URL prompt: SiYuan instance URL (default http://127.0.0.1:6806) - Network accessline 20
- name: SIYUAN_URL prompt: SiYuan instance URL (default http://127.0.0.1:6806) required_for: remote instances - Network accessline 21
prompt: SiYuan instance URL (default http://127.0.0.1:6806) - Network accessline 27
Use the [SiYuan](https://github.com/siyuan-note/siyuan) kernel API via curl to search, read, create, update, and delete blocks and documents in a self-hosted knowledge base. No extra tools needed -- just curl and an API token. - Network accessline 35
[Credential-related source omitted] - Network accessline 36
SIYUAN_URL=http://127.0.0.1:6806 - Network accessline 38
`SIYUAN_URL` defaults to `http://127.0.0.1:6806` if not set. - Network accessline 45
[Credential-related source omitted] - Access to secretsline 45
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[Credential-related source omitted] - Network accessline 295
[Credential-related source omitted] - Network accessline 297
SIYUAN_URL: "http://127.0.0.1:6806"
optional-skills/productivity/telephony/SKILL.md
- Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 114
[Credential-related source omitted] - Network accessline 151
Sign up at: - https://www.twilio.com/try-twilio - Network accessline 152
- https://www.twilio.com/try-twilio - Network accessline 188
Sign up at: - https://app.bland.ai - Network accessline 189
- https://app.bland.ai - Access to secretsline 194
[Credential-related source omitted] - Network accessline 199
Sign up at: - https://dashboard.vapi.ai - Network accessline 200
- https://dashboard.vapi.ai - Access to secretsline 205
[Credential-related source omitted] - Access to secretsline 217
[Credential-related source omitted] - Network accessline 264
python "$SCRIPT" twilio-send-sms "+15551230000" "Here is the chart." --media-url "https://example.com/chart.png" - Network accessline 301
python "$SCRIPT" twilio-call "+155****0000" --audio-url "https://example.com/briefing.mp3" - Network accessline 414
- Twilio phone numbers: https://www.twilio.com/docs/phone-numbers/api - Network accessline 415
- Twilio messaging: https://www.twilio.com/docs/messaging/api/message-resource - Network accessline 416
- Twilio voice: https://www.twilio.com/docs/voice/api/call-resource - Network accessline 417
- Vapi docs: https://docs.vapi.ai/ - Network accessline 418
- Bland.ai: https://app.bland.ai/
optional-skills/productivity/telephony/scripts/telephony.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 5
[Credential-related source omitted] - Network accessline 36
TWILIO_API_BASE = "https://api.twilio.com/2010-04-01/Accounts" - Network accessline 37
VAPI_API_BASE = "https://api.vapi.ai" - Network accessline 38
BLAND_API_BASE = "https://api.bland.ai/v1" - Access to secretsline 73
[Credential-related source omitted] - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 262
req = urllib.request.Request(url, data=body, headers=request_headers, method=method.upper()) - Network accessline 263
try: with urllib.request.urlopen(req, timeout=30) as resp: payload = resp.read().decode("utf-8") - Network accessline 264
with urllib.request.urlopen(req, timeout=30) as resp: - Access to secretsline 285
[Credential-related source omitted] - Access to secretsline 286
[Credential-related source omitted] - Access to secretsline 287
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[Credential-related source omitted] - Access to secretsline 432
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[Credential-related source omitted] - Access to secretsline 759
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[Credential-related source omitted] - Access to secretsline 822
[Credential-related source omitted] - Access to secretsline 849
[Credential-related source omitted] - Access to secretsline 850
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[Credential-related source omitted] - Access to secretsline 869
[Credential-related source omitted] - Access to secretsline 884
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[Credential-related source omitted] - Access to secretsline 887
[Credential-related source omitted] - Access to secretsline 929
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[Credential-related source omitted] - Access to secretsline 958
[Credential-related source omitted] - Access to secretsline 1011
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[Credential-related source omitted] - Access to secretsline 1082
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[Credential-related source omitted] - Access to secretsline 1104
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[Credential-related source omitted] - Access to secretsline 1115
[Credential-related source omitted] - Access to secretsline 1122
[Credential-related source omitted] - Access to secretsline 1130
[Credential-related source omitted] - Access to secretsline 1157
[Credential-related source omitted] - Access to secretsline 1159
[Credential-related source omitted] - Access to secretsline 1163
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[Credential-related source omitted] - Access to secretsline 1241
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[Credential-related source omitted] - Access to secretsline 1246
[Credential-related source omitted]
optional-skills/research/bioinformatics/SKILL.md
- Network accessline 22
◆ **bioSkills** — 385 reference skills (code patterns, parameter guides, decision trees) Repo: https://github.com/GPTomics/bioSkills - Network accessline 23
Repo: https://github.com/GPTomics/bioSkills - Network accessline 26
◆ **ClawBio** — 33 runnable pipeline skills (executable scripts, reproducibility bundles) Repo: https://github.com/ClawBio/ClawBio - Network accessline 27
Repo: https://github.com/ClawBio/ClawBio - Network accessline 35
# bioSkills (reference material) git clone --depth 1 https://github.com/GPTomics/bioSkills.git ~/.hermes/cache/scratch/bioSkills - Network accessline 36
git clone --depth 1 https://github.com/GPTomics/bioSkills.git ~/.hermes/cache/scratch/bioSkills - Network accessline 38
# ClawBio (runnable pipelines) git clone --depth 1 https://github.com/ClawBio/ClawBio.git ~/.hermes/cache/scratch/ClawBio - Network accessline 39
git clone --depth 1 https://github.com/ClawBio/ClawBio.git ~/.hermes/cache/scratch/ClawBio - Dangerous shell operationline 221
sudo apt install samtools bcftools ncbi-blast+ minimap2 bedtools
optional-skills/research/blogwatcher/SKILL.md
- Network accessline 9
hermes: tags: [RSS, Blogs, Feed-Reader, Monitoring] homepage: https://github.com/JulienTant/blogwatcher-cli - Network accessline 10
tags: [RSS, Blogs, Feed-Reader, Monitoring] homepage: https://github.com/JulienTant/blogwatcher-cli prerequisites: - Network accessline 11
homepage: https://github.com/JulienTant/blogwatcher-cli - Network accessline 34
- **Go:** `go install github.com/JulienTant/blogwatcher-cli/cmd/blogwatcher-cli@latest` - **Docker:** `docker run --rm -v blogwatcher-cli:/data ghcr.io/julientant/blogwatcher-cli` - **Binary (Linux amd64):** `curl -sL https://github.com/Jul - Network accessline 35
- **Docker:** `docker run --rm -v blogwatcher-cli:/data ghcr.io/julientant/blogwatcher-cli` - **Binary (Linux amd64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_amd64.tar.gz | ta - Network accessline 36
- **Binary (Linux amd64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_amd64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 37
- **Binary (Linux arm64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_arm64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 38
- **Binary (macOS Apple Silicon):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_darwin_arm64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 39
- **Binary (macOS Intel):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_darwin_amd64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 41
All releases: https://github.com/JulienTant/blogwatcher-cli/releases - Network accessline 69
- Add a blog: `blogwatcher-cli add "My Blog" https://example.com` - Network accessline 70
- Add with explicit feed: `blogwatcher-cli add "My Blog" https://example.com --feed-url https://example.com/feed.xml` - Network accessline 71
- Add with HTML scraping: `blogwatcher-cli add "My Blog" https://example.com --scrape-selector "article h2 a"` - Instruction override signalline 96
| `BLOGWATCHER_WORKERS` | Number of concurrent scan workers (default: 8) | | `BLOGWATCHER_SILENT` | Only output "scan done" when scanning | | `BLOGWATCHER_YES` | Skip confirmation prompts | - Instruction override signalline 97
| `BLOGWATCHER_SILENT` | Only output "scan done" when scanning | | `BLOGWATCHER_YES` | Skip confirmation prompts | | `BLOGWATCHER_CATEGORY` | Default filter for articles by category | - Instruction override signalline 98
| `BLOGWATCHER_YES` | Skip confirmation prompts | - Network accessline 107
xkcd URL: https://xkcd.com - Network accessline 108
URL: https://xkcd.com - Network accessline 109
Feed: https://xkcd.com/atom.xml - Network accessline 127
[1] [new] Barrel - Part 13 Blog: xkcd URL: https://xkcd.com/3095/ - Network accessline 128
Blog: xkcd URL: https://xkcd.com/3095/ Published: 2026-04-02 - Network accessline 129
URL: https://xkcd.com/3095/ - Network accessline 133
[2] [new] Volcano Fact Blog: xkcd URL: https://xkcd.com/3094/ - Network accessline 134
Blog: xkcd URL: https://xkcd.com/3094/ Published: 2026-04-01 - Network accessline 135
URL: https://xkcd.com/3094/
optional-skills/research/darwinian-evolver/SKILL.md
- Network accessline 16
Run Imbue's [darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) — an - Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 58
mkdir -p ~/.hermes/cache/darwinian-evolver && cd ~/.hermes/cache/darwinian-evolver [ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git - Network accessline 59
[ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Network accessline 195
- [Imbue research post](https://imbue.com/research/2026-02-27-darwinian-evolver/) - Network accessline 196
- [ARC-AGI-2 results](https://imbue.com/research/2026-02-27-arc-agi-2-evolution/) - Network accessline 197
- [imbue-ai/darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) (AGPL-3.0) - Network accessline 198
- [Darwin Gödel Machines](https://arxiv.org/abs/2505.22954) - Network accessline 199
- [PromptBreeder](https://arxiv.org/abs/2309.16797)
optional-skills/research/darwinian-evolver/scripts/parrot_openrouter.py
- Executable scriptline 1
Executable file - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Network accessline 40
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 42
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted]
optional-skills/research/darwinian-evolver/scripts/show_snapshot.py
- Executable scriptline 1
Executable file
optional-skills/research/darwinian-evolver/templates/custom_problem_template.py
- Executable scriptline 1
Executable file - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 48
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Network accessline 50
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted]
optional-skills/research/domain-intel/SKILL.md
- Network accessline 60
- **Use `web_search`** for general research about what a domain/company does - **Use `web_extract`** to get the actual content of a webpage - **Use `terminal` with `curl -I`** for a simple "is this URL reachable" check - Network accessline 62
- **Use `terminal` with `curl -I`** for a simple "is this URL reachable" check - Network accessline 67
| "Find info about a company" | `web_search` | General research, not domain-specific | | "Is this website safe?" | `web_search` | Reputation checks need web context | | "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP - Network accessline 68
| "Is this website safe?" | `web_search` | Reputation checks need web context | | "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP check | | "Find subdomains of X" | **This skill** | Only passive source for this | - Network accessline 69
| "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP check | - Network accessline 104
*Contributed by [@FurkanL0](https://github.com/FurkanL0)*
optional-skills/research/domain-intel/scripts/domain_intel.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 32
url = f"https://crt.sh/?q=%25.{urllib.parse.quote(domain)}&output=json" - Network accessline 33
req = urllib.request.Request(url, headers={ - Network accessline 34
"User-Agent": "domain-intel-skill/1.0", "Accept": "application/json", }) with urllib.request.urlopen(req, timeout=15) as r: - Network accessline 35
}) with urllib.request.urlopen(req, timeout=15) as r: entries = json.loads(r.read().decode()) - Network accessline 36
with urllib.request.urlopen(req, timeout=15) as r: - Network accessline 85
try: ctx = ssl.create_default_context() with socket.create_connection((host, port), timeout=timeout) as sock: - Network accessline 86
ctx = ssl.create_default_context() with socket.create_connection((host, port), timeout=timeout) as sock: with ctx.wrap_socket(sock, server_hostname=host) as s: - Network accessline 87
with socket.create_connection((host, port), timeout=timeout) as sock: - Network accessline 93
ctx.check_hostname = False ctx.verify_mode = ssl.CERT_NONE with socket.create_connection((host, port), timeout=timeout) as sock: - Network accessline 94
ctx.verify_mode = ssl.CERT_NONE with socket.create_connection((host, port), timeout=timeout) as sock: with ctx.wrap_socket(sock, server_hostname=host) as s: - Network accessline 95
with socket.create_connection((host, port), timeout=timeout) as sock: - Network accessline 162
try: with socket.create_connection((server, 43), timeout=10) as s: - Network accessline 163
with socket.create_connection((server, 43), timeout=10) as s: - Network accessline 231
records["AAAA"] = [] else: url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}" - Network accessline 232
else: url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}" try: - Network accessline 233
url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}" - Network accessline 234
try: req = urllib.request.Request(url, headers={"User-Agent": "domain-intel-skill/1.0"}) with urllib.request.urlopen(req, timeout=10) as r: - Network accessline 235
req = urllib.request.Request(url, headers={"User-Agent": "domain-intel-skill/1.0"}) - Network accessline 236
with urllib.request.urlopen(req, timeout=10) as r: - Network accessline 260
try: ns_url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type=NS" - Network accessline 261
ns_url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type=NS" - Network accessline 262
req = urllib.request.Request(ns_url, headers={"User-Agent": "domain-intel-skill/1.0"}) - Network accessline 263
with urllib.request.urlopen(req, timeout=10) as r: - Network accessline 276
ctx.check_hostname = False ctx.verify_mode = ssl.CERT_NONE with socket.create_connection((domain, 443), timeout=3) as s: - Network accessline 277
ctx.verify_mode = ssl.CERT_NONE with socket.create_connection((domain, 443), timeout=3) as s: with ctx.wrap_socket(s, server_hostname=domain): - Network accessline 278
with socket.create_connection((domain, 443), timeout=3) as s: - Network accessline 290
if server: try: with socket.create_connection((server, 43), timeout=10) as s: - Network accessline 291
try: with socket.create_connection((server, 43), timeout=10) as s: s.sendall((domain + "\r\n").encode()) - Network accessline 292
with socket.create_connection((server, 43), timeout=10) as s:
optional-skills/research/drug-discovery/SKILL.md
- Network accessline 30
TARGET="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET") curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \ - Network accessline 31
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET") curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \ | python -c " - Dangerous shell operationline 32
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" | python -c " - Network accessline 32
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \ - Network accessline 46
# Get bioactivity data for a ChEMBL target ID TARGET_ID="$1" # e.g. CHEMBL203 curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \ - Network accessline 47
TARGET_ID="$1" # e.g. CHEMBL203 curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \ | python -c " - Dangerous shell operationline 48
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" | python -c " - Network accessline 48
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \ - Network accessline 60
# Look up a specific molecule by ChEMBL ID MOL_ID="$1" # e.g. CHEMBL25 (aspirin) curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \ - Network accessline 61
MOL_ID="$1" # e.g. CHEMBL25 (aspirin) curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \ | python -c " - Dangerous shell operationline 62
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" | python -c " - Network accessline 62
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \ - Network accessline 85
COMPOUND="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND") curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBon - Network accessline 86
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND") curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount - Dangerous shell operationline 87
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA,InChIKey/JSON" | python -c " - Network accessline 87
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA,InChIKey/JSON" \ - Network accessline 116
DRUG="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG") curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" \ - Network accessline 117
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG") curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" \ | python -c " - Dangerous shell operationline 118
curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" | python -c " - Network accessline 118
curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" \ - Network accessline 137
DRUG="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG") curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\"${ENCODED}\"&count=patient.reaction.reactionmeddrap - Network accessline 138
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG") curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\"${ENCODED}\"&count=patient.reaction.reactionmeddrapt.exact&li - Dangerous shell operationline 139
curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\"${ENCODED}\"&count=patient.reaction.reactionmeddrapt.exact&limit=10" | python -c " - Network accessline 139
curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\"${ENCODED}\"&count=patient.reaction.reactionmeddrapt.exact&limit=10" \ - Network accessline 156
COMPOUND="$1" ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND") CID=$(curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/cids/TXT" | head -1 | tr -d '[:space:]') - Network accessline 157
ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND") CID=$(curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/cids/TXT" | head -1 | tr -d '[:space:]') echo "PubChem CID: - Network accessline 158
CID=$(curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/cids/TXT" | head -1 | tr -d '[:space:]') - Network accessline 159
echo "PubChem CID: $CID" curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/${CID}/property/IsomericSMILES,InChIKey,IUPACName/JSON" \ | python -c " - Dangerous shell operationline 160
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/${CID}/property/IsomericSMILES,InChIKey,IUPACName/JSON" | python -c " - Network accessline 160
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/${CID}/property/IsomericSMILES,InChIKey,IUPACName/JSON" \ - Network accessline 173
GENE="$1" curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \ - Dangerous shell operationline 174
curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" -H "Content-Type: application/json" -d "{\"query\":\"{ search(queryString: \\\"${GENE}\\\", entityNames: [\\\"target\\\"], page: {index: 0, size: 1}) { hits { id sc - Network accessline 174
curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
optional-skills/research/drug-discovery/references/ADMET_REFERENCE.md
optional-skills/research/drug-discovery/scripts/chembl_target.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 10
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 12
def get(endpoint): try: req = urllib.request.Request(f"{BASE}{endpoint}", headers={"Accept":"application/json"}) - Network accessline 13
try: req = urllib.request.Request(f"{BASE}{endpoint}", headers={"Accept":"application/json"}) with urllib.request.urlopen(req, timeout=15) as r: - Network accessline 14
req = urllib.request.Request(f"{BASE}{endpoint}", headers={"Accept":"application/json"}) - Network accessline 15
with urllib.request.urlopen(req, timeout=15) as r:
optional-skills/research/drug-discovery/scripts/ro5_screen.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 10
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name" - Network accessline 13
def fetch(name): - Network accessline 14
url = f"{BASE}/{urllib.parse.quote(name)}/property/{PROPS}/JSON" try: with urllib.request.urlopen(url, timeout=10) as r: - Network accessline 15
try: with urllib.request.urlopen(url, timeout=10) as r: return json.loads(r.read())["PropertyTable"]["Properties"][0] - Network accessline 16
with urllib.request.urlopen(url, timeout=10) as r: - Network accessline 35
passed = 0 for name in compounds: props = fetch(name.strip()) - Network accessline 36
for name in compounds: props = fetch(name.strip()) result = check(props) if props else None - Network accessline 37
props = fetch(name.strip())
optional-skills/research/duckduckgo-search/SKILL.md
- Access to secretsline 19
[Credential-related source omitted] - Network accessline 211
- **Rate limiting**: DuckDuckGo may throttle after many rapid requests. Add a short delay between searches if needed. - **No content extraction**: `ddgs` returns snippets, not full page content. Use `web_extract`, browser tools, or curl for - Network accessline 212
- **No content extraction**: `ddgs` returns snippets, not full page content. Use `web_extract`, browser tools, or curl for the full article/page.
optional-skills/research/duckduckgo-search/scripts/duckduckgo.sh
- Executable scriptline 1
#!/usr/bin/env bash
optional-skills/research/gitnexus-explorer/SKILL.md
- Network accessline 44
if [ ! -d "$GITNEXUS_DIR/gitnexus-web/dist" ]; then git clone https://github.com/abhigyanpatwari/GitNexus.git "$GITNEXUS_DIR" - Network accessline 45
git clone https://github.com/abhigyanpatwari/GitNexus.git "$GITNEXUS_DIR" - Network accessline 59
export const DEFAULT_BACKEND_URL = 'http://localhost:4747'; - Network accessline 63
export const DEFAULT_BACKEND_URL = typeof window !== 'undefined' && window.location.hostname !== 'localhost' ? window.location.origin : 'http://localhost:4747'; - Dangerous shell operationline 86
rm -rf .claude/ # remove Claude Code-specific artifacts - Network accessline 141
if (req.url.startsWith('/api')) proxyToApi(req, res); else serveStatic(req, res); }).listen(PORT, () => console.log(`GitNexus proxy on http://localhost:${PORT}`)); - Network accessline 143
}).listen(PORT, () => console.log(`GitNexus proxy on http://localhost:${PORT}`)); - Network accessline 156
Verify: `curl -s http://localhost:8888/api/repos` should return the indexed repo(s). - Network accessline 162
if ! command -v cloudflared &>/dev/null; then mkdir -p ~/.local/bin curl -sL https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 \ - Network accessline 163
mkdir -p ~/.local/bin curl -sL https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 \ -o ~/.local/bin/cloudflared - Network accessline 164
curl -sL https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 \ - Network accessline 170
# Start tunnel (--config /dev/null avoids conflicts with existing named tunnels) cloudflared tunnel --config /dev/null --url http://localhost:8888 --no-autoupdate --protocol http2 - Network accessline 171
cloudflared tunnel --config /dev/null --url http://localhost:8888 --no-autoupdate --protocol http2 - Network accessline 174
The tunnel URL (e.g., `https://random-words.trycloudflare.com`) is printed to stderr. - Dangerous shell operationline 188
rm -rf .claude/ - Dangerous shell operationline 203
then `rm -rf .claude/` for the rest. These are Claude Code integrations that
optional-skills/research/gitnexus-explorer/scripts/proxy.mjs
- Executable scriptline 1
Executable file - Network accessline 87
server.listen(PORT, () => { console.log(`GitNexus proxy listening on http://localhost:${PORT}`); - Network accessline 88
console.log(`GitNexus proxy listening on http://localhost:${PORT}`); - Network accessline 89
console.log(` Web UI: http://localhost:${PORT}/`); - Network accessline 90
console.log(` API: http://localhost:${PORT}/api/repos`); - Network accessline 91
console.log(` Backend: http://127.0.0.1:${API_PORT}`);
optional-skills/research/llm-wiki/SKILL.md
- Network accessline 17
Build and maintain a persistent, compounding knowledge base as interlinked markdown files. Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). - Network accessline 18
Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). - Network accessline 156
--- source_url: https://example.com/article # original URL, if applicable - Network accessline 157
source_url: https://example.com/article # original URL, if applicable - Dangerous shell operationline 471
sudo loginctl enable-linger $USER - Network accessline 502
[llm-wiki-compiler](https://github.com/atomicmemory/llm-wiki-compiler) is a Node.js CLI that
optional-skills/research/osint-investigation/SKILL.md
optional-skills/research/osint-investigation/references/sources/courtlistener.md
- Network accessline 11
- **REST API v4:** `https://www.courtlistener.com/api/rest/v4/` - Network accessline 16
Set `COURTLISTENER_TOKEN` env var. Get a free token at https://www.courtlistener.com/sign-in/ then create an API key. - Network accessline 17
https://www.courtlistener.com/sign-in/ then create an API key. - Network accessline 95
- API docs: https://www.courtlistener.com/help/api/rest/ - Network accessline 96
- Court IDs: https://www.courtlistener.com/api/jurisdictions/ - Network accessline 97
- RECAP archive: https://www.courtlistener.com/recap/ - Network accessline 98
- Bulk data: https://www.courtlistener.com/help/api/bulk-data/
optional-skills/research/osint-investigation/references/sources/gdelt.md
- Network accessline 15
- **DOC 2.0 API:** `https://api.gdeltproject.org/api/v2/doc/doc` - Network accessline 16
- **Events / GKG 2.0:** `https://api.gdeltproject.org/api/v2/events/events` - Network accessline 89
GDELT supports its own query operators: phrase quoting, AND/OR/NOT, `sourcecountry:US`, `theme:ECON_BANKRUPTCY`, `tone<-5`, etc. See https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/ for syntax. - Network accessline 91
See https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/ for syntax. - Network accessline 102
- DOC 2.0 API: https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/ - Network accessline 103
- Themes & query syntax: https://blog.gdeltproject.org/gkg-2-0-our-global-knowledge-graph-2-0-amazing-data-at-your-fingertips/ - Network accessline 104
- Project home: https://www.gdeltproject.org/
optional-skills/research/osint-investigation/references/sources/icij-offshore.md
- Network accessline 12
- **Bulk download (primary):** `https://offshoreleaks-data.icij.org/offshoreleaks/csv/full-oldb.LATEST.zip` (~70 MB ZIP, refreshed periodically) - Network accessline 13
- **Search UI (human):** `https://offshoreleaks.icij.org/` - Network accessline 101
- Database: https://offshoreleaks.icij.org/ - Network accessline 102
- About the data: https://offshoreleaks.icij.org/pages/about - Network accessline 103
- Methodology: https://www.icij.org/investigations/panama-papers/ - Network accessline 104
- API hints: Open Refine reconciliation endpoint at `https://offshoreleaks.icij.org/reconcile`
optional-skills/research/osint-investigation/references/sources/nyc-acris.md
- Network accessline 12
- **Socrata API:** `https://data.cityofnewyork.us/resource/636b-3b5g.json` (Parties) - Access to secretsline 14
[Credential-related source omitted] - Network accessline 87
- ACRIS portal: https://a836-acris.nyc.gov/CP/ - Network accessline 88
- NYC Open Data: https://data.cityofnewyork.us/ - Network accessline 89
- Parties dataset: https://data.cityofnewyork.us/City-Government/ACRIS-Real-Property-Parties/636b-3b5g - Network accessline 90
- Document type codes: https://www1.nyc.gov/site/finance/taxes/acris.page
optional-skills/research/osint-investigation/references/sources/ofac-sdn.md
- Network accessline 12
- **Full XML:** `https://www.treasury.gov/ofac/downloads/sdn.xml` - Network accessline 13
- **Delimited:** `https://www.treasury.gov/ofac/downloads/sdn.csv` - Network accessline 14
- **Consolidated:** `https://www.treasury.gov/ofac/downloads/consolidated/consolidated.xml` - Network accessline 89
- OFAC home: https://ofac.treasury.gov/ - Network accessline 90
- SDN list: https://ofac.treasury.gov/specially-designated-nationals-and-blocked-persons-list-sdn-human-readable-lists - Network accessline 91
- Data formats: https://ofac.treasury.gov/sdn-list/sanctions-list-search-tool - Network accessline 92
- Compliance guidance: https://ofac.treasury.gov/recent-actions
optional-skills/research/osint-investigation/references/sources/opencorporates.md
- Network accessline 12
- **REST API:** `https://api.opencorporates.com/v0.4/` - Network accessline 13
- **HTML fallback:** `https://opencorporates.com/companies?q=...` - **Auth:** API token required (free tier 500 calls/month, paid plans available) - **Rate limit:** Token-bound; un-tokened requests return 401 - Network accessline 17
Set `OPENCORPORATES_API_TOKEN` env var. Get a free token at https://opencorporates.com/api_accounts/new. - Network accessline 18
https://opencorporates.com/api_accounts/new. - Network accessline 101
- API docs: https://api.opencorporates.com/documentation/API-Reference - Network accessline 102
- Jurisdiction codes: https://api.opencorporates.com/v0.4/jurisdictions.json - Network accessline 103
- Schema: https://opencorporates.com/info/our_data
optional-skills/research/osint-investigation/references/sources/sec-edgar.md
- Network accessline 11
- **API:** `https://data.sec.gov/submissions/CIK<10-digit-padded>.json` (no auth) - Network accessline 12
- **Filing index:** `https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=...` - Network accessline 13
- **Full-text search:** `https://efts.sec.gov/LATEST/search-index?q=...` - **Auth:** None — requires `User-Agent` header with contact info per SEC policy - **Rate limit:** 10 requests/second per IP (enforced) - Network accessline 81
- Developer docs: https://www.sec.gov/edgar/sec-api-documentation - Network accessline 82
- EDGAR full-text search: https://efts.sec.gov/LATEST/search-index - Network accessline 83
- Fair access policy: https://www.sec.gov/os/accessing-edgar-data
optional-skills/research/osint-investigation/references/sources/senate-ld.md
- Network accessline 12
- **API:** `https://lda.senate.gov/api/v1/` (no auth required for read-only) - **Bulk download:** `https://lda.senate.gov/api/v1/filings/?format=csv` (paginated) - Network accessline 13
- **Bulk download:** `https://lda.senate.gov/api/v1/filings/?format=csv` (paginated) - Network accessline 14
- **Auth:** Token required for >120 req/hour — register at https://lda.senate.gov/api/auth/register/ - Network accessline 87
- API docs: https://lda.senate.gov/api/redoc/v1/ - Network accessline 88
- LDA guidance: https://lobbyingdisclosure.house.gov/ld_guidance.pdf - Network accessline 89
- Senate Public Records: https://lda.senate.gov/
optional-skills/research/osint-investigation/references/sources/usaspending.md
- Network accessline 11
- **API v2:** `https://api.usaspending.gov/api/v2/` (no auth, no key) - Network accessline 12
- **Bulk:** `https://files.usaspending.gov/` (CSV / Parquet by award type) - Network accessline 95
- API docs: https://api.usaspending.gov/ - Network accessline 96
- Data dictionary: https://www.usaspending.gov/data-dictionary - Network accessline 97
- Award schema: https://files.usaspending.gov/docs/Data_Dictionary_Crosswalk.xlsx
optional-skills/research/osint-investigation/references/sources/wayback.md
- Network accessline 11
- **CDX server:** `https://web.archive.org/cdx/search/cdx` - Network accessline 12
- **Wayback URL:** `https://web.archive.org/web/<timestamp>/<url>` - Network accessline 13
- **Save Page Now (write):** `https://web.archive.org/save/<url>` (different API) - Network accessline 62
# All captures of a specific URL python3 SKILL_DIR/scripts/fetch_wayback.py --url "https://example.com/page" \ - Network accessline 63
python3 SKILL_DIR/scripts/fetch_wayback.py --url "https://example.com/page" \ - Network accessline 91
- CDX server docs: https://github.com/internetarchive/wayback/blob/master/wayback-cdx-server/README.md - Network accessline 92
- Wayback API: https://archive.org/help/wayback_api.php - Network accessline 93
- Internet Archive: https://archive.org/
optional-skills/research/osint-investigation/references/sources/wikipedia.md
- Network accessline 15
- **Wikipedia OpenSearch:** `https://en.wikipedia.org/w/api.php?action=opensearch` - Network accessline 16
- **Wikipedia REST summary:** `https://en.wikipedia.org/api/rest_v1/page/summary/<title>` - Network accessline 17
- **Wikidata Action API:** `https://www.wikidata.org/w/api.php?action=wbgetentities` - Network accessline 18
- **Wikidata SPARQL:** `https://query.wikidata.org/sparql` (more powerful but aggressively rate-limited) - Network accessline 103
- Wikipedia OpenSearch: https://www.mediawiki.org/wiki/API:Opensearch - Network accessline 104
- Wikipedia REST: https://en.wikipedia.org/api/rest_v1/ - Network accessline 105
- Wikidata Action API: https://www.wikidata.org/wiki/Wikidata:Data_access - Network accessline 106
- Wikidata SPARQL: https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service - Network accessline 107
- User-Agent policy: https://meta.wikimedia.org/wiki/User-Agent_policy
optional-skills/research/osint-investigation/scripts/_http.py
- Executable scriptline 1
Executable file - Network accessline 14
DEFAULT_UA = ( "hermes-osint-investigation/0.2 " "(+https://github.com/NousResearch/hermes-agent; " - Network accessline 15
"hermes-osint-investigation/0.2 " "(+https://github.com/NousResearch/hermes-agent; " "set HERMES_OSINT_UA env var to identify yourself per " - Network accessline 16
"(+https://github.com/NousResearch/hermes-agent; " - Access to secretsline 41
[Credential-related source omitted] - Network accessline 45
last_err: Exception | None = None for attempt in range(max_retries + 1): req = urllib.request.Request(url, headers=h) - Network accessline 46
for attempt in range(max_retries + 1): req = urllib.request.Request(url, headers=h) try: - Network accessline 47
req = urllib.request.Request(url, headers=h) - Network accessline 48
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.read() - Network accessline 49
with urllib.request.urlopen(req, timeout=timeout) as resp:
optional-skills/research/osint-investigation/scripts/_normalize.py
- Executable scriptline 1
Executable file
optional-skills/research/osint-investigation/scripts/build_findings.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/research/osint-investigation/scripts/entity_resolution.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/research/osint-investigation/scripts/fetch_courtlistener.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
BASE = "https://www.courtlistener.com/api/rest/v4/search/" - Network accessline 46
def fetch( - Network accessline 93
"snippet": (r.get("snippet", "") or "").replace("\n", " ")[:500], "absolute_url": ( f"https://www.courtlistener.com{r.get('absolute_url', '')}" - Network accessline 94
"absolute_url": ( f"https://www.courtlistener.com{r.get('absolute_url', '')}" if r.get("absolute_url", "").startswith("/") - Network accessline 95
f"https://www.courtlistener.com{r.get('absolute_url', '')}" - Access to secretsline 130
[Credential-related source omitted] - Network accessline 132
p.add_argument("--out", required=True) a = p.parse_args() n = fetch( - Network accessline 133
a = p.parse_args() n = fetch( query=a.query, - Network accessline 134
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_gdelt.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
BASE = "https://api.gdeltproject.org/api/v2/doc/doc" - Network accessline 36
def fetch( - Network accessline 117
f"GDELT: 0 articles for query={query!r}. " "GDELT indexes ~2015→present. Try widening the timespan or " "checking the query syntax (https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/).", - Network accessline 118
"GDELT indexes ~2015→present. Try widening the timespan or " "checking the query syntax (https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/).", file=sys.stderr, - Network accessline 119
"checking the query syntax (https://blog.gdeltproject.org/gdelt-doc-2-0-api-debuts/).", - Network accessline 143
p.add_argument("--out", required=True) a = p.parse_args() n = fetch( - Network accessline 144
a = p.parse_args() n = fetch( query=a.query, - Network accessline 145
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_icij_offshore.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 4
The old reconcile endpoint (https://offshoreleaks.icij.org/reconcile) returns - Network accessline 8
https://offshoreleaks-data.icij.org/offshoreleaks/csv/full-oldb.LATEST.zip - Network accessline 30
BULK_URL = "https://offshoreleaks-data.icij.org/offshoreleaks/csv/full-oldb.LATEST.zip" - Access to secretsline 48
[Credential-related source omitted] - Network accessline 62
return zip_path print(f"Downloading ICIJ bulk database (~70 MB) to {zip_path}", file=sys.stderr) req = urllib.request.Request( - Network accessline 63
print(f"Downloading ICIJ bulk database (~70 MB) to {zip_path}", file=sys.stderr) req = urllib.request.Request( BULK_URL, - Network accessline 64
req = urllib.request.Request( - Network accessline 66
headers={"User-Agent": "hermes-agent osint-investigation skill"}, ) with urllib.request.urlopen(req, timeout=120) as resp: - Network accessline 67
) with urllib.request.urlopen(req, timeout=120) as resp: tmp = zip_path.with_suffix(".zip.tmp") - Network accessline 68
with urllib.request.urlopen(req, timeout=120) as resp: - Network accessline 99
def fetch( - Network accessline 165
"source": row.get("sourceID", "") or row.get("source", "") or "", "entity_url": ( f"https://offshoreleaks.icij.org/nodes/{node_id}" if node_id else "" - Network accessline 166
"entity_url": ( f"https://offshoreleaks.icij.org/nodes/{node_id}" if node_id else "" ), - Network accessline 167
f"https://offshoreleaks.icij.org/nodes/{node_id}" if node_id else "" - Network accessline 218
if not (a.entity or a.officer or a.jurisdiction): p.error("must supply at least one of --entity / --officer / --jurisdiction") n = fetch( - Network accessline 219
p.error("must supply at least one of --entity / --officer / --jurisdiction") n = fetch( entity=a.entity, - Network accessline 220
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_nyc_acris.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 26
PARTIES_URL = "https://data.cityofnewyork.us/resource/636b-3b5g.json" - Network accessline 27
MASTER_URL = "https://data.cityofnewyork.us/resource/bnx9-e6tj.json" - Network accessline 65
return "" return ( f"https://a836-acris.nyc.gov/DS/DocumentSearch/DocumentImageView?doc_id={document_id}" - Network accessline 66
return ( f"https://a836-acris.nyc.gov/DS/DocumentSearch/DocumentImageView?doc_id={document_id}" ) - Network accessline 67
f"https://a836-acris.nyc.gov/DS/DocumentSearch/DocumentImageView?doc_id={document_id}" - Network accessline 71
def fetch( - Network accessline 188
p.add_argument("--out", required=True) a = p.parse_args() n = fetch( - Network accessline 189
a = p.parse_args() n = fetch( name=a.name, - Network accessline 190
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_ofac_sdn.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 4
Public endpoint: https://www.treasury.gov/ofac/downloads/sdn.csv - Network accessline 5
Format reference: https://ofac.treasury.gov/specially-designated-nationals-and-blocked-persons-list-sdn-human-readable-lists - Network accessline 24
SDN_URL = "https://www.treasury.gov/ofac/downloads/sdn.csv" - Network accessline 25
ADD_URL = "https://www.treasury.gov/ofac/downloads/add.csv" - Network accessline 26
ALT_URL = "https://www.treasury.gov/ofac/downloads/alt.csv" - Network accessline 85
def fetch( - Network accessline 167
p.add_argument("--out", required=True) a = p.parse_args() n = fetch(program=a.program, entity_type=a.entity_type, out_path=a.out) - Network accessline 168
a = p.parse_args() n = fetch(program=a.program, entity_type=a.entity_type, out_path=a.out) print(f"Wrote {n} OFAC SDN rows to {a.out}") - Network accessline 169
n = fetch(program=a.program, entity_type=a.entity_type, out_path=a.out)
optional-skills/research/osint-investigation/scripts/fetch_opencorporates.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 24
API_URL = "https://api.opencorporates.com/v0.4/companies/search" - Network accessline 25
HTML_URL = "https://opencorporates.com/companies" - Network accessline 78
{ "name": (m.group("name") or "").strip(), "opencorporates_url": f"https://opencorporates.com{url_path}", - Network accessline 79
"name": (m.group("name") or "").strip(), "opencorporates_url": f"https://opencorporates.com{url_path}", "jurisdiction_code": (m.group("jur") or "").strip(), - Network accessline 80
"opencorporates_url": f"https://opencorporates.com{url_path}", - Network accessline 89
def fetch( - Network accessline 108
print( "OPENCORPORATES_API_TOKEN not set — using HTML fallback (limited fields). " "Get a free token at https://opencorporates.com/api_accounts/new", - Network accessline 109
"OPENCORPORATES_API_TOKEN not set — using HTML fallback (limited fields). " "Get a free token at https://opencorporates.com/api_accounts/new", file=sys.stderr, - Network accessline 110
"Get a free token at https://opencorporates.com/api_accounts/new", - Access to secretsline 176
[Credential-related source omitted] - Network accessline 177
p.add_argument("--out", required=True) a = p.parse_args() n = fetch( - Network accessline 178
a = p.parse_args() n = fetch( query=a.query, - Network accessline 179
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_sec_edgar.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 7
Filings JSON is published at: https://data.sec.gov/submissions/CIK<10-digit-padded>.json - Network accessline 8
https://data.sec.gov/submissions/CIK<10-digit-padded>.json - Network accessline 10
Company lookup uses: https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&company=<name>&output=atom - Network accessline 11
https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&company=<name>&output=atom - Network accessline 25
SUBMISSIONS_URL = "https://data.sec.gov/submissions/CIK{cik}.json" - Access to secretsline 39
[Credential-related source omitted] - Network accessline 55
url = "https://www.sec.gov/cgi-bin/browse-edgar" - Network accessline 67
def fetch( - Network accessline 122
acc_nodash = acc.replace("-", "") filing_url = ( f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{acc_nodash}/{pdoc}" - Network accessline 123
filing_url = ( f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{acc_nodash}/{pdoc}" if acc and pdoc - Network accessline 124
f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{acc_nodash}/{pdoc}" - Network accessline 176
a = p.parse_args() types = [t for t in (a.types or "").split(",") if t.strip()] n = fetch(cik=a.cik, company=a.company, types=types, since=a.since, out_path=a.out) - Network accessline 177
types = [t for t in (a.types or "").split(",") if t.strip()] n = fetch(cik=a.cik, company=a.company, types=types, since=a.since, out_path=a.out) print(f"Wrote {n} EDGAR filing rows to {a.out}") - Network accessline 178
n = fetch(cik=a.cik, company=a.company, types=types, since=a.since, out_path=a.out)
optional-skills/research/osint-investigation/scripts/fetch_senate_ld.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 18
ENDPOINT = "https://lda.senate.gov/api/v1/filings/" - Network accessline 38
def fetch( - Access to secretsline 127
[Credential-related source omitted] - Network accessline 131
if not (a.client or a.registrant): p.error("must supply at least one of --client / --registrant") n = fetch( - Network accessline 132
p.error("must supply at least one of --client / --registrant") n = fetch( client=a.client, - Network accessline 133
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_usaspending.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 16
ENDPOINT = "https://api.usaspending.gov/api/v2/search/spending_by_award/" - Network accessline 58
def _post(body: dict) -> dict: req = urllib.request.Request( - Network accessline 59
req = urllib.request.Request( - Network accessline 63
method="POST", ) with urllib.request.urlopen(req, timeout=60) as resp: - Network accessline 64
) with urllib.request.urlopen(req, timeout=60) as resp: return json.loads(resp.read().decode("utf-8")) - Network accessline 65
with urllib.request.urlopen(req, timeout=60) as resp: - Network accessline 69
def fetch( - Network accessline 155
if not (a.recipient or a.agency): p.error("must supply at least one of --recipient / --agency") n = fetch( - Network accessline 156
p.error("must supply at least one of --recipient / --agency") n = fetch( recipient=a.recipient, - Network accessline 157
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_wayback.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 19
BASE = "https://web.archive.org/cdx/search/cdx" - Network accessline 32
def fetch( - Network accessline 78
"url": orig, "timestamp": ts, "wayback_url": f"https://web.archive.org/web/{ts}/{orig}" if ts and orig else "", - Network accessline 79
"timestamp": ts, "wayback_url": f"https://web.archive.org/web/{ts}/{orig}" if ts and orig else "", "mimetype": entry[idx["mimetype"]] if "mimetype" in idx else "", - Network accessline 80
"wayback_url": f"https://web.archive.org/web/{ts}/{orig}" if ts and orig else "", - Network accessline 124
p.add_argument("--out", required=True) a = p.parse_args() n = fetch( - Network accessline 125
a = p.parse_args() n = fetch( url_or_host=a.url, - Network accessline 126
n = fetch(
optional-skills/research/osint-investigation/scripts/fetch_wikipedia.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 23
WP_OPENSEARCH = "https://en.wikipedia.org/w/api.php" - Network accessline 24
WP_SUMMARY = "https://en.wikipedia.org/api/rest_v1/page/summary/" - Network accessline 25
WD_ACTION = "https://www.wikidata.org/w/api.php" - Network accessline 203
def fetch(query: str, limit: int, no_wikidata: bool, out_path: str) -> int: - Network accessline 221
"wikipedia_title": title, "wikipedia_url": hit.get("url", ""), "wikidata_url": f"https://www.wikidata.org/wiki/{qid}" if qid else "", - Network accessline 222
"wikipedia_url": hit.get("url", ""), "wikidata_url": f"https://www.wikidata.org/wiki/{qid}" if qid else "", "instance_of": "; ".join(facts.get("instance_of", [])), - Network accessline 223
"wikidata_url": f"https://www.wikidata.org/wiki/{qid}" if qid else "", - Network accessline 258
p.add_argument("--out", required=True) a = p.parse_args() n = fetch(query=a.query, limit=a.limit, no_wikidata=a.no_wikidata, out_path=a.out) - Network accessline 259
a = p.parse_args() n = fetch(query=a.query, limit=a.limit, no_wikidata=a.no_wikidata, out_path=a.out) print(f"Wrote {n} Wikipedia/Wikidata rows to {a.out}") - Network accessline 260
n = fetch(query=a.query, limit=a.limit, no_wikidata=a.no_wikidata, out_path=a.out)
optional-skills/research/osint-investigation/scripts/timing_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/research/parallel-cli/SKILL.md
- Dangerous shell operationline 67
curl -fsSL https://parallel.ai/install.sh | bash - Network accessline 67
curl -fsSL https://parallel.ai/install.sh | bash - Access to secretsline 94
[Credential-related source omitted] - Network accessline 184
parallel-cli extract https://example.com --json - Network accessline 185
parallel-cli extract https://company.com --objective "Find pricing info" --json parallel-cli extract https://example.com --full-content --json parallel-cli fetch https://example.com --json - Network accessline 186
parallel-cli extract https://example.com --full-content --json - Network accessline 187
parallel-cli fetch https://example.com --json - Access to secretsline 363
[Credential-related source omitted]
optional-skills/research/qmd/SKILL.md
- Network accessline 21
Created by [Tobi Lütke](https://github.com/tobi/qmd). MIT licensed. - Network accessline 43
# Linux — use NodeSource or nvm curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - - Dangerous shell operationline 44
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - - Network accessline 44
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - - Dangerous shell operationline 45
sudo apt-get install -y nodejs - Network accessline 254
# Runs on http://localhost:8181 by default - Network accessline 260
mcp_servers: qmd: url: "http://localhost:8181/mcp" - Network accessline 261
qmd: url: "http://localhost:8181/mcp" timeout: 30 - Network accessline 262
url: "http://localhost:8181/mcp" - Network accessline 275
<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> - Network accessline 276
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> <plist version="1.0"> - Network accessline 277
"http://www.apple.com/DTDs/PropertyList-1.0.dtd"> - Network accessline 440
- [GitHub: tobi/qmd](https://github.com/tobi/qmd) - Network accessline 441
- [QMD Changelog](https://github.com/tobi/qmd/blob/main/CHANGELOG.md)
optional-skills/research/research-paper-writing/SKILL.md
- Network accessline 258
# Via web_extract (for specific papers): web_extract("https://arxiv.org/abs/2303.17651") - Network accessline 259
web_extract("https://arxiv.org/abs/2303.17651") - Network accessline 274
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp" - Network accessline 328
def doi_to_bibtex(doi: str) -> str: response = requests.get( - Network accessline 329
response = requests.get( - Network accessline 330
f"https://doi.org/{doi}", - Network accessline 968
- [ ] Self-citations written in third person: "Smith et al. [1] showed..." not "We previously showed [1]..." - [ ] No GitHub/GitLab URLs pointing to your personal repos - [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code - Network accessline 969
- [ ] No GitHub/GitLab URLs pointing to your personal repos - [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code links - [ ] No institutional logos or identifiers in figures - Network accessline 970
- [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code links - Dangerous shell operationline 1045
rm -f *.aux *.bbl *.blg *.log *.out *.pdf - Network accessline 1220
# Use Anonymous GitHub for double-blind review # https://anonymous.4open.science/ - Network accessline 1221
# https://anonymous.4open.science/ - Network accessline 1393
| **`read_file`** / **`write_file`** / **`patch`** | Paper editing, experiment scripts, result files. Use `patch` for targeted edits to large .tex files. | | **`web_search`** | Literature discovery: `web_search("transformer attention mechan - Network accessline 1394
| **`web_search`** | Literature discovery: `web_search("transformer attention mechanism 2024")` | | **`web_extract`** | Fetch paper content, verify citations: `web_extract("https://arxiv.org/abs/2303.17651")` | | **`delegate_task`** | **Par - Network accessline 1395
| **`web_extract`** | Fetch paper content, verify citations: `web_extract("https://arxiv.org/abs/2303.17651")` | - Network accessline 1439
doi = paper.externalIds.get('DOI', 'N/A') if doi != 'N/A': bibtex = requests.get(f"https://doi.org/{doi}", - Network accessline 1440
if doi != 'N/A': bibtex = requests.get(f"https://doi.org/{doi}", headers={"Accept": "application/x-bibtex"}).text - Network accessline 1441
bibtex = requests.get(f"https://doi.org/{doi}", - Network accessline 1618
**Writing Philosophy:** - [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Network accessline 1619
- [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Network accessline 1620
- [Sebastian Farquhar: How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) - Network accessline 1621
- [Gopen & Swan: Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) - Network accessline 1622
- [Lipton: Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) - Network accessline 1623
- [Perez: Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) - Network accessline 1625
**APIs:** [Semantic Scholar](https://api.semanticscholar.org/api-docs/) | [CrossRef](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | [arXiv](https://info.arxiv.org/help/api/basics.html) - Network accessline 1627
**Venues:** [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | [ICML](https://icml.cc/Conferences/2025/AuthorInstructions) | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | [ACL](https://github.com/acl-org/a
optional-skills/research/research-paper-writing/references/autoreason-methodology.md
- Network accessline 5
**Source**: [NousResearch/autoreason](https://github.com/NousResearch/autoreason) — "Autoreason: When Iterative LLM Refinement Works and Why It Fails"
optional-skills/research/research-paper-writing/references/citation-workflow.md
- Network accessline 124
# Check CrossRef (via DOI) if doi: resp = requests.get(f"https://api.crossref.org/works/{doi}") - Network accessline 125
if doi: resp = requests.get(f"https://api.crossref.org/works/{doi}") if resp.status_code == 200: - Network accessline 126
resp = requests.get(f"https://api.crossref.org/works/{doi}") - Network accessline 130
# Check arXiv if arxiv_id: resp = requests.get( - Network accessline 131
if arxiv_id: resp = requests.get( f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 132
resp = requests.get( - Network accessline 133
f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 148
def doi_to_bibtex(doi: str) -> str: """Get verified BibTeX from DOI via CrossRef content negotiation.""" response = requests.get( - Network accessline 149
"""Get verified BibTeX from DOI via CrossRef content negotiation.""" response = requests.get( f"https://doi.org/{doi}", - Network accessline 150
response = requests.get( - Network accessline 151
f"https://doi.org/{doi}", - Access to secretsline 249
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 284
if paper.doi: try: resp = requests.get( - Network accessline 285
try: resp = requests.get( f"https://api.crossref.org/works/{paper.doi}", - Network accessline 286
resp = requests.get( - Network accessline 287
f"https://api.crossref.org/works/{paper.doi}", - Network accessline 296
if paper.arxiv_id: try: resp = requests.get( - Network accessline 297
try: resp = requests.get( f"http://export.arxiv.org/api/query?id_list={paper.arxiv_id}", - Network accessline 298
resp = requests.get( - Network accessline 299
f"http://export.arxiv.org/api/query?id_list={paper.arxiv_id}", - Network accessline 311
if paper.doi: try: resp = requests.get( - Network accessline 312
try: resp = requests.get( f"https://doi.org/{paper.doi}", - Network accessline 313
resp = requests.get( - Network accessline 314
f"https://doi.org/{paper.doi}", - Network accessline 551
**APIs:** - Semantic Scholar: https://api.semanticscholar.org/api-docs/ - Network accessline 552
- Semantic Scholar: https://api.semanticscholar.org/api-docs/ - Network accessline 553
- CrossRef: https://www.crossref.org/documentation/retrieve-metadata/rest-api/ - Network accessline 554
- arXiv: https://info.arxiv.org/help/api/basics.html - Network accessline 555
- OpenAlex: https://docs.openalex.org/ - Network accessline 557
**Python Libraries:** - `semanticscholar`: https://pypi.org/project/semanticscholar/ - Network accessline 558
- `semanticscholar`: https://pypi.org/project/semanticscholar/ - Network accessline 559
- `arxiv`: https://pypi.org/project/arxiv/ - Network accessline 560
- `habanero` (CrossRef): https://github.com/sckott/habanero - Network accessline 562
**Verification Tools:** - Citely: https://citely.ai/citation-checker - Network accessline 563
- Citely: https://citely.ai/citation-checker - Network accessline 564
- ReciteWorks: https://reciteworks.com/
optional-skills/research/research-paper-writing/references/phase5-paper-drafting.md
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This skill synthesizes writing philosophy from researchers who have published extensively at top venues. The writing philosophy layer was originally compiled by [Orchestra Research](https://github.com/orchestra-research) as the `ml-paper-wr - Network accessline 52
| Source | Key Contribution | Link | |--------|-----------------|------| | **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLp - Network accessline 53
|--------|-----------------|------| | **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how- - Network accessline 54
| **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) | - Network accessline 55
| **Sebastian Farquhar** (DeepMind) | 5-sentence abstract formula | [How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) | - Network accessline 56
| **Gopen & Swan** | 7 principles of reader expectations | [Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | - Network accessline 57
| **Zachary Lipton** | Word choice, eliminating hedging | [Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) | - Network accessline 58
| **Jacob Steinhardt** (UC Berkeley) | Precision, consistent terminology | [Writing Tips](https://bounded-regret.ghost.io/) | - Network accessline 59
| **Ethan Perez** (Anthropic) | Micro-level clarity tips | [Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) | - Dangerous shell operationline 713
# Linux: sudo apt install latexdiff
optional-skills/research/research-paper-writing/references/sources.md
- Network accessline 9
The writing philosophy, citation verification workflow, and conference reference materials in this skill were originally compiled by **[Orchestra Research](https://github.com/orchestra-research)** as the `ml-paper-writing` skill (January 20 - Network accessline 17
| Source | Author | URL | Key Contribution | |--------|--------|-----|------------------| | **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/high - Network accessline 18
|--------|--------|-----|------------------| | **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-pape - Network accessline 19
| **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) | Narrative framework, "What/Why/So What" - Network accessline 20
| **How to Write ML Papers** | Sebastian Farquhar (DeepMind) | [Blog](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) | 5-sentence abstract formula, structure templates | - Network accessline 21
| **A Survival Guide to a PhD** | Andrej Karpathy | [Blog](http://karpathy.github.io/2016/09/07/phd/) | Paper structure recipe, contribution framing | - Network accessline 22
| **Heuristics for Scientific Writing** | Zachary Lipton (CMU) | [Blog](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) | Word choice, section balance, intensifier warni - Network accessline 23
| **Advice for Authors** | Jacob Steinhardt (UC Berkeley) | [Blog](https://jsteinhardt.stat.berkeley.edu/blog/advice-for-authors) | Precision over brevity, consistent terminology | - Network accessline 24
| **Easy Paper Writing Tips** | Ethan Perez (Anthropic) | [Blog](https://ethanperez.net/easy-paper-writing-tips/) | Micro-level tips, apostrophe unfolding, clarity tricks | - Network accessline 28
| Source | Author | URL | Key Contribution | |--------|--------|-----|------------------| | **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress position - Network accessline 29
|--------|--------|-----|------------------| | **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress positions, old-before-new, 7 principles | | **Summary - Network accessline 30
| **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress positions, old-before-new, 7 principles | - Network accessline 31
| **Summary of Science of Scientific Writing** | Lawrence Crowl | [Summary](https://www.crowl.org/Lawrence/writing/GopenSwan90.html) | Condensed version of Gopen & Swan | - Network accessline 35
| Source | URL | Key Contribution | |--------|-----|------------------| | How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) | - Network accessline 36
|--------|-----|------------------| | How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) | Practical walkthrough, LaTeX tips | - Network accessline 37
| How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) | Practical walkthrough, LaTeX tips | - Network accessline 38
| A Recipe for Training Neural Networks | [Karpathy Blog](http://karpathy.github.io/2019/04/25/recipe/) | Debugging methodology that translates to paper structure | - Network accessline 39
| ICML Paper Writing Best Practices | [ICML](https://icml.cc/Conferences/2022/BestPractices) | Official venue guidance | - Network accessline 40
| Bill Freeman's Writing Slides | [MIT](https://billf.mit.edu/sites/default/files/documents/cvprPapers.pdf) | Visual guide to paper structure | - Network accessline 48
| Document | URL | Purpose | |----------|-----|---------| | Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist | - Network accessline 49
|----------|-----|---------| | Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist | | Reviewer Guidelines 2025 | [NeurIPS](https://neurips.cc/Conferences/2025/ReviewerGuidel - Network accessline 50
| Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist | - Network accessline 51
| Reviewer Guidelines 2025 | [NeurIPS](https://neurips.cc/Conferences/2025/ReviewerGuidelines) | Evaluation criteria, scoring | - Network accessline 52
| Style Files | [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | LaTeX templates | - Network accessline 56
| Document | URL | Purpose | |----------|-----|---------| | Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements | - Network accessline 57
|----------|-----|---------| | Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements | | Reviewer Instructions 2025 | [ICML](https://icml.cc/Conferences/2025/ReviewerInstructions) | Review for - Network accessline 58
| Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements | - Network accessline 59
| Reviewer Instructions 2025 | [ICML](https://icml.cc/Conferences/2025/ReviewerInstructions) | Review form, evaluation | - Network accessline 60
| Style & Author Instructions | [ICML](https://icml.cc/Conferences/2022/StyleAuthorInstructions) | Formatting specifications | - Network accessline 64
| Document | URL | Purpose | |----------|-----|---------| | Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure | - Network accessline 65
|----------|-----|---------| | Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure | | Reviewer Guide 2025 | [ICLR](https://iclr.cc/Conferences/2025/ReviewerGuide) | Review proc - Network accessline 66
| Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure | - Network accessline 67
| Reviewer Guide 2025 | [ICLR](https://iclr.cc/Conferences/2025/ReviewerGuide) | Review process, evaluation | - Network accessline 71
| Document | URL | Purpose | |----------|-----|---------| | ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates | - Network accessline 72
|----------|-----|---------| | ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates | | ACL Rolling Review | [ARR](https://aclrollingreview.org/) | Submission process | - Network accessline 73
| ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates | - Network accessline 74
| ACL Rolling Review | [ARR](https://aclrollingreview.org/) | Submission process | - Network accessline 78
| Document | URL | Purpose | |----------|-----|---------| | Author Kit 2026 | [AAAI](https://aaai.org/authorkit26/) | Templates and guidelines | - Network accessline 80
| Author Kit 2026 | [AAAI](https://aaai.org/authorkit26/) | Templates and guidelines | - Network accessline 84
| Document | URL | Purpose | |----------|-----|---------| | Template | [GitHub](https://github.com/COLM-org/Template) | LaTeX templates | - Network accessline 86
| Template | [GitHub](https://github.com/COLM-org/Template) | LaTeX templates | - Network accessline 94
| API | Documentation | Best For | |-----|---------------|----------| | **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs | - Network accessline 95
|-----|---------------|----------| | **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs | | **CrossRef** | [Docs](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | DO - Network accessline 96
| **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs | - Network accessline 97
| **CrossRef** | [Docs](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | DOI lookup, BibTeX retrieval | - Network accessline 98
| **arXiv** | [Docs](https://info.arxiv.org/help/api/basics.html) | Preprints, PDF access | - Network accessline 99
| **OpenAlex** | [Docs](https://docs.openalex.org/) | Open alternative, bulk access | - Network accessline 111
| Tool | URL | Purpose | |------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | - Network accessline 112
|------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | | ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking | - Network accessline 113
| Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | - Network accessline 114
| ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking | - Network accessline 122
| Tool | URL | Purpose | |------|-----|---------| | PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams | - Network accessline 123
|------|-----|---------| | PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams | | SciencePlots | [GitHub](https://github.com/garrettj403/SciencePlots) | Publication-ready matplotlib | - Network accessline 124
| PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams | - Network accessline 125
| SciencePlots | [GitHub](https://github.com/garrettj403/SciencePlots) | Publication-ready matplotlib | - Network accessline 126
| Okabe-Ito Palette | [Reference](https://jfly.uni-koeln.de/color/) | Colorblind-safe colors | - Network accessline 130
| Resource | URL | Purpose | |----------|-----|---------| | Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor | - Network accessline 131
|----------|-----|---------| | Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor | | BibLaTeX Guide | [CTAN](https://ctan.org/pkg/biblatex) | Modern citation management | - Network accessline 132
| Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor | - Network accessline 133
| BibLaTeX Guide | [CTAN](https://ctan.org/pkg/biblatex) | Modern citation management | - Network accessline 139
| Source | URL | Key Finding | |--------|-----|-------------| | AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate | - Network accessline 140
|--------|-----|-------------| | AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate | | Hallucination in AI Writing | [PMC](https://pmc.ncbi.nlm.nih.gov/articles/P - Network accessline 141
| AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate | - Network accessline 142
| Hallucination in AI Writing | [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC10726751/) | Types of citation errors | - Network accessline 143
| NeurIPS 2025 AI Report | [ByteIota](https://byteiota.com/neurips-2025-100-ai-hallucinations-slip-through-review/) | 100+ hallucinated citations | - Network accessline 177
| Source | URL | Key Contribution | |--------|-----|------------------| | **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | - Network accessline 178
|--------|-----|------------------| | **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | | **Model Cards for Model Reporting** | Mitchell et al., 2019 - Network accessline 179
| **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | - Network accessline 180
| **Model Cards for Model Reporting** | Mitchell et al., 2019 ([arXiv](https://arxiv.org/abs/1810.03993)) | Structured model documentation framework | - Network accessline 181
| **Crowdsourcing and Human Computation** | [Survey](https://arxiv.org/abs/2202.06516) | Best practices for crowdsourced annotation | - Network accessline 182
| **Krippendorff's Alpha** | [Wikipedia](https://en.wikipedia.org/wiki/Krippendorff%27s_alpha) | Inter-annotator agreement metric reference | - Network accessline 183
| **Prolific** | [prolific.co](https://www.prolific.co/) | Recommended crowdsourcing platform for research | - Network accessline 187
| Source | URL | Key Contribution | |--------|-----|------------------| | **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | - Network accessline 188
|--------|-----|------------------| | **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | | **NeurIPS Broader Impact Guide** | [NeurIPS](https://neurips.cc/public/guides/PaperCheckl - Network accessline 189
| **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | - Network accessline 190
| **NeurIPS Broader Impact Guide** | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | Official guidance on impact statements | - Network accessline 191
| **ACL Ethics Policy** | [ACL](https://www.aclweb.org/portal/content/acl-code-ethics) | Ethics requirements for NLP research |
optional-skills/research/research-paper-writing/templates/README.md
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**Setup:** 1. Install [TeX Live](https://www.tug.org/texlive/) (full distribution recommended) - Network accessline 12
1. Install [TeX Live](https://www.tug.org/texlive/) (full distribution recommended) - Network accessline 13
- macOS: `brew install --cask mactex` - Ubuntu: `sudo apt install texlive-full` - Windows: Download from [tug.org/texlive](https://www.tug.org/texlive/) - Dangerous shell operationline 14
- Ubuntu: `sudo apt install texlive-full` - Network accessline 15
- Windows: Download from [tug.org/texlive](https://www.tug.org/texlive/) - Network accessline 62
1. Go to [overleaf.com](https://www.overleaf.com) - Network accessline 108
| Conference | Directory | Year | Source | |------------|-----------|------|--------| | ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) | - Network accessline 109
|------------|-----------|------|--------| | ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) | | ICLR | `iclr2026/` | 2026 | [Official GitHub](https://github.com/ICLR/Master-Template) | - Network accessline 110
| ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) | - Network accessline 111
| ICLR | `iclr2026/` | 2026 | [Official GitHub](https://github.com/ICLR/Master-Template) | - Network accessline 112
| NeurIPS | `neurips2025/` | 2025 | Community template | | ACL | `acl/` | 2025+ | [Official ACL](https://github.com/acl-org/acl-style-files) | | AAAI | `aaai2026/` | 2026 | [AAAI Author Kit](https://aaai.org/authorkit26/) | - Network accessline 113
| ACL | `acl/` | 2025+ | [Official ACL](https://github.com/acl-org/acl-style-files) | - Network accessline 114
| AAAI | `aaai2026/` | 2026 | [AAAI Author Kit](https://aaai.org/authorkit26/) | - Network accessline 115
| COLM | `colm2025/` | 2025 | [Official COLM](https://github.com/COLM-org/Template) | - Network accessline 246
- ICML: https://icml.cc/ - Network accessline 247
- ICLR: https://iclr.cc/ - Network accessline 248
- NeurIPS: https://neurips.cc/ - Network accessline 249
- ACL: https://github.com/acl-org/acl-style-files - Network accessline 250
- AAAI: https://aaai.org/ - Network accessline 251
- COLM: https://colmweb.org/
optional-skills/research/research-paper-writing/templates/aaai2026/README.md
optional-skills/research/research-paper-writing/templates/aaai2026/aaai2026-unified-supp.tex
optional-skills/research/research-paper-writing/templates/aaai2026/aaai2026-unified-template.tex
- Dangerous shell operationline 121
Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\ - Dangerous shell operationline 132
\textsuperscript{\rm 1}Association for the Advancement of Artificial Intelligence\\ - Dangerous shell operationline 137
% Sunil Issar\textsuperscript{\rm 2}, - Dangerous shell operationline 138
% J. Scott Penberthy\textsuperscript{\rm 3}, - Dangerous shell operationline 139
% George Ferguson\textsuperscript{\rm 4}, - Dangerous shell operationline 140
% Hans Guesgen\textsuperscript{\rm 5} - Dangerous shell operationline 169
First Author Name\textsuperscript{\rm 1}, - Dangerous shell operationline 170
Second Author Name\textsuperscript{\rm 2}, - Dangerous shell operationline 171
Third Author Name\textsuperscript{\rm 1} - Dangerous shell operationline 175
\textsuperscript{\rm 1}Affiliation 1\\ - Dangerous shell operationline 176
\textsuperscript{\rm 2}Affiliation 2\\ - Network accessline 198
% NOTE: For anonymous submissions, do not include links that could reveal your identity % \begin{links} % \link{Code}{https://aaai.org/example/code} - Network accessline 199
% \begin{links} % \link{Code}{https://aaai.org/example/code} % \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 200
% \link{Code}{https://aaai.org/example/code} - Network accessline 201
% \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 202
% \link{Extended version}{https://aaai.org/example/extended-version} - Network accessline 206
% You must keep this block between (not within) the abstract and the main body of the paper. \begin{links} \link{Code}{https://aaai.org/example/code} - Network accessline 207
\begin{links} \link{Code}{https://aaai.org/example/code} \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 208
\link{Code}{https://aaai.org/example/code} - Network accessline 209
\link{Datasets}{https://aaai.org/example/datasets} - Network accessline 210
\link{Extended version}{https://aaai.org/example/extended-version} - Network accessline 405
\begin{scriptsize}\begin{verbatim} \begin{links} \link{Code}{https://aaai.org/example/guidelines} - Network accessline 406
\begin{links} \link{Code}{https://aaai.org/example/guidelines} \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 407
\link{Code}{https://aaai.org/example/guidelines} - Network accessline 408
\link{Datasets}{https://aaai.org/example/datasets} - Network accessline 409
\link{Extended version}{https://aaai.org/example} - Network accessline 525
\subsubsection{Obtaining Type 1 Computer Modern for \LaTeX{}.} If you use Computer Modern for the mathematics in your paper (you cannot use it for the text) you may need to download type 1 Computer fonts. They are available without charge f - Network accessline 527
http://www.ams.org/tex/type1-fonts.html. - Network accessline 532
\subsection{Title and Authors} Your title must appear centered over both text columns in sixteen-point bold type (twenty-four point leading). The title must be written in Title Case capitalization rules (not sentence case). The rules are a - Network accessline 533
Your title must appear centered over both text columns in sixteen-point bold type (twenty-four point leading). The title must be written in Title Case capitalization rules (not sentence case). The rules are a bit involved, but in general ve - Dangerous shell operationline 551
\noindent For authors from different institutions, use \textbackslash textsuperscript \{\textbackslash rm x \} to match authors and affiliations. Notice that there should not be any spaces between the author name (or comma following it) and - Dangerous shell operationline 555
AuthorOne\equalcontrib\textsuperscript{\rm 1,\rm 2}, - Dangerous shell operationline 556
AuthorTwo\equalcontrib\textsuperscript{\rm 2}, - Dangerous shell operationline 557
AuthorThree\textsuperscript{\rm 3},\\ - Dangerous shell operationline 558
AuthorFour\textsuperscript{\rm 4}, - Dangerous shell operationline 559
AuthorFive \textsuperscript{\rm 5}} - Dangerous shell operationline 562
\textsuperscript{\rm 1}AffiliationOne,\\ - Dangerous shell operationline 563
\textsuperscript{\rm 2}AffiliationTwo,\\ - Dangerous shell operationline 564
\textsuperscript{\rm 3}AffiliationThree,\\ - Dangerous shell operationline 565
\textsuperscript{\rm 4}AffiliationFour,\\ - Dangerous shell operationline 566
\textsuperscript{\rm 5}AffiliationFive\\ - Network accessline 849
@misc(key, [...] howpublished="\url{http://...}", - Network accessline 850
[...] howpublished="\url{http://...}", note="Accessed: YYYY-mm-dd", - Network accessline 851
howpublished="\url{http://...}", - Network accessline 859
\vspace{.2em} For the most up to date version of the AAAI reference style, please consult the \textit{AI Magazine} Author Guidelines at \url{https://aaai.org/ojs/index.php/aimagazine/about/submissions#authorGuidelines} - Network accessline 860
For the most up to date version of the AAAI reference style, please consult the \textit{AI Magazine} Author Guidelines at \url{https://aaai.org/ojs/index.php/aimagazine/about/submissions#authorGuidelines}
optional-skills/research/research-paper-writing/templates/aaai2026/aaai2026.bib
- Network accessline 38
publisher = {American Association for the Advancement of Science}, issn = {0036-8075}, URL = {https://science.sciencemag.org/content/208/4447/1019}, - Network accessline 39
issn = {0036-8075}, URL = {https://science.sciencemag.org/content/208/4447/1019}, eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf}, - Network accessline 40
URL = {https://science.sciencemag.org/content/208/4447/1019}, - Network accessline 41
eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf}, - Network accessline 58
year = {1984}, issn = {0020-7373}, doi = {https://doi.org/10.1016/S0020-7373(84)80003-6}, - Network accessline 59
issn = {0020-7373}, doi = {https://doi.org/10.1016/S0020-7373(84)80003-6}, url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036}, - Network accessline 60
doi = {https://doi.org/10.1016/S0020-7373(84)80003-6}, - Network accessline 61
url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036}, - Network accessline 106
title = "Pluto: The 'Other' Red Planet", author = "{NASA}", howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}", - Network accessline 107
author = "{NASA}", howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}", year = 2015, - Network accessline 108
howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",
optional-skills/research/research-paper-writing/templates/aaai2026/aaai2026.sty
- Network accessline 241
\@ifpackageloaded{bbm}% {% \PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with- - Network accessline 242
{% \PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with-type-1-fonts for possible al - Network accessline 243
\PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with-type-1-fonts for possible alternative
optional-skills/research/research-paper-writing/templates/acl/README.md
- Network accessline 12
- as an [Overleaf template](https://www.overleaf.com/latex/templates/association-for-computational-linguistics-acl-conference/jvxskxpnznfj) - Network accessline 14
- as a [.zip file](https://github.com/acl-org/acl-style-files/archive/refs/heads/master.zip) - Network accessline 16
Please see [`acl_latex.tex`](https://github.com/acl-org/acl-style-files/blob/master/acl_latex.tex) for an example. - Network accessline 21
- [Paper formatting guidelines](https://acl-org.github.io/ACLPUB/formatting.html) - Network accessline 46
- git pull from github to a local repository - then, git push from your local repository to overleaf project - Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549 - Network accessline 47
- then, git push from your local repository to overleaf project - Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549 - Overleaf git url is https://git.overleaf.com/5f64f1fb97c4c50001b60549 - Network accessline 48
- Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549 - Network accessline 49
- Overleaf git url is https://git.overleaf.com/5f64f1fb97c4c50001b60549
optional-skills/research/research-paper-writing/templates/acl/acl.sty
- Network accessline 2
% The official sources can be found at % % https://github.com/acl-org/acl-style-files/ - Network accessline 3
% % https://github.com/acl-org/acl-style-files/ % - Network accessline 4
% https://github.com/acl-org/acl-style-files/ - Network accessline 35
\ifacl@linenumbers % Add draft line numbering via the lineno package % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/ - Network accessline 36
% Add draft line numbering via the lineno package % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/ \usepackage[switch,mathlines]{lineno} - Network accessline 37
% https://texblog.org/2012/02/08/adding-line-numbers-to-documents/ - Network accessline 61
% Patch amsmath commands so that the previous line and the equation itself % are numbered. Bug: multline has an extra line number. % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align - Network accessline 62
% are numbered. Bug: multline has an extra line number. % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align \usepackage{etoolbox} %% <- for \pretocmd, \apptocmd and \patchcmd - Network accessline 63
% https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align
optional-skills/research/research-paper-writing/templates/acl/acl_latex.tex
- Network accessline 13
% For Vietnamese characters % \usepackage[T5]{fontenc} % See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets - Network accessline 15
% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets - Network accessline 117
These instructions are for authors submitting papers to *ACL conferences using \LaTeX. They are not self-contained. All authors must follow the general instructions for *ACL proceedings,\footnote{\url{http://acl-org.github.io/ACLPUB/formatt - Network accessline 298
You can obtain the complete ACL Anthology as a Bib\TeX{} file from \url{https://aclweb.org/anthology/anthology.bib.gz}.
optional-skills/research/research-paper-writing/templates/acl/acl_natbib.bst
- Network accessline 1
%%% Modification of BibTeX style file acl_natbib_nourl.bst %%% ... by urlbst, version 0.9.1 (marked with "% urlbst") %%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst> - Network accessline 2
%%% ... by urlbst, version 0.9.1 (marked with "% urlbst") %%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst> %%% Modifications Copyright 2002–23, Norman Gray, - Network accessline 3
%%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst> - Network accessline 125
"online" 'onlinestring := % label that a resource is online "[link]" 'linktextstring := % anonymous link text "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED - Network accessline 126
"[link]" 'linktextstring := % anonymous link text "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED "https://doi.org/" 'doiurl := % prefix to make URL from DOI - Network accessline 127
"http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED - Network accessline 128
"https://doi.org/" 'doiurl := % prefix to make URL from DOI - Network accessline 129
"doi:" 'doiprefix := % printed text to introduce DOI "https://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref "cited " 'citedstring := % label in "lastchecked" remark - Network accessline 130
"https://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref - Network accessline 1191
{ doi empty$ { "" } { doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack - Network accessline 1192
{ "" } { doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack 's := 't := - Network accessline 1193
{ doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack - Network accessline 1231
% then don't print the URL (thanks to Joseph Wright % for (the original version of) this code, % at http://tex.stackexchange.com/questions/5660) - Network accessline 1232
% for (the original version of) this code, % at http://tex.stackexchange.com/questions/5660) adddoi - Network accessline 1233
% at http://tex.stackexchange.com/questions/5660) - Network accessline 1287
% author, note, year, month, and lastchecked fields optional % See references % ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm - Network accessline 1288
% See references % ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm % http://www.classroom.net/classroom/CitingNetResources.html - Network accessline 1289
% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm - Network accessline 1290
% http://www.classroom.net/classroom/CitingNetResources.html - Network accessline 1291
% http://neal.ctstateu.edu/history/cite.html - Network accessline 1292
% http://www.cas.usf.edu/english/walker/mla.html
optional-skills/research/research-paper-writing/templates/acl/anthology.bib.txt
- Network accessline 10
For example, https://aclanthology.org/N04-1035 can be cited as \cite{galley-etal-2004-whats}. - Network accessline 12
The consolidated file can be downloaded from here: - https://aclanthology.org/anthology.bib - Network accessline 13
- https://aclanthology.org/anthology.bib - Network accessline 19
There are currently (2025) two files: - https://aclanthology.org/anthology-1.bib - Network accessline 20
- https://aclanthology.org/anthology-1.bib - Network accessline 21
- https://aclanthology.org/anthology-2.bib
optional-skills/research/research-paper-writing/templates/acl/custom.bib
optional-skills/research/research-paper-writing/templates/acl/formatting.md
- Network accessline 10
> https://acl-org.github.io/ACLPUB/ - Network accessline 132
Long titles should be typed on two lines without a blank line intervening. Put the title 2.5 cm from the top of the page. Write the title in [title case](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case); do not w - Network accessline 134
Write the title in [title case](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case); do not write the title in all capital letters, except for acronyms (e.g., "BLEU") or proper nouns ("English") that are normally up - Network accessline 252
Provide as complete a citation as possible, using a consistent format, such as the [one for Computational Linguistics](http://cljournal.org/style_guide_refs.html) or the one in the [Publication Manual of the American Psychological Associati - Network accessline 258
All references are required to contain DOIs of all cited works when possible, or, as a second resort, links to ACL Anthology pages. Appropriate records should be found for most materials in the current [ACL Anthology](https://aclweb.org/ant - Network accessline 259
Appropriate records should be found for most materials in the current [ACL Anthology](https://aclweb.org/anthology/). - Network accessline 263
> Rie Kubota Ando and Tong Zhang. 2005. [A framework for learning predictive structures from multiple tasks and unlabeled data](https://www.jmlr.org/papers/v6/ando05a.html). *Journal of Machine Learning Research*, 6:1817–1853. - Network accessline 267
> Galen Andrew and Jianfeng Gao. 2007. [Scalable training of L1-regularized log-linear models](https://doi.org/10.1145/1273496.1273501). In *Proceedings of the 24th International Conference on Machine Learning*, pages 33–40. - Network accessline 271
> James Goodman, Andreas Vlachos, and Jason Naradowsky. 2016. [Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing](http://dx.doi.org/10.18653/v1/P16-1001). In *Proceedings of the 54th - Network accessline 275
> Benjamin Börschinger and Mark Johnson. 2011. [A particle filter algorithm for Bayesian word segmentation](https://www.aclweb.org/anthology/U11-1004/). In *Proceedings of the Australasian Language Technology Association Workshop 2011*, pag - Network accessline 279
> Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015. [Yara parser: A fast and accurate dependency parser](http://arxiv.org/abs/1503.06733). *Computing Research Repository*, arXiv:1503.06733. Version 2.
optional-skills/research/research-paper-writing/templates/colm2025/colm2025_conference.bib
- Network accessline 5
publisher = {Curran Associates, Inc.}, title = {Attention is All you Need}, url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}, - Network accessline 6
title = {Attention is All you Need}, url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}, volume = {30}, - Network accessline 7
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},
optional-skills/research/research-paper-writing/templates/colm2025/colm2025_conference.tex
- Network accessline 71
COLM requires electronic submissions, processed by \url{https://openreview.net/}. See COLM's website for more instructions. - Network accessline 72
\url{https://openreview.net/}. See COLM's website for more instructions. - Network accessline 102
The style files for COLM and other conference information are available online at: \begin{center} \url{http://www.colmweb.org/} - Network accessline 103
\begin{center} \url{http://www.colmweb.org/} \end{center} - Network accessline 104
\url{http://www.colmweb.org/} - Network accessline 143
We strongly recommend following arXiv's guidelines for making your paper friendly for HTML conversion: \url{https://info.arxiv.org/help/submit_latex_best_practices.html}. - Network accessline 277
\end{verbatim} for .pdf graphics. See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps}) - Network accessline 279
See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})
optional-skills/research/research-paper-writing/templates/colm2025/math_commands.tex
- Dangerous shell operationline 82
% rm is already a command, just don't name any random variables m
optional-skills/research/research-paper-writing/templates/iclr2026/iclr2026_conference.tex
- Network accessline 5
% Optional math commands from https://github.com/goodfeli/dlbook_notation. - Network accessline 65
ICLR requires electronic submissions, processed by \url{https://openreview.net/}. See ICLR's website for more instructions. - Network accessline 66
\url{https://openreview.net/}. See ICLR's website for more instructions. - Network accessline 93
The style files for ICLR and other conference information are available online at: \begin{center} \url{http://www.iclr.cc/} - Network accessline 94
\begin{center} \url{http://www.iclr.cc/} \end{center} - Network accessline 95
\url{http://www.iclr.cc/} - Network accessline 230
notation file from the textbook, \textit{Deep Learning} \cite{goodfellow2016deep} available at \url{https://github.com/goodfeli/dlbook_notation/}. Use of this style - Network accessline 231
\cite{goodfellow2016deep} available at \url{https://github.com/goodfeli/dlbook_notation/}. Use of this style is not required and can be disabled by commenting out - Network accessline 232
\url{https://github.com/goodfeli/dlbook_notation/}. Use of this style - Network accessline 390
\end{verbatim} for .pdf graphics. See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps}) - Network accessline 392
See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})
optional-skills/research/research-paper-writing/templates/iclr2026/math_commands.tex
- Dangerous shell operationline 82
% rm is already a command, just don't name any random variables m
optional-skills/research/research-paper-writing/templates/icml2026/example_paper.tex
- Network accessline 136
are available on the conference web site at: \begin{center} \texttt{http://icml.cc/} - Network accessline 137
\begin{center} \texttt{http://icml.cc/} \end{center} - Network accessline 138
\texttt{http://icml.cc/} - Network accessline 572
for everyone including people with disabilities and sensory or neurological differences. Tips of how to achieve this and what to pay attention to will be provided on the conference website \url{http://icml.cc/}. - Network accessline 574
provided on the conference website \url{http://icml.cc/}.
optional-skills/research/research-paper-writing/templates/icml2026/fancyhdr.sty
optional-skills/research/research-paper-writing/templates/neurips2025/Makefile
optional-skills/research/rss-feeds/SKILL.md
- Network accessline 41
python3 scripts/feed.py read https://hnrss.org/frontpage --limit 10 - Network accessline 42
python3 scripts/feed.py read https://simonwillison.net/ # page URL → discovers the feed - Network accessline 44
python3 scripts/feed.py discover https://example.com/ # list candidate feed URLs - Network accessline 49
| Source | Feed URL pattern | |---|---| | GitHub releases / commits / tags | `https://github.com/OWNER/REPO/releases.atom`, `…/commits/BRANCH.atom`, `…/tags.atom` | - Network accessline 50
|---|---| | GitHub releases / commits / tags | `https://github.com/OWNER/REPO/releases.atom`, `…/commits/BRANCH.atom`, `…/tags.atom` | | Subreddit / Reddit search | `https://www.reddit.com/r/NAME/.rss`, `https://www.reddit.com/search.rss?q= - Network accessline 51
| GitHub releases / commits / tags | `https://github.com/OWNER/REPO/releases.atom`, `…/commits/BRANCH.atom`, `…/tags.atom` | - Network accessline 52
| Subreddit / Reddit search | `https://www.reddit.com/r/NAME/.rss`, `https://www.reddit.com/search.rss?q=…` (1 req/min anon; see `reddit-reading`) | - Network accessline 53
| YouTube channel | `https://www.youtube.com/feeds/videos.xml?channel_id=UC…` | - Network accessline 54
| Hacker News | `https://hnrss.org/frontpage`, `https://hnrss.org/newest?q=TERM` | - Network accessline 55
| arXiv category | `https://rss.arxiv.org/rss/cs.CL` | - Network accessline 94
`python3 scripts/feed.py read https://github.com/NousResearch/hermes-agent/releases.atom - Network accessline 96
`discover https://simonwillison.net/` prints an `/atom/` URL.
optional-skills/research/rss-feeds/scripts/feed.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 7
python3 feed.py read https://example.com/feed.xml [--limit N] [--since 2026-09-01] - Network accessline 8
python3 feed.py discover https://example.com/ - Network accessline 9
python3 feed.py read https://example.com/ -> discovers, then reads the first feed - Network accessline 26
USER_AGENT = "hermes-agent/1.0 (rss-feeds skill; +https://github.com/NousResearch/hermes-agent)" - Network accessline 27
TIMEOUT = 20 NS = { "atom": "http://www.w3.org/2005/Atom", - Network accessline 28
NS = { "atom": "http://www.w3.org/2005/Atom", "dc": "http://purl.org/dc/elements/1.1/", - Network accessline 29
"atom": "http://www.w3.org/2005/Atom", - Network accessline 30
"dc": "http://purl.org/dc/elements/1.1/", - Network accessline 31
"content": "http://purl.org/rss/1.0/modules/content/", - Network accessline 32
"media": "http://search.yahoo.com/mrss/", - Network accessline 40
def fetch(url: str) -> tuple[bytes, str]: - Network accessline 41
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT, "Accept": "*/*"}) - Network accessline 42
with urllib.request.urlopen(req, timeout=TIMEOUT) as resp: - Network accessline 107
raise ValueError(f"unrecognised XML root <{tag}>") entries = [] for item in channel.iter("item") if tag == "rss" else root.iter("{http://purl.org/rss/1.0/}item"): - Network accessline 108
entries = [] for item in channel.iter("item") if tag == "rss" else root.iter("{http://purl.org/rss/1.0/}item"): entries.append({ - Network accessline 109
for item in channel.iter("item") if tag == "rss" else root.iter("{http://purl.org/rss/1.0/}item"): - Network accessline 110
entries.append({ "title": strip_html(_text(item, "title", "{http://purl.org/rss/1.0/}title")), "link": (_text(item, "link", "{http://purl.org/rss/1.0/}link") or "").strip() or None, - Network accessline 111
"title": strip_html(_text(item, "title", "{http://purl.org/rss/1.0/}title")), - Network accessline 112
"link": (_text(item, "link", "{http://purl.org/rss/1.0/}link") or "").strip() or None, - Network accessline 113
"published": parse_date(_text(item, "pubDate", "dc:date")), "author": strip_html(_text(item, "dc:creator", "author")), "summary": strip_html(_text(item, "content:encoded", "description", "{http://purl.org/rss/1.0/}de - Network accessline 114
"author": strip_html(_text(item, "dc:creator", "author")), "summary": strip_html(_text(item, "content:encoded", "description", "{http://purl.org/rss/1.0/}description"))[:2000], }) - Network accessline 115
"summary": strip_html(_text(item, "content:encoded", "description", "{http://purl.org/rss/1.0/}description"))[:2000], - Network accessline 117
return {"format": "rss", "title": strip_html(_text(channel, "title", "{http://purl.org/rss/1.0/}title")), "entries": entries} - Network accessline 144
if page_html is None: page_html, _ = fetch(page_url) - Network accessline 145
page_html, _ = fetch(page_url) - Network accessline 172
def read(url: str) -> dict: data, ctype = fetch(url) - Network accessline 173
data, ctype = fetch(url) - Network accessline 180
for cand in candidates: try: cdata, cctype = fetch(cand) - Network accessline 181
try: cdata, cctype = fetch(cand) except (urllib.error.URLError, OSError) as exc: - Network accessline 182
cdata, cctype = fetch(cand)
optional-skills/research/scrapling/SKILL.md
- Network accessline 10
tags: [Web Scraping, Browser, Cloudflare, Stealth, Crawling, Spider] related_skills: [duckduckgo-search, domain-intel] homepage: https://github.com/D4Vinci/Scrapling - Network accessline 11
related_skills: [duckduckgo-search, domain-intel] homepage: https://github.com/D4Vinci/Scrapling prerequisites: - Network accessline 12
homepage: https://github.com/D4Vinci/Scrapling - Network accessline 19
[Scrapling](https://github.com/D4Vinci/Scrapling) is a web scraping framework with anti-bot bypass, stealth browser automation, and a spider framework. It provides three fetching strategies (HTTP, dynamic JS, stealth/Cloudflare) and a full - Network accessline 63
scrapling extract get 'https://example.com' output.md - Network accessline 69
scrapling extract get 'https://example.com' output.md \ - Network accessline 77
scrapling extract fetch 'https://example.com' output.md \ - Network accessline 86
scrapling extract stealthy-fetch 'https://protected-site.com' output.html \ - Network accessline 95
scrapling extract post 'https://example.com/api' output.json \ - Network accessline 114
page = Fetcher.get('https://quotes.toscrape.com/') - Network accessline 125
with FetcherSession(impersonate='chrome') as session: page = session.get('https://example.com/', stealthy_headers=True) - Network accessline 126
page = session.get('https://example.com/', stealthy_headers=True) - Network accessline 136
page = Fetcher.post('https://api.example.com/data', json={"key": "value"}) - Network accessline 137
page = Fetcher.put('https://api.example.com/item/1', data={"name": "updated"}) - Network accessline 138
page = Fetcher.delete('https://api.example.com/item/1') - Network accessline 144
page = Fetcher.get('https://example.com', proxy='http://user:pass@proxy:8080') - Network accessline 154
page = DynamicFetcher.fetch('https://example.com', headless=True) - Network accessline 161
page = DynamicFetcher.fetch( - Network accessline 162
'https://example.com', - Network accessline 175
with DynamicSession(headless=True, disable_resources=True, network_idle=True) as session: page = session.fetch('https://example.com') - Network accessline 176
page = session.fetch('https://example.com') - Network accessline 192
page = DynamicFetcher.fetch('https://example.com', page_action=scroll_and_click) - Network accessline 203
page = StealthyFetcher.fetch( - Network accessline 204
'https://protected-site.com', - Network accessline 218
with StealthySession(headless=True, solve_cloudflare=True) as session: page1 = session.fetch('https://protected-site.com/page1') - Network accessline 219
page1 = session.fetch('https://protected-site.com/page1') - Network accessline 220
page2 = session.fetch('https://protected-site.com/page2') - Network accessline 276
class QuotesSpider(Spider): name = "quotes" start_urls = ["https://quotes.toscrape.com/"] - Network accessline 277
name = "quotes" start_urls = ["https://quotes.toscrape.com/"] concurrent_requests = 10 - Network accessline 278
start_urls = ["https://quotes.toscrape.com/"] - Network accessline 306
class SmartSpider(Spider): name = "smart" start_urls = ["https://example.com/"] - Network accessline 308
start_urls = ["https://example.com/"]
optional-skills/research/searxng-search/SKILL.md
- Network accessline 17
Free meta-search using [SearXNG](https://searxng.org/) — a privacy-respecting, self-hosted search aggregator that queries 70+ search engines simultaneously. - Access to secretsline 19
[Credential-related source omitted] - Network accessline 26
# Public instances (no setup required) SEARXNG_URL=https://searxng.example.com - Network accessline 27
SEARXNG_URL=https://searxng.example.com - Network accessline 29
# Self-hosted SearXNG SEARXNG_URL=http://localhost:8888 - Network accessline 30
SEARXNG_URL=http://localhost:8888 - Network accessline 40
# Check if SEARXNG_URL is set and the instance is reachable curl -s --max-time 5 "${SEARXNG_URL}/search?q=test&format=json" | head -c 200 - Network accessline 41
curl -s --max-time 5 "${SEARXNG_URL}/search?q=test&format=json" | head -c 200 - Network accessline 49
## Method 1: CLI via curl (Preferred) - Network accessline 54
# Text search (JSON output) curl -s --max-time 10 \ - Network accessline 55
curl -s --max-time 10 \ - Network accessline 58
# With Safesearch off curl -s --max-time 10 \ - Network accessline 59
curl -s --max-time 10 \ - Network accessline 62
# Specific categories (general, news, science, etc.) curl -s --max-time 10 \ - Network accessline 63
curl -s --max-time 10 \ - Network accessline 82
# Extract titles and URLs from JSON curl -s --max-time 10 "${SEARXNG_URL}/search?q=fastapi&format=json&limit=5" \ - Dangerous shell operationline 83
curl -s --max-time 10 "${SEARXNG_URL}/search?q=fastapi&format=json&limit=5" | python3 -c " - Network accessline 83
curl -s --max-time 10 "${SEARXNG_URL}/search?q=fastapi&format=json&limit=5" \ - Access to secretsline 104
[Credential-related source omitted] - Network accessline 116
resp = requests.get(f"{base_url}/search", params=params, timeout=10) - Network accessline 137
# Then set SEARXNG_URL=http://localhost:8888 - Network accessline 138
SEARXNG_URL=http://localhost:8888 - Network accessline 148
Public SearXNG instances are available at: - `https://searxng.example.com` (replace with any public instance) - Network accessline 149
- `https://searxng.example.com` (replace with any public instance) - Network accessline 156
# Search for relevant pages curl -s "${SEARXNG_URL}/search?q=fastapi+deployment&format=json&limit=3" - Network accessline 157
curl -s "${SEARXNG_URL}/search?q=fastapi+deployment&format=json&limit=3" - Network accessline 195
Public instances are listed at: https://searxng.org/
optional-skills/research/searxng-search/scripts/searxng.sh
optional-skills/security/1password/SKILL.md
- Network accessline 11
category: security setup: help: "Create a service account at https://my.1password.com → Settings → Service Accounts" - Network accessline 12
setup: help: "Create a service account at https://my.1password.com → Settings → Service Accounts" collect_secrets: - Network accessline 13
help: "Create a service account at https://my.1password.com → Settings → Service Accounts" - Network accessline 15
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Network accessline 16
[Credential-related source omitted] - Network accessline 17
provider_url: "https://developer.1password.com/docs/service-accounts/" - Access to secretsline 48
[Credential-related source omitted] - Network accessline 61
export OP_CONNECT_HOST="http://localhost:8080" - Access to secretsline 62
[Credential-related source omitted] - Network accessline 160
- `references/get-started.md` - `references/cli-examples.md` - https://developer.1password.com/docs/cli/ - Network accessline 161
- `references/cli-examples.md` - https://developer.1password.com/docs/cli/ - https://developer.1password.com/docs/service-accounts/ - Network accessline 162
- https://developer.1password.com/docs/cli/ - Network accessline 163
- https://developer.1password.com/docs/service-accounts/
optional-skills/security/1password/references/cli-examples.md
- Access to secretsline 22
[Credential-related source omitted]
optional-skills/security/1password/references/get-started.md
- Network accessline 3
Official docs: https://developer.1password.com/docs/cli/get-started/
optional-skills/security/godmode/SKILL.md
- Network accessline 16
Bypass safety filters on API-served LLMs using techniques from [G0DM0D3](https://github.com/elder-plinius/G0DM0D3) and [L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S). Three attack modes plus Hermes-native configuration for persistent - Dangerous shell operationline 66
exec(open(os.path.expanduser( - Access to secretsline 67
[Credential-related source omitted] - Dangerous shell operationline 83
**Important:** Always use `load_godmode.py` instead of loading individual scripts directly. The individual scripts have `argparse` CLI entry points and `__name__` guards that break when loaded via `exec()` in execute_code. The loader handle - Dangerous shell operationline 198
[Credential-related source omitted] - Access to secretsline 198
[Credential-related source omitted] - Dangerous shell operationline 235
[Credential-related source omitted] - Access to secretsline 235
[Credential-related source omitted] - Access to secretsline 240
[Credential-related source omitted] - Network accessline 290
[Credential-related source omitted] - Network accessline 291
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Network accessline 292
base_url="https://openrouter.ai/api/v1" - Network accessline 352
- **G0DM0D3:** [elder-plinius/G0DM0D3](https://github.com/elder-plinius/G0DM0D3) (AGPL-3.0) - Network accessline 353
- **L1B3RT4S:** [elder-plinius/L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S) (AGPL-3.0) - Network accessline 354
- **Pliny the Prompter:** [@elder_plinius](https://x.com/elder_plinius) - Dangerous shell operationline 403
9. **Always use `load_godmode.py` in execute_code** — The individual scripts (`parseltongue.py`, `godmode_race.py`, `auto_jailbreak.py`) have argparse CLI entry points with `if __name__ == '__main__'` blocks. When loaded via `exec()` in exe - Access to secretsline 406
[Credential-related source omitted]
optional-skills/security/godmode/references/jailbreak-templates.md
- Network accessline 3
Source: [G0DM0D3](https://github.com/elder-plinius/G0DM0D3) + [L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S) - Dangerous shell operationline 117
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Network accessline 126
- The system prompt in GODMODE CLASSIC keeps `{QUERY}` as literal text (the variable name is part of the jailbreak frame) - Only the user message gets the actual query injected - Templates source: [L1B3RT4S repo](https://github.com/elder-pl - Network accessline 128
- Templates source: [L1B3RT4S repo](https://github.com/elder-plinius/L1B3RT4S) by Pliny the Prompter
optional-skills/security/godmode/references/refusal-detection.md
optional-skills/security/godmode/scripts/auto_jailbreak.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 9
exec(open(os.path.expanduser( - Access to secretsline 10
[Credential-related source omitted] - Dangerous shell operationline 40
# Resolve skill directory — works both as direct script and via exec() - Dangerous shell operationline 44
# __file__ not defined when loaded via exec() — search standard paths - Dangerous shell operationline 59
exec(compile(open(_parseltongue_path).read(), str(_parseltongue_path), 'exec'), _caller_globals) - Dangerous shell operationline 61
exec(compile(open(_race_path).read(), str(_race_path), 'exec'), _caller_globals) - Network accessline 336
model_cfg = cfg.get("model", {}) if isinstance(model_cfg, str): return model_cfg, "https://openrouter.ai/api/v1" - Network accessline 337
if isinstance(model_cfg, str): return model_cfg, "https://openrouter.ai/api/v1" model_name = model_cfg.get("name", "") - Network accessline 338
return model_cfg, "https://openrouter.ai/api/v1" - Network accessline 339
model_name = model_cfg.get("name", "") base_url = model_cfg.get("base_url", "https://openrouter.ai/api/v1") return model_name, base_url - Network accessline 340
base_url = model_cfg.get("base_url", "https://openrouter.ai/api/v1") - Access to secretsline 349
[Credential-related source omitted] - Access to secretsline 351
[Credential-related source omitted] - Access to secretsline 353
[Credential-related source omitted] - Access to secretsline 355
[Credential-related source omitted] - Access to secretsline 427
[Credential-related source omitted] - Access to secretsline 439
[Credential-related source omitted] - Network accessline 457
return {"success": False, "error": "No model specified and couldn't read config.yaml"} if not base_url: base_url = "https://openrouter.ai/api/v1" - Network accessline 458
[Credential-related source omitted] - Network accessline 459
base_url = "https://openrouter.ai/api/v1" - Access to secretsline 460
[Credential-related source omitted] - Access to secretsline 461
[Credential-related source omitted] - Access to secretsline 462
[Credential-related source omitted] - Access to secretsline 476
[Credential-related source omitted]
optional-skills/security/godmode/scripts/godmode_race.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 10
[Credential-related source omitted] - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Access to secretsline 323
[Credential-related source omitted] - Access to secretsline 324
[Credential-related source omitted] - Access to secretsline 325
[Credential-related source omitted] - Network accessline 327
[Credential-related source omitted] - Access to secretsline 327
[Credential-related source omitted] - Access to secretsline 400
[Credential-related source omitted] - Access to secretsline 448
[Credential-related source omitted] - Access to secretsline 449
[Credential-related source omitted] - Access to secretsline 450
[Credential-related source omitted] - Network accessline 452
[Credential-related source omitted] - Access to secretsline 452
[Credential-related source omitted]
optional-skills/security/godmode/scripts/load_godmode.py
optional-skills/security/godmode/scripts/parseltongue.py
optional-skills/security/oss-forensics/SKILL.md
- Access to secretsline 103
[Credential-related source omitted] - Network accessline 125
# Clone repository git clone https://github.com/OWNER/REPO.git target_repo && cd target_repo - Network accessline 126
git clone https://github.com/OWNER/REPO.git target_repo && cd target_repo - Network accessline 163
# Commits (paginated) curl -s "https://api.github.com/repos/OWNER/REPO/commits?per_page=100" > api_commits.json - Network accessline 164
curl -s "https://api.github.com/repos/OWNER/REPO/commits?per_page=100" > api_commits.json - Network accessline 166
# Pull Requests including closed/deleted curl -s "https://api.github.com/repos/OWNER/REPO/pulls?state=all&per_page=100" > api_prs.json - Network accessline 167
curl -s "https://api.github.com/repos/OWNER/REPO/pulls?state=all&per_page=100" > api_prs.json - Network accessline 169
# Issues curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=all&per_page=100" > api_issues.json - Network accessline 170
curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=all&per_page=100" > api_issues.json - Network accessline 172
# Contributors and collaborator changes curl -s "https://api.github.com/repos/OWNER/REPO/contributors" > api_contributors.json - Network accessline 173
curl -s "https://api.github.com/repos/OWNER/REPO/contributors" > api_contributors.json - Network accessline 175
# Repository events (last 300) curl -s "https://api.github.com/repos/OWNER/REPO/events?per_page=100" > api_events.json - Network accessline 176
curl -s "https://api.github.com/repos/OWNER/REPO/events?per_page=100" > api_events.json - Network accessline 178
# Check specific suspicious commit SHA details curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" > commit_detail.json - Network accessline 179
curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" > commit_detail.json - Network accessline 181
# Releases curl -s "https://api.github.com/repos/OWNER/REPO/releases?per_page=100" > api_releases.json - Network accessline 182
curl -s "https://api.github.com/repos/OWNER/REPO/releases?per_page=100" > api_releases.json - Network accessline 184
# Check if a specific commit exists (force-pushed commits may 404 on commits/ but succeed on git/commits/) curl -s "https://api.github.com/repos/OWNER/REPO/commits/SHA" | jq .sha - Network accessline 185
curl -s "https://api.github.com/repos/OWNER/REPO/commits/SHA" | jq .sha - Network accessline 205
# Search for archived snapshots of the repo main page curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO&output=json&limit=100&from=YYYYMMDD&to=YYYYMMDD" > wayback_main.json - Network accessline 206
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO&output=json&limit=100&from=YYYYMMDD&to=YYYYMMDD" > wayback_main.json - Network accessline 208
# Search for a specific deleted issue curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUM&output=json&limit=50" > wayback_issue_NUM.json - Network accessline 209
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUM&output=json&limit=50" > wayback_issue_NUM.json - Network accessline 211
# Search for a specific deleted PR curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/pull/NUM&output=json&limit=50" > wayback_pr_NUM.json - Network accessline 212
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/pull/NUM&output=json&limit=50" > wayback_pr_NUM.json - Network accessline 214
# Fetch the best snapshot of a page # Use the Wayback Machine URL: https://web.archive.org/web/TIMESTAMP/ORIGINAL_URL - Network accessline 215
# Use the Wayback Machine URL: https://web.archive.org/web/TIMESTAMP/ORIGINAL_URL - Network accessline 216
# Example: https://web.archive.org/web/20240101000000*/github.com/OWNER/REPO - Network accessline 218
# Advanced: Search for deleted releases/tags curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/releases/tag/*&output=json" > wayback_tags.json - Network accessline 219
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/releases/tag/*&output=json" > wayback_tags.json - Network accessline 221
# Advanced: Search for historical wiki changes curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/wiki/*&output=json" > wayback_wiki.json - Network accessline 222
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/wiki/*&output=json" > wayback_wiki.json - Access to secretsline 408
[Credential-related source omitted]
optional-skills/security/oss-forensics/references/evidence-types.md
- Access to secretsline 29
[Credential-related source omitted] - Network accessline 33
| `PACKAGE_NAME` | A malicious or squatted package name | `colo-rs` (typosquatting `color`), `lodash-utils` | | `ACTOR_USERNAME` | A GitHub handle linked to the attack | `malicious-bot-account` | | `MALICIOUS_URL` | A URL to a malicious res - Network accessline 34
| `ACTOR_USERNAME` | A GitHub handle linked to the attack | `malicious-bot-account` | | `MALICIOUS_URL` | A URL to a malicious resource | `https://evil.example.com/payload.sh` | | `WORKFLOW_FILE` | A suspicious CI/CD workflow file | `.githu - Network accessline 35
| `MALICIOUS_URL` | A URL to a malicious resource | `https://evil.example.com/payload.sh` |
optional-skills/security/oss-forensics/references/recovery-techniques.md
- Network accessline 14
# View commit metadata curl -s "https://github.com/OWNER/REPO/commit/SHA" - Network accessline 15
curl -s "https://github.com/OWNER/REPO/commit/SHA" - Network accessline 17
# Download as patch (includes full diff) curl -s "https://github.com/OWNER/REPO/commit/SHA.patch" > recovered_commit.patch - Network accessline 18
curl -s "https://github.com/OWNER/REPO/commit/SHA.patch" > recovered_commit.patch - Network accessline 20
# Download as diff curl -s "https://github.com/OWNER/REPO/commit/SHA.diff" > recovered_commit.diff - Network accessline 21
curl -s "https://github.com/OWNER/REPO/commit/SHA.diff" > recovered_commit.diff - Network accessline 23
# Example (Istio credential leak - real incident): curl -s "https://github.com/istio/istio/commit/FORCE_PUSHED_SHA.patch" - Network accessline 24
curl -s "https://github.com/istio/istio/commit/FORCE_PUSHED_SHA.patch" - Network accessline 35
# Works for commits force-pushed off branches but still on server # Note: /commits/SHA may 404, but /git/commits/SHA often succeeds for orphaned commits curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" | jq . - Network accessline 37
curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" | jq . - Network accessline 39
# Get the tree (file listing) of a force-pushed commit curl -s "https://api.github.com/repos/OWNER/REPO/git/trees/SHA?recursive=1" | jq . - Network accessline 40
curl -s "https://api.github.com/repos/OWNER/REPO/git/trees/SHA?recursive=1" | jq . - Network accessline 42
# Get a specific file from a force-pushed commit curl -s "https://api.github.com/repos/OWNER/REPO/contents/PATH?ref=SHA" | jq .content | base64 -d - Network accessline 43
curl -s "https://api.github.com/repos/OWNER/REPO/contents/PATH?ref=SHA" | jq .content | base64 -d - Network accessline 88
# Find all archived snapshots of a specific issue curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool - Dangerous shell operationline 89
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool - Network accessline 89
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool - Network accessline 91
# Fetch the best snapshot # Use the timestamp from the CDX result: # https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER - Network accessline 92
# Use the timestamp from the CDX result: # https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER curl -s "https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER" > issue_NUMBER_archived - Network accessline 93
# https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER - Network accessline 94
curl -s "https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER" > issue_NUMBER_archived.html - Network accessline 96
# Find all snapshots of the repo in a date range curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool - Dangerous shell operationline 97
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool - Network accessline 97
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool - Network accessline 103
# Closed issues (not deleted) are retrievable curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=closed&per_page=100" | jq '.[].number' - Network accessline 104
curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=closed&per_page=100" | jq '.[].number' - Network accessline 129
# Or via API (base64 encoded content): curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c " - Dangerous shell operationline 130
curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c " - Network accessline 130
curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c " - Possible obfuscationline 133
print(base64.b64decode(d['content']).decode()) - Network accessline 144
python3 SKILL_DIR/scripts/evidence-store.py --store evidence.json add --source "git fetch origin FORCE_PUSHED_SHA" --content "Recovered commit: FORCE_PUSHED_SHA | Author: attacker@example.com | Date: 2024-01-15 | Added file: malicious - Network accessline 147
--type git \ --actor "attacker-handle" \ --url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \ - Network accessline 148
--actor "attacker-handle" \ --url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \ --timestamp "2024-01-15T00:00:00Z" \ - Network accessline 149
--url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \
optional-skills/security/oss-forensics/scripts/evidence-store.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 15
python3 evidence-store.py --store evidence.json add \ --source "git fsck output" --content "dangling commit abc123" \ --type git --actor "malicious-user" --url "https://github.com/owner/repo/commit/abc123" - Network accessline 17
--type git --actor "malicious-user" --url "https://github.com/owner/repo/commit/abc123" - Access to secretsline 45
[Credential-related source omitted]
optional-skills/security/oss-forensics/templates/forensic-report.md
- Network accessline 149
- Evidence store: `evidence.json` (SHA-256 integrity: run `python3 SKILL_DIR/scripts/evidence-store.py --store evidence.json verify`) - Related issues: <!-- Link to GitHub issues, CVEs, security advisories --> - RAPTOR framework: https://gi - Network accessline 151
- RAPTOR framework: https://github.com/gadievron/raptor
optional-skills/security/oss-forensics/templates/malicious-package-report.md
- Access to secretsline 17
[Credential-related source omitted]
optional-skills/security/sherlock/SKILL.md
- Network accessline 18
Hunt down social media accounts by username across 400+ social networks using the [Sherlock Project](https://github.com/sherlock-project/sherlock). - Network accessline 100
[+] Instagram: https://instagram.com/username - Network accessline 101
[+] Twitter: https://twitter.com/username - Network accessline 102
[+] GitHub: https://github.com/username - Network accessline 176
> Found 12 accounts for username 'johndoe123': > > • https://twitter.com/johndoe123 - Network accessline 177
> > • https://twitter.com/johndoe123 > • https://github.com/johndoe123 - Network accessline 178
> • https://twitter.com/johndoe123 - Network accessline 179
> • https://github.com/johndoe123 - Network accessline 180
> • https://instagram.com/johndoe123
optional-skills/security/unbroker/README.md
- Access to secretsline 43
[Credential-related source omitted] - Network accessline 114
- **Coverage that matches or exceeds commercial services.** Two lanes: (1) people-search sites with per-site opt-out mechanics (19 curated records, including FamilyTreeNow, Radaris, and Nuwber, plus a live pull from [BADBOOL](https://gi - Network accessline 115
per-site opt-out mechanics (19 curated records, including FamilyTreeNow, Radaris, and Nuwber, plus a live pull from [BADBOOL](https://github.com/yaelwrites/Big-Ass-Data-Broker-Opt-Out-List)), and (2) the **state data-broker registries** - Network accessline 116
a live pull from [BADBOOL](https://github.com/yaelwrites/Big-Ass-Data-Broker-Opt-Out-List)), and - Network accessline 166
- Broker dataset adapted from the **Big-Ass Data Broker Opt-Out List (BADBOOL)** by **Yael Grauer**, licensed [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (attribution - Network accessline 167
licensed [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (attribution - Network accessline 168
required, non-commercial). See [yaelwrites.com](https://yaelwrites.com/).
optional-skills/security/unbroker/SKILL.md
- Network accessline 13
category: security related_skills: [google-workspace, agentmail, himalaya, scrapling, osint-investigation] homepage: https://github.com/NousResearch/hermes-agent - Network accessline 14
related_skills: [google-workspace, agentmail, himalaya, scrapling, osint-investigation] homepage: https://github.com/NousResearch/hermes-agent --- - Network accessline 15
homepage: https://github.com/NousResearch/hermes-agent - Access to secretsline 74
[Credential-related source omitted]
optional-skills/security/unbroker/references/brokers/addresses.json
- Network accessline 10
"search": { "method": "url_pattern", "url": "https://www.addresses.com/", - Network accessline 11
"method": "url_pattern", "url": "https://www.addresses.com/", "fetch": "web_extract", - Network accessline 12
"url": "https://www.addresses.com/", "fetch": "web_extract", "match_signal": "result", - Network accessline 17
], "url_patterns": { "name": "https://www.addresses.com/people/{First}+{Last}" - Network accessline 18
"url_patterns": { "name": "https://www.addresses.com/people/{First}+{Last}" }, - Network accessline 19
"name": "https://www.addresses.com/people/{First}+{Last}" - Network accessline 27
"tier": "T1", "method": "web_form", "url": "https://suppression.peopleconnect.us/login", - Network accessline 28
"method": "web_form", "url": "https://suppression.peopleconnect.us/login", "requires": { - Network accessline 29
"url": "https://suppression.peopleconnect.us/login",
optional-skills/security/unbroker/references/brokers/advancedbackgroundchecks.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://www.advancedbackgroundchecks.com", - Network accessline 8
"method": "url_pattern", "url": "https://www.advancedbackgroundchecks.com", "fetch": "web_extract", - Network accessline 9
"url": "https://www.advancedbackgroundchecks.com", "fetch": "web_extract", "match_signal": "result", - Network accessline 12
"by": ["name", "phone", "address"], "url_patterns": { "name": "https://www.advancedbackgroundchecks.com/name/{first}-{last}", - Network accessline 13
"url_patterns": { "name": "https://www.advancedbackgroundchecks.com/name/{first}-{last}", "name_state": "https://www.advancedbackgroundchecks.com/name/{first}-{last}/in/{ST}", - Network accessline 14
"name": "https://www.advancedbackgroundchecks.com/name/{first}-{last}", - Network accessline 15
"name_state": "https://www.advancedbackgroundchecks.com/name/{first}-{last}/in/{ST}", - Network accessline 16
"phone": "https://www.advancedbackgroundchecks.com/phone/{digits10}", - Network accessline 17
"address": "https://www.advancedbackgroundchecks.com/address/{num}-{street-with-type}-{city}-{ST}-{zip}", - Network accessline 18
"person_profile": "https://www.advancedbackgroundchecks.com/find/person/{first}-{last}-{22charID}" - Network accessline 32
"tier": "T2", "method": "web_form", "url": "https://www.advancedbackgroundchecks.com/opt-out", - Network accessline 33
"method": "web_form", "url": "https://www.advancedbackgroundchecks.com/opt-out", "requires": { - Network accessline 34
"url": "https://www.advancedbackgroundchecks.com/opt-out",
optional-skills/security/unbroker/references/brokers/beenverified.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://www.beenverified.com/app/optout/search", - Network accessline 10
"method": "url_pattern", "url": "https://www.beenverified.com/app/optout/search", "fetch": "browser", - Network accessline 11
"url": "https://www.beenverified.com/app/optout/search", "fetch": "browser", "match_signal": "result", - Network accessline 17
"tier": "T1", "method": "web_form", "url": "https://www.beenverified.com/svc/optout/search/optouts", - Network accessline 18
"method": "web_form", "url": "https://www.beenverified.com/svc/optout/search/optouts", "email": "privacy@beenverified.com", - Network accessline 19
"url": "https://www.beenverified.com/svc/optout/search/optouts",
optional-skills/security/unbroker/references/brokers/clustal.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://www.clustal.org/", - Network accessline 8
"method": "url_pattern", "url": "https://www.clustal.org/", "fetch": "web_extract", - Network accessline 9
"url": "https://www.clustal.org/", "fetch": "web_extract", "match_signal": "record", - Network accessline 12
"by": ["name"], "url_patterns": { "name_index": "https://www.clustal.org/people-search/{letter}/{first-last}/", - Network accessline 13
"url_patterns": { "name_index": "https://www.clustal.org/people-search/{letter}/{first-last}/", "name_state": "https://www.clustal.org/people-search/{letter}/{first-last}/{st}/", - Network accessline 14
"name_index": "https://www.clustal.org/people-search/{letter}/{first-last}/", - Network accessline 15
"name_state": "https://www.clustal.org/people-search/{letter}/{first-last}/{st}/", - Network accessline 16
"record": "https://www.clustal.org/record/{first-last}-{8charID}/" - Network accessline 26
"tier": "T0", "method": "web_form", "url": "https://www.clustal.org/privacy-control/", - Network accessline 27
"method": "web_form", "url": "https://www.clustal.org/privacy-control/", "requires": { - Network accessline 28
"url": "https://www.clustal.org/privacy-control/",
optional-skills/security/unbroker/references/brokers/clustrmaps.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://clustrmaps.com/", - Network accessline 12
"method": "url_pattern", "url": "https://clustrmaps.com/", "fetch": "browser", - Network accessline 13
"url": "https://clustrmaps.com/", "fetch": "browser", "match_signal": "result", - Network accessline 24
"tier": "T2", "method": "web_form", "url": "https://clustrmaps.com/bl/opt-out", - Network accessline 25
"method": "web_form", "url": "https://clustrmaps.com/bl/opt-out", "requires": { - Network accessline 26
"url": "https://clustrmaps.com/bl/opt-out",
optional-skills/security/unbroker/references/brokers/cyberbackgroundchecks.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.cyberbackgroundchecks.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.cyberbackgroundchecks.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.cyberbackgroundchecks.com/", "fetch": "browser", "match_signal": "result", - Network accessline 21
], "url_patterns": { "phone": "https://www.cyberbackgroundchecks.com/phone/{digits10}" - Network accessline 22
"url_patterns": { "phone": "https://www.cyberbackgroundchecks.com/phone/{digits10}" }, - Network accessline 23
"phone": "https://www.cyberbackgroundchecks.com/phone/{digits10}" - Network accessline 30
"tier": "T2", "method": "web_form", "url": "https://www.cyberbackgroundchecks.com/removal", - Network accessline 31
"method": "web_form", "url": "https://www.cyberbackgroundchecks.com/removal", "requires": { - Network accessline 32
"url": "https://www.cyberbackgroundchecks.com/removal",
optional-skills/security/unbroker/references/brokers/familytreenow.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.familytreenow.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.familytreenow.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.familytreenow.com/", "fetch": "browser", "match_signal": "result", - Network accessline 24
"tier": "T2", "method": "web_form", "url": "https://www.familytreenow.com/optout", - Network accessline 25
"method": "web_form", "url": "https://www.familytreenow.com/optout", "requires": { - Network accessline 26
"url": "https://www.familytreenow.com/optout",
optional-skills/security/unbroker/references/brokers/fastpeoplesearch.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://www.fastpeoplesearch.com/", - Network accessline 8
"method": "url_pattern", "url": "https://www.fastpeoplesearch.com/", "fetch": "browser", - Network accessline 9
"url": "https://www.fastpeoplesearch.com/", "fetch": "browser", "antibot": "datadome", - Network accessline 14
"by": ["name", "phone", "address"], "url_patterns": { "name": "https://www.fastpeoplesearch.com/name/{first}-{last}", - Network accessline 15
"url_patterns": { "name": "https://www.fastpeoplesearch.com/name/{first}-{last}", "phone": "https://www.fastpeoplesearch.com/{areacode}-{prefix}-{line}" - Network accessline 16
"name": "https://www.fastpeoplesearch.com/name/{first}-{last}", - Network accessline 17
"phone": "https://www.fastpeoplesearch.com/{areacode}-{prefix}-{line}" - Network accessline 27
"tier": "T2", "method": "web_form", "url": "https://www.fastpeoplesearch.com/removal", - Network accessline 28
"method": "web_form", "url": "https://www.fastpeoplesearch.com/removal", "requires": { - Network accessline 29
"url": "https://www.fastpeoplesearch.com/removal",
optional-skills/security/unbroker/references/brokers/intelius.json
- Network accessline 22
"search": { "method": "url_pattern", "url": "https://www.intelius.com/", - Network accessline 23
"method": "url_pattern", "url": "https://www.intelius.com/", "fetch": "web_extract", - Network accessline 24
"url": "https://www.intelius.com/", "fetch": "web_extract", "match_signal": "result", - Network accessline 31
], "url_patterns": { "name": "https://www.intelius.com/people-search/{First}-{Last}/", - Network accessline 32
"url_patterns": { "name": "https://www.intelius.com/people-search/{First}-{Last}/", "name_state": "https://www.intelius.com/people-search/{first}-{last}/{state-name}/", - Network accessline 33
"name": "https://www.intelius.com/people-search/{First}-{Last}/", - Network accessline 34
"name_state": "https://www.intelius.com/people-search/{first}-{last}/{state-name}/", - Network accessline 35
"full_report": "https://www.intelius.com/search/?firstName={First}&lastName={Last}&city={City}&state={ST}&traffic%5Bsource%5D=INTSEO", - Network accessline 36
"phone": "https://www.intelius.com/reverse-phone-lookup/{areacode}-{prefix}-{line}/" - Network accessline 47
"tier": "T1", "method": "web_form", "url": "https://suppression.peopleconnect.us/login", - Network accessline 48
"method": "web_form", "url": "https://suppression.peopleconnect.us/login", "email": "privacy@peopleconnect.us", - Network accessline 49
"url": "https://suppression.peopleconnect.us/login", - Network accessline 67
"via": "in_flow", "prefer": false, "url": "https://suppression.peopleconnect.us/guided-mode", - Network accessline 68
"prefer": false, "url": "https://suppression.peopleconnect.us/guided-mode", "email": "privacy@peopleconnect.us", - Network accessline 69
"url": "https://suppression.peopleconnect.us/guided-mode",
optional-skills/security/unbroker/references/brokers/mylife.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://www.mylife.com", - Network accessline 8
"method": "url_pattern", "url": "https://www.mylife.com", "fetch": "browser", - Network accessline 9
"url": "https://www.mylife.com", "fetch": "browser", "match_signal": "profile", - Network accessline 15
"tier": "T3", "method": "phone", "url": "https://www.mylife.com/privacyrequest", - Network accessline 16
"method": "phone", "url": "https://www.mylife.com/privacyrequest", "requires": { - Network accessline 17
"url": "https://www.mylife.com/privacyrequest",
optional-skills/security/unbroker/references/brokers/nuwber.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://nuwber.com/", - Network accessline 12
"method": "url_pattern", "url": "https://nuwber.com/", "fetch": "browser", - Network accessline 13
"url": "https://nuwber.com/", "fetch": "browser", "match_signal": "result", - Network accessline 24
"tier": "T2", "method": "web_form", "url": "https://nuwber.com/removal/link", - Network accessline 25
"method": "web_form", "url": "https://nuwber.com/removal/link", "requires": { - Network accessline 26
"url": "https://nuwber.com/removal/link",
optional-skills/security/unbroker/references/brokers/peekyou.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.peekyou.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.peekyou.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.peekyou.com/", "fetch": "browser", "match_signal": "result", - Network accessline 24
"tier": "T2", "method": "web_form", "url": "https://www.peekyou.com/about/contact/optout", - Network accessline 25
"method": "web_form", "url": "https://www.peekyou.com/about/contact/optout", "requires": { - Network accessline 26
"url": "https://www.peekyou.com/about/contact/optout",
optional-skills/security/unbroker/references/brokers/peoplefinders.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.peoplefinders.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.peoplefinders.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.peoplefinders.com/", "fetch": "browser", "match_signal": "result", - Network accessline 21
], "url_patterns": { "phone": "https://www.peoplefinders.com/phone/{areacode}-{prefix}-{line}" - Network accessline 22
"url_patterns": { "phone": "https://www.peoplefinders.com/phone/{areacode}-{prefix}-{line}" }, - Network accessline 23
"phone": "https://www.peoplefinders.com/phone/{areacode}-{prefix}-{line}" - Network accessline 30
"tier": "T2", "method": "web_form", "url": "https://www.peoplefinders.com/opt-out", - Network accessline 31
"method": "web_form", "url": "https://www.peoplefinders.com/opt-out", "requires": { - Network accessline 32
"url": "https://www.peoplefinders.com/opt-out",
optional-skills/security/unbroker/references/brokers/radaris.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://radaris.com/", - Network accessline 10
"method": "url_pattern", "url": "https://radaris.com/", "fetch": "web_extract", - Network accessline 11
"url": "https://radaris.com/", "fetch": "web_extract", "match_signal": "View Profile", - Network accessline 18
], "url_patterns": { "name": "https://radaris.com/p/{First}/{Last}/" - Network accessline 19
"url_patterns": { "name": "https://radaris.com/p/{First}/{Last}/" }, - Network accessline 20
"name": "https://radaris.com/p/{First}/{Last}/" - Network accessline 29
"tier": "T2", "method": "web_form", "url": "https://radaris.com/control-privacy", - Network accessline 30
"method": "web_form", "url": "https://radaris.com/control-privacy", "requires": { - Network accessline 31
"url": "https://radaris.com/control-privacy", - Network accessline 47
"quirks": [ "CAPTCHA: the form carries a Google reCAPTCHA (hidden field g-recaptcha-response) plus anti-bot fingerprint tokens (jfp, token). Tier is T2 without a captcha-clearing browser backend (T1 with Browserbase). (Record previous - Network accessline 48
"CAPTCHA: the form carries a Google reCAPTCHA (hidden field g-recaptcha-response) plus anti-bot fingerprint tokens (jfp, token). Tier is T2 without a captcha-clearing browser backend (T1 with Browserbase). (Record previously mis-declared ca - Network accessline 49
"The /control-privacy form REQUIRES the per-person NUMBERED profile URL. Pasting the aggregate /p/{First}/{Last}/ URL is rejected with the validation error: 'The URL must include unique number at the end'. The real removable URL looks like
optional-skills/security/unbroker/references/brokers/rehold.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://rehold.com/", - Network accessline 10
"method": "url_pattern", "url": "https://rehold.com/", "fetch": "browser", - Network accessline 11
"url": "https://rehold.com/", "fetch": "browser", "match_signal": "result", - Network accessline 21
"tier": "T2", "method": "web_form", "url": "https://rehold.com/optout", - Network accessline 22
"method": "web_form", "url": "https://rehold.com/optout", "requires": { - Network accessline 23
"url": "https://rehold.com/optout",
optional-skills/security/unbroker/references/brokers/searchpeoplefree.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.searchpeoplefree.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.searchpeoplefree.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.searchpeoplefree.com/", "fetch": "browser", "match_signal": "result", - Network accessline 21
], "url_patterns": { "phone": "https://www.searchpeoplefree.com/phone-lookup/{areacode}-{prefix}-{line}" - Network accessline 22
"url_patterns": { "phone": "https://www.searchpeoplefree.com/phone-lookup/{areacode}-{prefix}-{line}" }, - Network accessline 23
"phone": "https://www.searchpeoplefree.com/phone-lookup/{areacode}-{prefix}-{line}" - Network accessline 30
"tier": "T2", "method": "web_form", "url": "https://www.searchpeoplefree.com/opt-out", - Network accessline 31
"method": "web_form", "url": "https://www.searchpeoplefree.com/opt-out", "requires": { - Network accessline 32
"url": "https://www.searchpeoplefree.com/opt-out",
optional-skills/security/unbroker/references/brokers/socialcatfish.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://socialcatfish.com/", - Network accessline 10
"method": "url_pattern", "url": "https://socialcatfish.com/", "fetch": "browser", - Network accessline 11
"url": "https://socialcatfish.com/", "fetch": "browser", "match_signal": "result", - Network accessline 25
"tier": "T2", "method": "web_form", "url": "https://socialcatfish.com/opt-out/", - Network accessline 26
"method": "web_form", "url": "https://socialcatfish.com/opt-out/", "requires": { - Network accessline 27
"url": "https://socialcatfish.com/opt-out/",
optional-skills/security/unbroker/references/brokers/spokeo.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://www.spokeo.com/search", - Network accessline 10
"method": "url_pattern", "url": "https://www.spokeo.com/search", "fetch": "browser", - Network accessline 11
"url": "https://www.spokeo.com/search", "fetch": "browser", "match_signal": "profile", - Network accessline 14
"by": ["name", "phone", "email", "address"], "url_patterns": { "phone": "https://www.spokeo.com/{areacode}-{prefix}-{line}" - Network accessline 15
"url_patterns": { "phone": "https://www.spokeo.com/{areacode}-{prefix}-{line}" }, - Network accessline 16
"phone": "https://www.spokeo.com/{areacode}-{prefix}-{line}" - Network accessline 23
"tier": "T1", "method": "web_form", "url": "https://www.spokeo.com/optout", - Network accessline 24
"method": "web_form", "url": "https://www.spokeo.com/optout", "email": "privacy@spokeo.com", - Network accessline 25
"url": "https://www.spokeo.com/optout",
optional-skills/security/unbroker/references/brokers/thatsthem.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://thatsthem.com/", - Network accessline 8
"method": "url_pattern", "url": "https://thatsthem.com/", "fetch": "browser", - Network accessline 9
"url": "https://thatsthem.com/", "fetch": "browser", "match_signal": "result", - Network accessline 12
"by": ["name", "phone", "email", "address"], "url_patterns": { "name": "https://thatsthem.com/name/{First}-{Last}/{City}-{ST}", - Network accessline 13
"url_patterns": { "name": "https://thatsthem.com/name/{First}-{Last}/{City}-{ST}", "phone": "https://thatsthem.com/phone/{areacode}-{prefix}-{line}", - Network accessline 14
"name": "https://thatsthem.com/name/{First}-{Last}/{City}-{ST}", - Network accessline 15
"phone": "https://thatsthem.com/phone/{areacode}-{prefix}-{line}", - Network accessline 16
"email": "https://thatsthem.com/email/{email}", - Network accessline 17
"address": "https://thatsthem.com/address/{Street}-{City}-{ST}-{ZIP}" - Network accessline 27
"tier": "T2", "method": "web_form", "url": "https://thatsthem.com/optout", - Network accessline 28
"method": "web_form", "url": "https://thatsthem.com/optout", "requires": { - Network accessline 29
"url": "https://thatsthem.com/optout",
optional-skills/security/unbroker/references/brokers/truepeoplesearch.json
- Network accessline 7
"search": { "method": "url_pattern", "url": "https://www.truepeoplesearch.com/", - Network accessline 8
"method": "url_pattern", "url": "https://www.truepeoplesearch.com/", "fetch": "browser", - Network accessline 9
"url": "https://www.truepeoplesearch.com/", "fetch": "browser", "antibot": "datadome", - Network accessline 14
"by": ["name", "phone", "address", "email"], "url_patterns": { "name": "https://www.truepeoplesearch.com/results?name={First%20Last}&citystatezip={City,%20ST}" - Network accessline 15
"url_patterns": { "name": "https://www.truepeoplesearch.com/results?name={First%20Last}&citystatezip={City,%20ST}" }, - Network accessline 16
"name": "https://www.truepeoplesearch.com/results?name={First%20Last}&citystatezip={City,%20ST}" - Network accessline 25
"tier": "T2", "method": "web_form", "url": "https://www.truepeoplesearch.com/removal", - Network accessline 26
"method": "web_form", "url": "https://www.truepeoplesearch.com/removal", "requires": { - Network accessline 27
"url": "https://www.truepeoplesearch.com/removal",
optional-skills/security/unbroker/references/brokers/usphonebook.json
- Network accessline 11
"search": { "method": "url_pattern", "url": "https://www.usphonebook.com/", - Network accessline 12
"method": "url_pattern", "url": "https://www.usphonebook.com/", "fetch": "browser", - Network accessline 13
"url": "https://www.usphonebook.com/", "fetch": "browser", "match_signal": "result", - Network accessline 21
], "url_patterns": { "phone": "https://www.usphonebook.com/{areacode}-{prefix}-{line}" - Network accessline 22
"url_patterns": { "phone": "https://www.usphonebook.com/{areacode}-{prefix}-{line}" }, - Network accessline 23
"phone": "https://www.usphonebook.com/{areacode}-{prefix}-{line}" - Network accessline 30
"tier": "T2", "method": "web_form", "url": "https://www.usphonebook.com/opt-out", - Network accessline 31
"method": "web_form", "url": "https://www.usphonebook.com/opt-out", "requires": { - Network accessline 32
"url": "https://www.usphonebook.com/opt-out",
optional-skills/security/unbroker/references/brokers/whitepages.json
- Network accessline 9
"search": { "method": "url_pattern", "url": "https://www.whitepages.com/", - Network accessline 10
"method": "url_pattern", "url": "https://www.whitepages.com/", "fetch": "browser", - Network accessline 11
"url": "https://www.whitepages.com/", "fetch": "browser", "match_signal": "listing", - Network accessline 17
"tier": "T1", "method": "email", "url": "https://www.whitepages.com/suppression_requests", - Network accessline 18
"method": "email", "url": "https://www.whitepages.com/suppression_requests", "email": "privacyrequest@whitepages.com", - Network accessline 19
"url": "https://www.whitepages.com/suppression_requests", - Network accessline 32
"via": "email", "email": "privacyrequest@whitepages.com", "url": "https://whitepagesprivacy.zendesk.com/hc/en-us/requests/new", - Network accessline 33
"email": "privacyrequest@whitepages.com", "url": "https://whitepagesprivacy.zendesk.com/hc/en-us/requests/new", "kinds": ["ccpa", "generic"], - Network accessline 34
"url": "https://whitepagesprivacy.zendesk.com/hc/en-us/requests/new",
optional-skills/security/unbroker/references/methods.md
optional-skills/security/unbroker/scripts/autopilot.py
optional-skills/security/unbroker/scripts/badbool.py
- Executable scriptline 1
Executable file - Network accessline 3
BADBOOL (https://github.com/yaelwrites/Big-Ass-Data-Broker-Opt-Out-List) is a - Network accessline 9
`parse()` is pure (markdown in, records out) so it is tested offline; `fetch()` is - Network accessline 20
DEFAULT_URL = ( "https://raw.githubusercontent.com/yaelwrites/" - Network accessline 21
"https://raw.githubusercontent.com/yaelwrites/" - Network accessline 159
def fetch(url: str = DEFAULT_URL, timeout: int = 30) -> str: - Network accessline 160
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 161
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 169
"""Fetch (or accept) BADBOOL markdown, parse it, and write the snapshot cache.""" - Network accessline 170
md = markdown if markdown is not None else fetch(url)
optional-skills/security/unbroker/scripts/brokers.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/cdp.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 113
def _http_get(url: str, timeout: float) -> bytes: req = urllib.request.Request(url, headers={"User-Agent": "unbroker-cdp/1.0"}) - Network accessline 114
req = urllib.request.Request(url, headers={"User-Agent": "unbroker-cdp/1.0"}) - Network accessline 115
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 125
url = f"http://{host}:{int(port)}/json/version"
optional-skills/security/unbroker/scripts/config.py
- Executable scriptline 1
Executable file - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 104
[Credential-related source omitted] - Access to secretsline 105
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Access to secretsline 150
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted]
optional-skills/security/unbroker/scripts/crypto.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/dossier.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/email_modes.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/emailer.py
- Executable scriptline 1
Executable file - Access to secretsline 55
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 296
out: list[dict] = [] for mid in reversed(ids): # newest first _typ, msg_data = conn.fetch(mid, "(RFC822)") - Network accessline 297
for mid in reversed(ids): # newest first _typ, msg_data = conn.fetch(mid, "(RFC822)") raw = next((p[1] for p in msg_data or [] if isinstance(p, tuple)), None) - Network accessline 298
_typ, msg_data = conn.fetch(mid, "(RFC822)")
optional-skills/security/unbroker/scripts/ledger.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/legal.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/paths.py
optional-skills/security/unbroker/scripts/pdd.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 16
python pdd.py plan sub_xxxx --priority crucial python pdd.py record sub_xxxx spokeo found --found true \ --evidence '{"listing_urls":["https://www.spokeo.com/..."]}' - Network accessline 17
python pdd.py record sub_xxxx spokeo found --found true --evidence '{"listing_urls":["https://www.spokeo.com/..."]}' - Network accessline 18
--evidence '{"listing_urls":["https://www.spokeo.com/..."]}' - Network accessline 19
python pdd.py render-email sub_xxxx spokeo --listing https://www.spokeo.com/... - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 128
[Credential-related source omitted] - Access to secretsline 146
[Credential-related source omitted] - Access to secretsline 148
[Credential-related source omitted] - Access to secretsline 182
[Credential-related source omitted]
optional-skills/security/unbroker/scripts/registry.py
- Executable scriptline 1
Executable file - Network accessline 16
`parse()` is pure (CSV text in, records out) so it is tested offline; `fetch()` is - Network accessline 34
_CA_CSV = "https://cppa.ca.gov/data_broker_registry/registry{year}.csv" - Network accessline 35
_CA_FLOOR_YEAR = 2025 DEFAULT_URL = _CA_CSV.format(year=_CA_FLOOR_YEAR) DROP_URL = "https://privacy.ca.gov/drop" - Network accessline 37
DROP_URL = "https://privacy.ca.gov/drop" - Network accessline 39
DNC_URL = "https://www.donotcall.gov/register.html" - Network accessline 85
"name": "California Data Broker Registry (CPPA)"}, "vt": {"jurisdiction": "US-VT", "format": "portal", "has_drop": False, "url": "https://bizfilings.vermont.gov/online/DatabrokerInquire/", - Network accessline 86
"vt": {"jurisdiction": "US-VT", "format": "portal", "has_drop": False, "url": "https://bizfilings.vermont.gov/online/DatabrokerInquire/", "name": "Vermont Data Broker Registry (Secretary of State)"}, - Network accessline 87
"url": "https://bizfilings.vermont.gov/online/DatabrokerInquire/", - Network accessline 88
"name": "Vermont Data Broker Registry (Secretary of State)"}, "or": {"jurisdiction": "US-OR", "format": "portal", "has_drop": False, "url": "https://dfr.oregon.gov/business/licensing/data-broker-registry/Pages/index.aspx", - Network accessline 89
"or": {"jurisdiction": "US-OR", "format": "portal", "has_drop": False, "url": "https://dfr.oregon.gov/business/licensing/data-broker-registry/Pages/index.aspx", "name": "Oregon Data Broker Registry (DCBS)"}, - Network accessline 90
"url": "https://dfr.oregon.gov/business/licensing/data-broker-registry/Pages/index.aspx", - Network accessline 91
"name": "Oregon Data Broker Registry (DCBS)"}, "tx": {"jurisdiction": "US-TX", "format": "portal", "has_drop": False, "url": "https://texas-sos.appianportalsgov.com/data-broker-registry", - Network accessline 92
"tx": {"jurisdiction": "US-TX", "format": "portal", "has_drop": False, "url": "https://texas-sos.appianportalsgov.com/data-broker-registry", "name": "Texas Data Broker Registry (Secretary of State)"}, - Network accessline 93
"url": "https://texas-sos.appianportalsgov.com/data-broker-registry", - Network accessline 244
def fetch(url: str = DEFAULT_URL, timeout: int = 60) -> str: - Network accessline 245
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 246
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 253
for url in ca_candidate_urls(): try: recs = parse(fetch(url), jurisdiction="US-CA", has_drop=True) - Network accessline 254
try: recs = parse(fetch(url), jurisdiction="US-CA", has_drop=True) except Exception: - Network accessline 255
recs = parse(fetch(url), jurisdiction="US-CA", has_drop=True) - Network accessline 265
"""CA single-source refresh: fetch (or accept) the CA CSV and write the cache.""" - Network accessline 266
text = csv_text if csv_text is not None else fetch(url) - Network accessline 298
used_url, recs = _fetch_ca_latest() # newest-year-first with fallback else: recs = parse(fetch(src["url"]), jurisdiction=src["jurisdiction"], has_drop=src["has_drop"]) - Network accessline 299
else: recs = parse(fetch(src["url"]), jurisdiction=src["jurisdiction"], has_drop=src["has_drop"]) except Exception as exc: - Network accessline 300
recs = parse(fetch(src["url"]), jurisdiction=src["jurisdiction"], has_drop=src["has_drop"])
optional-skills/security/unbroker/scripts/report.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/scan.py
- Executable scriptline 1
Executable file - Network accessline 16
def fetch(url: str, timeout: int = 20) -> tuple[int, str]: - Network accessline 17
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 18
try: with urllib.request.urlopen(req, timeout=timeout) as resp: charset = resp.headers.get_content_charset() or "utf-8" - Network accessline 19
with urllib.request.urlopen(req, timeout=timeout) as resp:
optional-skills/security/unbroker/scripts/storage.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/tiers.py
- Executable scriptline 1
Executable file
optional-skills/security/unbroker/scripts/vectors.py
- Executable scriptline 1
Executable file
optional-skills/security/web-pentest/SKILL.md
- Dangerous shell operationline 146
2. **Inventory sinks** — every `execute(`, `os.system(`, `eval(`, - Network accessline 180
whatweb -v $TARGET_URL > evidence/whatweb.txt curl -sIk $TARGET_URL > evidence/headers.txt - Network accessline 181
curl -sIk $TARGET_URL > evidence/headers.txt - Network accessline 258
- Full request (method, URL, headers, body) - Response (status, headers, relevant body excerpt) - Reproducer command (curl one-liner) - Network accessline 259
- Response (status, headers, relevant body excerpt) - Reproducer command (curl one-liner) - Impact statement - Network accessline 260
- Reproducer command (curl one-liner)
optional-skills/security/web-pentest/references/bypass-techniques.md
- Dangerous shell operationline 64
- `onerror=eval('alert(1)')` → `onerror=eval(name)` + set - Network accessline 106
resolution (TTL=0) - DNS records that resolve to internal IPs: `localtest.me` (127.0.0.1) - URL parsing differentials: `http://allowed-host@127.0.0.1`, - Network accessline 107
- DNS records that resolve to internal IPs: `localtest.me` (127.0.0.1) - URL parsing differentials: `http://allowed-host@127.0.0.1`, `http://127.0.0.1#@allowed-host` - Network accessline 108
- URL parsing differentials: `http://allowed-host@127.0.0.1`, - Network accessline 109
`http://127.0.0.1#@allowed-host` - Network accessline 110
- IDN homograph: `http://1.0.0.1` (fullwidth dots)
optional-skills/security/web-pentest/references/exploitation-techniques.md
- Network accessline 65
Witness: `<svg/onload=fetch("/HERMES-PENTEST-XSS-"+document.cookie)>` - Network accessline 76
Pattern: `<svg/onload=fetch("/HERMES-${runId}-${vulnId}")>`. Add a - Network accessline 163
- Operator-owned callback (`https://hermes-callback.example/abcdef`) - Network accessline 164
— confirms the request left the target's network - Internal recon (operator OK + scope): `http://127.0.0.1:6379/`, `http://127.0.0.1:9200/`, `http://[::1]:80/` - Network accessline 165
- Internal recon (operator OK + scope): `http://127.0.0.1:6379/`, - Network accessline 166
`http://127.0.0.1:9200/`, `http://[::1]:80/` - Network accessline 168
Cloud metadata (operator OK + your own infra): - AWS: `http://169.254.169.254/latest/meta-data/iam/security-credentials/` - Network accessline 169
- AWS: `http://169.254.169.254/latest/meta-data/iam/security-credentials/` - Network accessline 170
- GCP: `http://metadata.google.internal/computeMetadata/v1/` (needs - Network accessline 171
`Metadata-Flavor: Google`) - Azure: `http://169.254.169.254/metadata/identity/oauth2/token` - Alibaba/Aliyun: `http://100.100.100.200/` - Network accessline 172
- Azure: `http://169.254.169.254/metadata/identity/oauth2/token` - Network accessline 173
- Alibaba/Aliyun: `http://100.100.100.200/` - Network accessline 187
default landing pages (`/server-status`, `/.git/`, `/.env`, `/phpinfo.php`) - Default creds: only on lab targets - Open redirects: `?next=https://evil.example/` — confirms misuse for - Network accessline 188
- Default creds: only on lab targets - Open redirects: `?next=https://evil.example/` — confirms misuse for phishing chains - Network accessline 189
- Open redirects: `?next=https://evil.example/` — confirms misuse for
optional-skills/security/web-pentest/references/scope-enforcement.md
- Network accessline 42
| Surface | Where the host lives | Example | |---------|----------------------|---------| | `curl URL` | The URL | `curl https://staging.example.com/login` | - Network accessline 43
|---------|----------------------|---------| | `curl URL` | The URL | `curl https://staging.example.com/login` | | `curl --resolve HOST:PORT:ADDR` | HOST | reject — resolve overrides scope | - Network accessline 44
| `curl URL` | The URL | `curl https://staging.example.com/login` | - Network accessline 45
| `curl --resolve HOST:PORT:ADDR` | HOST | reject — resolve overrides scope | - Network accessline 46
| `nmap TARGET` | Each TARGET arg | `nmap 10.0.5.5 staging.example.com` | | `whatweb URL` | The URL | `whatweb https://staging.example.com` | | `browser_exec` `new_tab(url)` / `goto_url(url)` | The URL | python-side: extract host from `url` - Network accessline 47
| `whatweb URL` | The URL | `whatweb https://staging.example.com` | - Network accessline 106
{"ts": "2026-05-25T03:14:15Z", "method": "GET", "url": "https://staging.example.com/api/users", "host": "staging.example.com", "in_scope": true, "phase": "recon", "result_status": 200, "evidence_ref": "evidence/recon.md#endpoints"}
optional-skills/security/web-pentest/references/vuln-taxonomy.md
- Network accessline 20
| `CMD-shell` | `os.system("ls " + v)` | DON'T — refactor to argv list | | `PATH-segment` | `open("/data/" + v)` | Normalize + allowlist + base-relative check | | `URL-host` | redirect to `https://${v}/x` | Allowlist of acceptable hosts | - Network accessline 21
| `PATH-segment` | `open("/data/" + v)` | Normalize + allowlist + base-relative check | | `URL-host` | redirect to `https://${v}/x` | Allowlist of acceptable hosts | | `URL-fetch` | `requests.get(v)` | Allowlist + block private/metadata IPs - Network accessline 22
| `URL-host` | redirect to `https://${v}/x` | Allowlist of acceptable hosts | | `URL-fetch` | `requests.get(v)` | Allowlist + block private/metadata IPs (SSRF) | | `TEMPLATE-string` | `Template("Hello {{ v }}")` | Autoescape ON, no user-con - Network accessline 23
| `URL-fetch` | `requests.get(v)` | Allowlist + block private/metadata IPs (SSRF) |
optional-skills/security/web-pentest/scripts/recon-scan.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 2
# Rate-limited recon scan wrapper for the web-pentest skill. # Wraps nmap + whatweb + curl headers; enforces scope.txt. - Network accessline 3
# Wraps nmap + whatweb + curl headers; enforces scope.txt. - Network accessline 6
# # Example: # recon-scan.sh engagement-20260525-031415 http://127.0.0.1:9119 - Network accessline 8
# recon-scan.sh engagement-20260525-031415 http://127.0.0.1:9119 - Network accessline 91
echo "[recon-scan] fetching headers..." HEADERS_FILE="$EVIDENCE_DIR/headers.txt" curl -sSIk --max-time 15 -A "hermes-pentest/recon" "$TARGET_URL" > "$HEADERS_FILE" || true - Network accessline 92
HEADERS_FILE="$EVIDENCE_DIR/headers.txt" curl -sSIk --max-time 15 -A "hermes-pentest/recon" "$TARGET_URL" > "$HEADERS_FILE" || true sleep 0.2 - Network accessline 93
curl -sSIk --max-time 15 -A "hermes-pentest/recon" "$TARGET_URL" > "$HEADERS_FILE" || true - Network accessline 107
for path in robots.txt sitemap.xml .well-known/security.txt; do outfile="$EVIDENCE_DIR/$(echo "$path" | tr / _).txt" curl -sSk --max-time 10 -A "hermes-pentest/recon" -o "$outfile" -w "%{http_code}\n" "$TARGET_URL/$path" \ - Network accessline 108
outfile="$EVIDENCE_DIR/$(echo "$path" | tr / _).txt" curl -sSk --max-time 10 -A "hermes-pentest/recon" -o "$outfile" -w "%{http_code}\n" "$TARGET_URL/$path" \ > "$outfile.status" || true - Network accessline 109
curl -sSk --max-time 10 -A "hermes-pentest/recon" -o "$outfile" -w "%{http_code}\n" "$TARGET_URL/$path" \
optional-skills/security/web-pentest/templates/authorization.md
optional-skills/security/web-pentest/templates/exploitation-queue.json
optional-skills/security/web-pentest/templates/pentest-report.md
- Network accessline 64
- **CVSS 3.1 base score:** N.N - **CWE:** CWE-XX - **Affected endpoint(s):** `GET https://target.example/api/...` - Network accessline 65
- **CWE:** CWE-XX - **Affected endpoint(s):** `GET https://target.example/api/...` - **Affected parameter(s):** `id` - Network accessline 66
- **Affected endpoint(s):** `GET https://target.example/api/...` - Network accessline 96
curl -sS 'https://target.example/api/items?id=1%27%20OR%201=1--' \ - Network accessline 143
- The application sends to `https://third-party.example/...` — payload
optional-skills/smart-home/openhue/SKILL.md
- Network accessline 9
hermes: tags: [Smart-Home, Hue, Lights, IoT, Automation] homepage: https://www.openhue.io/cli - Network accessline 10
tags: [Smart-Home, Hue, Lights, IoT, Automation] homepage: https://www.openhue.io/cli prerequisites: - Network accessline 11
homepage: https://www.openhue.io/cli - Network accessline 23
# Linux (pre-built binary — releases ship tarballs, not bare binaries) curl -sL "https://github.com/openhue/openhue-cli/releases/latest/download/openhue_Linux_x86_64.tar.gz" \ - Network accessline 24
curl -sL "https://github.com/openhue/openhue-cli/releases/latest/download/openhue_Linux_x86_64.tar.gz" \
optional-skills/social-media/reddit-reading/SKILL.md
- Network accessline 17
comments, and user activity — from a server or headless machine where the normal routes are dead. It does not post, vote, or log in as a user. Idea credit: the per-platform backend routing in [Agent Reach](https://github.com/Panniantong/Age - Network accessline 19
backend routing in [Agent Reach](https://github.com/Panniantong/Agent-Reach). - Network accessline 37
**Optional upgrade (app credentials, still no user login):** for sustained use or full data, register a free "script" type app at https://www.reddit.com/prefs/apps and put its - Network accessline 38
data, register a free "script" type app at https://www.reddit.com/prefs/apps and put its - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 65
python3 scripts/reddit.py sub LocalLLaMA --sort hot --limit 15 python3 scripts/reddit.py search "hermes agent" --sub LocalLLaMA --sort new python3 scripts/reddit.py thread https://www.reddit.com/r/x/comments/abc123/slug/ --limit 40 - Network accessline 66
python3 scripts/reddit.py search "hermes agent" --sub LocalLLaMA --sort new python3 scripts/reddit.py thread https://www.reddit.com/r/x/comments/abc123/slug/ --limit 40 python3 scripts/reddit.py user spez --limit 10 - Network accessline 67
python3 scripts/reddit.py thread https://www.reddit.com/r/x/comments/abc123/slug/ --limit 40 - Access to secretsline 104
[Credential-related source omitted]
optional-skills/social-media/reddit-reading/scripts/reddit.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 6
[Credential-related source omitted] - Access to secretsline 6
[Credential-related source omitted] - Network accessline 7
app-only ``client_credentials`` grant for a free "script" app registered at https://www.reddit.com/prefs/apps. No username, password or cookie is ever used and the script never acts as a user. ~100 requests/minute, full JSON including s - Network accessline 8
https://www.reddit.com/prefs/apps. No username, password or cookie is ever used and - Network accessline 16
python3 reddit.py sub LocalLLaMA [--sort hot|new|top] [--limit N] python3 reddit.py search "hermes agent" [--sub LocalLLaMA] [--sort new] [--limit N] python3 reddit.py thread https://www.reddit.com/r/x/comments/abc123/... [--limit N - Network accessline 17
python3 reddit.py search "hermes agent" [--sub LocalLLaMA] [--sort new] [--limit N] python3 reddit.py thread https://www.reddit.com/r/x/comments/abc123/... [--limit N] python3 reddit.py user spez [--limit N] - Network accessline 18
python3 reddit.py thread https://www.reddit.com/r/x/comments/abc123/... [--limit N] - Network accessline 40
USER_AGENT = "hermes-agent/1.0 (reddit-reading skill; +https://github.com/NousResearch/hermes-agent)" - Network accessline 41
TIMEOUT = 25 ATOM = {"a": "http://www.w3.org/2005/Atom"} WWW = "https://www.reddit.com" - Network accessline 42
ATOM = {"a": "http://www.w3.org/2005/Atom"} - Network accessline 43
WWW = "https://www.reddit.com" - Network accessline 44
OAUTH = "https://oauth.reddit.com" - Network accessline 62
hdrs = {"User-Agent": USER_AGENT, "Accept": "*/*"} hdrs.update(headers or {}) req = urllib.request.Request(url, headers=hdrs) - Network accessline 63
hdrs.update(headers or {}) req = urllib.request.Request(url, headers=hdrs) try: - Network accessline 64
req = urllib.request.Request(url, headers=hdrs) - Network accessline 65
try: with urllib.request.urlopen(req, timeout=TIMEOUT) as resp: return resp.read(), dict(resp.headers) - Network accessline 66
with urllib.request.urlopen(req, timeout=TIMEOUT) as resp: - Access to secretsline 91
[Credential-related source omitted] - Network accessline 96
body = urllib.parse.urlencode({"grant_type": "client_credentials"}).encode() auth = base64.b64encode(f"{cid}:{secret}".encode()).decode() req = urllib.request.Request( - Network accessline 97
auth = base64.b64encode(f"{cid}:{secret}".encode()).decode() req = urllib.request.Request( f"{WWW}/api/v1/access_token", data=body, - Network accessline 98
req = urllib.request.Request( - Access to secretsline 99
[Credential-related source omitted] - Network accessline 100
[Credential-related source omitted] - Network accessline 101
) with urllib.request.urlopen(req, timeout=TIMEOUT) as resp: return json.loads(resp.read())["access_token"] - Network accessline 102
with urllib.request.urlopen(req, timeout=TIMEOUT) as resp: - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 187
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 271
for p in result: score = f" ↑{p['score']}" if p.get("score") is not None else "" nc = f" 💬{p['num_comments']}" if p.get("num_comments") is not None else "" - Network accessline 272
score = f" ↑{p['score']}" if p.get("score") is not None else "" nc = f" 💬{p['num_comments']}" if p.get("num_comments") is not None else "" lines.append(f"- {p.get('title') or p.get('body', '')[:80]}{score}{nc} — u/{p.get('a - Network accessline 273
nc = f" 💬{p['num_comments']}" if p.get("num_comments") is not None else ""
optional-skills/software-development/ast-grep/SKILL.md
- Network accessline 21
Upstream source: vendored from [code-yeongyu/ast-grep-skill](https://github.com/code-yeongyu/ast-grep-skill) (MIT), as shipped in oh-my-openagent's shared-skills bundle. - Network accessline 232
3. **Inspect the parsed pattern**: `sg run -p '<pattern>' --lang <lang> --debug-query=ast --stdin <<< '<sample>'`. If it shows `ERROR` nodes, the pattern is malformed. 4. **Check the AST of the target file**: `sg run -p '$_' --lang <lang> - - Network accessline 234
5. **Try the playground**: <https://ast-grep.github.io/playground.html> — paste code + pattern, see what's happening.
optional-skills/software-development/ast-grep/install.ps1
- Executable scriptline 1
Executable file - Network accessline 155
$asset = "app-$triple.zip" $url = "https://github.com/ast-grep/ast-grep/releases/download/$Version/$asset" - Network accessline 156
$url = "https://github.com/ast-grep/ast-grep/releases/download/$Version/$asset" - Network accessline 232
Err " cargo install ast-grep --locked # any OS with Rust" Err " pip install ast-grep-cli # any OS with Python" Err " https://github.com/ast-grep/ast-grep/releases # manual bin - Network accessline 233
Err " pip install ast-grep-cli # any OS with Python" Err " https://github.com/ast-grep/ast-grep/releases # manual binary" exit 2 - Network accessline 234
Err " https://github.com/ast-grep/ast-grep/releases # manual binary"
optional-skills/software-development/ast-grep/install.sh
- Executable scriptline 1
#!/usr/bin/env bash - Access to secretsline 154
[Credential-related source omitted] - Network accessline 189
command -v curl >/dev/null 2>&1 || command -v wget >/dev/null 2>&1 || { - Network accessline 190
err "need curl or wget for GitHub fallback" - Network accessline 194
ASSET="app-${TRIPLE}.zip" URL="https://github.com/ast-grep/ast-grep/releases/download/${PINNED_VERSION}/${ASSET}" - Network accessline 195
URL="https://github.com/ast-grep/ast-grep/releases/download/${PINNED_VERSION}/${ASSET}" - Dangerous shell operationline 197
trap 'rm -rf "$TMP"' RETURN - Network accessline 199
log "downloading $URL" - Network accessline 200
if command -v curl >/dev/null 2>&1; then - Network accessline 201
curl -fsSL "$URL" -o "$TMP/$ASSET" || return 3 - Network accessline 202
else wget -q "$URL" -O "$TMP/$ASSET" || return 3 fi - Network accessline 203
wget -q "$URL" -O "$TMP/$ASSET" || return 3 - Network accessline 283
err " cargo install ast-grep --locked # any OS with Rust" err " pip install ast-grep-cli # any OS with Python" err " https://github.com/ast-grep/ast-grep/releases # manual binary" - Network accessline 284
err " pip install ast-grep-cli # any OS with Python" err " https://github.com/ast-grep/ast-grep/releases # manual binary" exit 2 - Network accessline 285
err " https://github.com/ast-grep/ast-grep/releases # manual binary"
optional-skills/software-development/ast-grep/references/cli.md
- Network accessline 229
- `references/yaml-rules.md` — rule schema (`pattern`, `kind`, `regex`, `inside`, `has`, `all`, `any`, `not`, `matches`, `transform`, `fix`). - `references/sgconfig.md` — project configuration. - Official: <https://ast-grep.github.io/refere - Network accessline 231
- Official: <https://ast-grep.github.io/reference/cli.html>
optional-skills/software-development/ast-grep/references/install.md
- Dangerous shell operationline 20
sudo port install ast-grep # MacPorts - Network accessline 85
# 1. Pick the right asset for your OS+arch from the latest release: # https://github.com/ast-grep/ast-grep/releases/latest - Network accessline 86
# https://github.com/ast-grep/ast-grep/releases/latest - Network accessline 97
VERSION=0.45.0 TRIPLE=aarch64-apple-darwin curl -fsSL "https://github.com/ast-grep/ast-grep/releases/download/${VERSION}/app-${TRIPLE}.zip" -o ~/.hermes/cache/scratch/ast-grep.zip - Network accessline 98
TRIPLE=aarch64-apple-darwin curl -fsSL "https://github.com/ast-grep/ast-grep/releases/download/${VERSION}/app-${TRIPLE}.zip" -o ~/.hermes/cache/scratch/ast-grep.zip unzip ~/.hermes/cache/scratch/ast-grep.zip -d ~/.hermes/cache/scratch/ast-g - Network accessline 99
curl -fsSL "https://github.com/ast-grep/ast-grep/releases/download/${VERSION}/app-${TRIPLE}.zip" -o ~/.hermes/cache/scratch/ast-grep.zip - Dangerous shell operationline 101
sudo mv ~/.hermes/cache/scratch/ast-grep/ast-grep /usr/local/bin/sg - Dangerous shell operationline 102
sudo chmod +x /usr/local/bin/sg - Network accessline 115
git clone https://github.com/ast-grep/ast-grep.git - Network accessline 145
- **VS Code**: install the [`ast-grep`](https://marketplace.visualstudio.com/items?itemName=ast-grep.ast-grep-vscode) extension. Requires `sgconfig.yml` in workspace root for live diagnostics. - Network accessline 146
- **Neovim**: configure `nvim-lspconfig` with `ast_grep` server, or install [`telescope-ast-grep.nvim`](https://github.com/ray-x/telescope-ast-grep.nvim). - Network accessline 148
- **Emacs**: install [`ast-grep.el`](https://github.com/SunskyXH/ast-grep.el). - Dangerous shell operationline 166
| GitHub binary | `rm <skill_root>/bin/sg` |
optional-skills/software-development/ast-grep/references/patterns.md
- Network accessline 125
The web playground is also fast: <https://ast-grep.github.io/playground.html>. - Network accessline 145
- `references/recipes.md` — copy-paste-ready patterns for TS/JS/Py/Go/Rust. - `references/yaml-rules.md` — `kind`, `regex`, `inside`, `has`, `all`, `any`, `not`, `matches`. - Official: <https://ast-grep.github.io/guide/pattern-syntax.html> - Network accessline 147
- Official: <https://ast-grep.github.io/guide/pattern-syntax.html>
optional-skills/software-development/ast-grep/references/pitfalls.md
- Network accessline 153
Or open <https://ast-grep.github.io/playground.html> and click on a node to see its `kind`. - Dangerous shell operationline 277
ast-grep is great when *the syntactic shape* is what you care about: "find every call to `eval(...)`", "find every `as any`", "find every empty catch block." It is weak for "find every variable that's never used." - Network accessline 285
1. Open <https://ast-grep.github.io/playground.html>.
optional-skills/software-development/ast-grep/references/recipes.md
- Network accessline 400
- `references/yaml-rules.md` — full YAML rule schema (atomic / relational / composite / transform / fix). - `references/cli.md` — `sg run`, `sg scan`, `sg test`, `sg new`. - Official catalog: <https://ast-grep.github.io/catalog/> (community - Network accessline 402
- Official catalog: <https://ast-grep.github.io/catalog/> (community-maintained, browse by language).
optional-skills/software-development/ast-grep/references/sgconfig.md
- Network accessline 235
# yaml-language-server: $schema=https://raw.githubusercontent.com/ast-grep/ast-grep/main/schemas/rule.json - Network accessline 246
- `references/yaml-rules.md` — rule schema (atomic / relational / composite / transform / fix). - `references/cli.md` — `sg scan`, `sg test`, `sg new project`. - Official: <https://ast-grep.github.io/reference/sgconfig.html>, <https://ast-g - Network accessline 248
- Official: <https://ast-grep.github.io/reference/sgconfig.html>, <https://ast-grep.github.io/guide/project/project-config.html>
optional-skills/software-development/ast-grep/references/yaml-rules.md
- Network accessline 7
- <https://ast-grep.github.io/reference/yaml.html> - Network accessline 8
- <https://ast-grep.github.io/reference/rule.html> - Network accessline 9
- <https://ast-grep.github.io/cheatsheet/rule.html> - Network accessline 23
Use a proper logger so we can route logs to stderr in production and silence them in tests. url: https://internal.docs/rules/no-console - Network accessline 25
url: https://internal.docs/rules/no-console - Network accessline 506
- `references/cli.md` — `sg scan`, `sg test`. - `references/sgconfig.md` — project-level configuration. - Official rule reference: <https://ast-grep.github.io/reference/rule.html> - Network accessline 507
- `references/sgconfig.md` — project-level configuration. - Official rule reference: <https://ast-grep.github.io/reference/rule.html> - Cheat sheets: <https://ast-grep.github.io/cheatsheet/rule.html>, <https://ast-grep.github.io/cheatsheet/ - Network accessline 508
- Official rule reference: <https://ast-grep.github.io/reference/rule.html> - Network accessline 509
- Cheat sheets: <https://ast-grep.github.io/cheatsheet/rule.html>, <https://ast-grep.github.io/cheatsheet/yaml.html>
optional-skills/software-development/ast-grep/scripts/ast_grep_helper.py
optional-skills/software-development/ast-grep/tests/smoke.ps1
- Executable scriptline 1
Executable file
optional-skills/software-development/ast-grep/tests/smoke.sh
optional-skills/software-development/code-wiki/SKILL.md
- Network accessline 170
For link targets in local mode use relative paths. For cloned repos use `https://github.com/<owner>/<repo>/blob/<sha>/<path>` so links survive future commits. - Dangerous shell operationline 391
If you cloned to a temp dir, remind the user it can be removed (`rm -rf "$WIKI_TMP"`) after they've reviewed the wiki.
optional-skills/software-development/inspecting-hermes-desktop-dom/SKILL.md
- Network accessline 59
curl -s --max-time 3 http://127.0.0.1:${HERMES_DESKTOP_CDP_PORT:-9222}/json/version - Network accessline 124
cd apps/desktop HERMES_HOME=$HOME/.hermes/cache/scratch/cdp-probe-home \ HERMES_DESKTOP_DEV_SERVER=http://127.0.0.1:5174 \ - Network accessline 125
HERMES_HOME=$HOME/.hermes/cache/scratch/cdp-probe-home HERMES_DESKTOP_DEV_SERVER=http://127.0.0.1:5174 - Network accessline 126
HERMES_DESKTOP_DEV_SERVER=http://127.0.0.1:5174 \
optional-skills/software-development/node-inspect-debugger/SKILL.md
- Network accessline 214
# 3. Find the WS URL curl -s http://127.0.0.1:9229/json/list | jq -r '.[0].webSocketDebuggerUrl' - Network accessline 215
curl -s http://127.0.0.1:9229/json/list | jq -r '.[0].webSocketDebuggerUrl' - Network accessline 270
curl -s http://127.0.0.1:9229/json/list # lists all inspectable targets on the host - Network accessline 285
- [ ] `curl -s http://127.0.0.1:9229/json/list` returns exactly the target you expect
optional-skills/software-development/pr-lens/SKILL.md
- Network accessline 11
category: software-development related_skills: [] upstream: https://github.com/coldteadotai/pr-lens (pinned 0993b4d) - Network accessline 12
related_skills: [] upstream: https://github.com/coldteadotai/pr-lens (pinned 0993b4d) --- - Network accessline 13
upstream: https://github.com/coldteadotai/pr-lens (pinned 0993b4d) - Network accessline 62
[Credential-related source omitted] - Network accessline 145
Adapted from [coldteadotai/pr-lens](https://github.com/coldteadotai/pr-lens) (packages/agent-skill, pinned 0993b4d), MIT License, Copyright (c) 2026 Coldtea AI. See LICENSE.txt.
optional-skills/software-development/pr-lens/references/config.md
- Network accessline 29
{ "$ref": "https://unpkg.com/@coldtea/pr-lens-schema/json-schema/config.schema.json" }
optional-skills/software-development/pr-lens/references/example.graph.json
- Network accessline 26
"number": 128, "title": "Send broadcasts in batches of 500", "url": "https://github.com/ohansemmanuel/bestregards/pull/128" - Network accessline 27
"title": "Send broadcasts in batches of 500", "url": "https://github.com/ohansemmanuel/bestregards/pull/128" }, - Network accessline 28
"url": "https://github.com/ohansemmanuel/bestregards/pull/128"
optional-skills/software-development/pr-lens/references/graph-document.md
- Network accessline 7
The validator enforces the same thing from a JSON Schema, published at `https://unpkg.com/@coldtea/pr-lens-schema/json-schema/graph-doc.schema.json` if you want it machine-readable.
optional-skills/software-development/python-debugpy/SKILL.md
- Network accessline 147
For Hermes, use a separate development checkout and data home, not a live production generation. Follow the [PM developer workflow](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow) - Network accessline 148
production generation. Follow the [PM developer workflow](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow) and activate that checkout — PowerShell: `. .\activate.ps1`. The declared `dev` - Network accessline 149
[PM developer workflow](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow) - Dangerous shell operationline 206
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope - Network accessline 219
HOST, PORT = "127.0.0.1", 5678 s = socket.create_connection((HOST, PORT)) - Network accessline 220
s = socket.create_connection((HOST, PORT)) - Network accessline 284
nc 127.0.0.1 4444 - Network accessline 316
Trigger the matching slash command from the TUI, then `nc 127.0.0.1 4444` in another terminal. - Network accessline 384
Trigger the handler. `nc 127.0.0.1 4444`, then `w` to see the suspended frame, `!import asyncio; asyncio.all_tasks()` to see what else is pending.
optional-skills/software-development/rest-graphql-debug/SKILL.md
- Network accessline 49
# Verbose request/response exchange terminal('curl -v https://api.example.com/users/1') - Network accessline 50
terminal('curl -v https://api.example.com/users/1') - Network accessline 52
# POST with JSON terminal("""curl -X POST https://api.example.com/users \\ - Network accessline 53
terminal("""curl -X POST https://api.example.com/users \\ - Network accessline 58
# Headers only terminal('curl -sI https://api.example.com/health') - Network accessline 59
terminal('curl -sI https://api.example.com/health') - Network accessline 61
# Pretty-print JSON terminal('curl -s https://api.example.com/users | python -m json.tool') - Dangerous shell operationline 62
terminal('curl -s https://api.example.com/users | python -m json.tool') - Network accessline 62
terminal('curl -s https://api.example.com/users | python -m json.tool') - Network accessline 68
terminal("""curl -X POST https://api.example.com/graphql \\ - Network accessline 77
execute_code(''' import os, requests resp = requests.post( - Network accessline 78
import os, requests resp = requests.post( "https://api.example.com/graphql", - Network accessline 79
resp = requests.post( - Network accessline 80
"https://api.example.com/graphql", - Access to secretsline 82
[Credential-related source omitted] - Network accessline 96
execute_code(''' import requests resp = requests.get( - Network accessline 97
import requests resp = requests.get( "https://api.example.com/users/1", - Network accessline 98
resp = requests.get( - Network accessline 99
"https://api.example.com/users/1", - Network accessline 113
terminal('nslookup api.example.com') terminal('curl -v --connect-timeout 5 https://api.example.com/health') - Network accessline 114
terminal('curl -v --connect-timeout 5 https://api.example.com/health') - Network accessline 124
terminal('''curl -w "dns:%{time_namelookup}s connect:%{time_connect}s tls:%{time_appconnect}s ttfb:%{time_starttransfer}s total:%{time_total}s\\n" \\ - Network accessline 125
-o /dev/null -s https://api.example.com/endpoint''') - Network accessline 133
from requests.exceptions import ConnectTimeout, ReadTimeout try: requests.get(url, timeout=(3.05, 30)) - Network accessline 134
try: requests.get(url, timeout=(3.05, 30)) except ConnectTimeout: - Network accessline 135
requests.get(url, timeout=(3.05, 30)) - Network accessline 148
terminal('curl -vI https://api.example.com 2>&1 | grep -E "SSL|subject|expire|issuer"') - Network accessline 156
[Credential-related source omitted] - Network accessline 157
[Credential-related source omitted] - Access to secretsline 162
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Network accessline 178
terminal("""curl -v -X POST https://api.example.com/endpoint \\ - Network accessline 186
# WRONG — data= sends form-encoded, header lies requests.post(url, data='{"k":"v"}', headers={"Content-Type": "application/json"}) - Network accessline 187
requests.post(url, data='{"k":"v"}', headers={"Content-Type": "application/json"}) - Network accessline 189
# RIGHT — json= auto-sets header AND serializes requests.post(url, json={"k": "v"}) - Network accessline 190
requests.post(url, json={"k": "v"}) - Network accessline 192
# WRONG — Accept says XML, code calls .json() requests.get(url, headers={"Accept": "text/xml"}) - Network accessline 193
requests.get(url, headers={"Accept": "text/xml"}) - Network accessline 195
# RIGHT — let requests build multipart with boundary requests.post(url, files={"file": open("doc.pdf", "rb")}) - Network accessline 196
requests.post(url, files={"file": open("doc.pdf", "rb")}) - Network accessline 206
execute_code(''' import requests resp = requests.post(url, json=payload, timeout=10) - Network accessline 207
import requests resp = requests.post(url, json=payload, timeout=10) print(f"status={resp.status_code}") - Network accessline 208
resp = requests.post(url, json=payload, timeout=10) - Network accessline 234
1. `Authorization` header actually present? (`curl -v` to confirm) - Access to secretsline 237
[Credential-related source omitted] - Network accessline 274
def with_backoff(method, url, **kwargs): for attempt in range(5): resp = requests.request(method, url, **kwargs) - Network accessline 275
for attempt in range(5): resp = requests.request(method, url, **kwargs) if resp.status_code != 429: - Network accessline 276
resp = requests.request(method, url, **kwargs) - Network accessline 320
resp = requests.get(f"{BASE}/users/1", headers=HEADERS, timeout=10) - Network accessline 334
execute_code(''' import requests resp = requests.post(url, json=payload, headers=headers, timeout=10) - Network accessline 335
import requests resp = requests.post(url, json=payload, headers=headers, timeout=10) request_id = ( - Network accessline 336
resp = requests.post(url, json=payload, headers=headers, timeout=10) - Network accessline 354
Expected: 201 with order object Actual: 500 {"error":"internal server error"} Repro: curl -X POST … (auth: <REDACTED>) - Network accessline 356
Repro: curl -X POST … (auth: <REDACTED>) - Network accessline 366
[Credential-related source omitted] - Access to secretsline 366
[Credential-related source omitted] - Access to secretsline 367
[Credential-related source omitted] - Network accessline 370
class TestAPISmoke: def test_health(self): resp = requests.get(f"{BASE_URL}/health", timeout=5) - Network accessline 371
def test_health(self): resp = requests.get(f"{BASE_URL}/health", timeout=5) assert resp.status_code == 200 - Network accessline 372
resp = requests.get(f"{BASE_URL}/health", timeout=5) - Network accessline 375
def test_list_users_returns_array(self): resp = requests.get(f"{BASE_URL}/users", headers=HEADERS, timeout=10) - Network accessline 376
resp = requests.get(f"{BASE_URL}/users", headers=HEADERS, timeout=10) - Network accessline 381
def test_get_user_required_fields(self): resp = requests.get(f"{BASE_URL}/users/1", headers=HEADERS, timeout=10) - Network accessline 382
resp = requests.get(f"{BASE_URL}/users/1", headers=HEADERS, timeout=10) - Network accessline 388
def test_invalid_auth_returns_401(self): resp = requests.get( - Network accessline 389
resp = requests.get( - Access to secretsline 401
[Credential-related source omitted] - Network accessline 426
terminal('curl -sI https://api.example.com') - Network accessline 438
[Credential-related source omitted] - Access to secretsline 438
[Credential-related source omitted] - Network accessline 439
base = "https://api.example.com" - Network accessline 442
# 1. auth me = requests.get(f"{base}/me", headers=H, timeout=10) - Network accessline 443
me = requests.get(f"{base}/me", headers=H, timeout=10) - Network accessline 448
while True: params = {"cursor": cursor} if cursor else {} r = requests.get(f"{base}/users", headers=H, params=params, timeout=10) - Network accessline 449
params = {"cursor": cursor} if cursor else {} r = requests.get(f"{base}/users", headers=H, params=params, timeout=10) body = r.json() - Network accessline 450
r = requests.get(f"{base}/users", headers=H, params=params, timeout=10) - Network accessline 465
web_extract(urls=["https://docs.example.com/api/v1/users"]) - Network accessline 473
context=""" Follow the rest-graphql-debug skill (optional-skills/software-development/rest-graphql-debug). Base URL: https://api.example.com - Network accessline 474
Follow the rest-graphql-debug skill (optional-skills/software-development/rest-graphql-debug). Base URL: https://api.example.com Auth: Bearer token from API_TOKEN env var. - Network accessline 475
Base URL: https://api.example.com - Network accessline 479
- happy path: assert status + response schema - error cases: 400, 404, 422 - log a repro curl for any failure (redact tokens) - Network accessline 481
- log a repro curl for any failure (redact tokens) - Network accessline 499
## Repro curl -X POST https://api.example.com/api/v1/users \ - Network accessline 500
curl -X POST https://api.example.com/api/v1/users \
optional-skills/software-development/subagent-driven-development/references/context-budget-discipline.md
- Network accessline 5
Adapted from the GSD (Get Shit Done) project's context-budget reference — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)).
optional-skills/software-development/subagent-driven-development/references/gates-taxonomy.md
- Network accessline 5
Adapted from the GSD (Get Shit Done) project's gates reference — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)).
optional-skills/web-development/cloudflare-temporary-deploy/SKILL.md
- Access to secretsline 39
[Credential-related source omitted] - Network accessline 60
export default { async fetch(): Promise<Response> { - Network accessline 61
async fetch(): Promise<Response> { - Network accessline 81
curl -sS <live_url> - Network accessline 93
| Deploy (no account) | `npx wrangler@latest deploy --temporary` | | Deploy + parse URLs | `npx wrangler@latest deploy --temporary 2>&1 \| python scripts/parse_deploy_output.py` | | Verify live | `curl -sS <live_url>` | - Network accessline 94
| Deploy + parse URLs | `npx wrangler@latest deploy --temporary 2>&1 \| python scripts/parse_deploy_output.py` | | Verify live | `curl -sS <live_url>` | | Clear cached temp account | `npx wrangler@latest logout` | - Network accessline 95
| Verify live | `curl -sS <live_url>` | - Access to secretsline 115
[Credential-related source omitted] - Network accessline 123
- `npx wrangler@latest --version` returns `>= 4.102.0`. - `npx wrangler@latest deploy --temporary` prints a `workers.dev` live URL and a `claim-preview?claimToken=` claim URL. - `curl -sS <live_url>` returns the exact body the Worker code p - Network accessline 124
- `npx wrangler@latest deploy --temporary` prints a `workers.dev` live URL and a `claim-preview?claimToken=` claim URL. - `curl -sS <live_url>` returns the exact body the Worker code produces. - A second deploy reports `Account: <name> (reu - Network accessline 125
- `curl -sS <live_url>` returns the exact body the Worker code produces.
optional-skills/web-development/cloudflare-temporary-deploy/scripts/parse_deploy_output.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 20
_LIVE_URL = re.compile(r"https://[A-Za-z0-9._-]+\.workers\.dev\S*") - Network accessline 23
_CLAIM_URL = re.compile(r"https://\S*claim\S*claimToken=\S+", re.IGNORECASE) - Network accessline 62
Account: example-name (created) Claim within: 60 minutes Claim URL: https://dash.cloudflare.com/claim-preview?claimToken=abc123XYZ - Network accessline 64
Claim URL: https://dash.cloudflare.com/claim-preview?claimToken=abc123XYZ - Network accessline 66
Uploaded example-worker Deployed example-worker triggers https://example-worker.example-name.workers.dev - Network accessline 67
Deployed example-worker triggers https://example-worker.example-name.workers.dev """ - Network accessline 68
https://example-worker.example-name.workers.dev - Network accessline 73
Account: example-name (reused) Claim within: 42 minutes Claim URL: https://dash.cloudflare.com/claim-preview?claimToken=def456 - Network accessline 74
Claim within: 42 minutes Claim URL: https://dash.cloudflare.com/claim-preview?claimToken=def456 Deployed example-worker triggers - Network accessline 75
Claim URL: https://dash.cloudflare.com/claim-preview?claimToken=def456 - Network accessline 76
Deployed example-worker triggers https://example-worker.example-name.workers.dev """ - Network accessline 77
https://example-worker.example-name.workers.dev - Network accessline 87
def _selftest() -> int: r = parse(_SAMPLE) assert r["live_url"] == "https://example-worker.example-name.workers.dev", r - Network accessline 88
r = parse(_SAMPLE) assert r["live_url"] == "https://example-worker.example-name.workers.dev", r assert r["claim_url"] == "https://dash.cloudflare.com/claim-preview?claimToken=abc123XYZ", r - Network accessline 89
assert r["live_url"] == "https://example-worker.example-name.workers.dev", r - Network accessline 90
assert r["claim_url"] == "https://dash.cloudflare.com/claim-preview?claimToken=abc123XYZ", r
optional-skills/web-development/har-derived-api-client/SKILL.md
- Network accessline 77
# 1a. Capture, LOCAL browser (Hermes launched it) python3 scripts/har_capture.py "https://SITE/" out.har \ - Network accessline 78
python3 scripts/har_capture.py "https://SITE/" out.har \ - Network accessline 82
# get the endpoint from /browser connect or BROWSER_CDP_URL python3 scripts/har_capture_cdp.py "ws://HOST/devtools/browser/..." out.har \ --goto "https://SITE/" --action "fill:input[name=search]:my query" \ - Network accessline 83
python3 scripts/har_capture_cdp.py "ws://HOST/devtools/browser/..." out.har --goto "https://SITE/" --action "fill:input[name=search]:my query" - Network accessline 84
--goto "https://SITE/" --action "fill:input[name=search]:my query" \ - Access to secretsline 109
[Credential-related source omitted] - Network accessline 125
import requests r = requests.get( - Network accessline 126
r = requests.get( - Network accessline 127
"https://en.wikipedia.org/w/rest.php/v1/search/title", - Network accessline 155
python3 scripts/har_capture.py "https://en.wikipedia.org/wiki/Main_Page" ~/.hermes/cache/scratch/wiki.har \ - Network accessline 160
Expect the derivation to print `GET https://en.wikipedia.org/w/rest.php/v1/search/title`
optional-skills/web-development/har-derived-api-client/scripts/har_capture.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/web-development/har-derived-api-client/scripts/har_capture_cdp.py
- Executable scriptline 1
#!/usr/bin/env python3
optional-skills/web-development/har-derived-api-client/scripts/har_to_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 11
[Credential-related source omitted] - Network accessline 124
for (method, host, path), g in groups.items(): print(f"\n=== {method} https://{host}{path} (x{g['count']})") - Network accessline 125
print(f"\n=== {method} https://{host}{path} (x{g['count']})")
optional-skills/web-development/page-agent/SKILL.md
- Network accessline 16
alibaba/page-agent (https://github.com/alibaba/page-agent, 17k+ stars, MIT) is an in-page GUI agent written in TypeScript. It lives inside a webpage, reads the DOM as text (no screenshots, no multi-modal LLM), and executes natural-language - Network accessline 47
<script src="https://cdn.jsdelivr.net/npm/page-agent@1.8.0/dist/iife/page-agent.demo.js" crossorigin="true"></script> - Network accessline 55
javascript:(function(){var s=document.createElement('script');s.src='https://cdn.jsdelivr.net/npm/page-agent@1.8.0/dist/iife/page-agent.demo.js';document.head.appendChild(s);})(); - Network accessline 71
const agent = new PageAgent({ model: 'qwen3.5-plus', baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1', - Network accessline 72
[Credential-related source omitted] - Network accessline 73
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1', - Access to secretsline 74
[Credential-related source omitted] - Network accessline 87
| Provider | `baseURL` | `model` | |----------|-----------|---------| | Qwen / DashScope | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `qwen3.5-plus` | - Network accessline 88
|----------|-----------|---------| | Qwen / DashScope | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `qwen3.5-plus` | | OpenAI | `https://api.openai.com/v1` | `gpt-4o-mini` | - Network accessline 89
| Qwen / DashScope | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `qwen3.5-plus` | - Network accessline 90
| OpenAI | `https://api.openai.com/v1` | `gpt-4o-mini` | - Network accessline 91
| Ollama (local) | `http://localhost:11434/v1` | `qwen3:14b` | - Network accessline 92
| OpenRouter | `https://openrouter.ai/api/v1` | `anthropic/claude-sonnet-4.6` | - Network accessline 96
- `model`, `baseURL`, `apiKey` — LLM connection - `language` — UI language (`en-US`, `zh-CN`, etc.) - Allowlist and data-masking hooks exist for locking down what the agent can touch — see https://alibaba.github.io/page-agent/ for the full - Network accessline 98
- Allowlist and data-masking hooks exist for locking down what the agent can touch — see https://alibaba.github.io/page-agent/ for the full option list - Network accessline 107
git clone https://github.com/alibaba/page-agent.git - Network accessline 115
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Network accessline 117
LLM_BASE_URL=https://api.openai.com/v1 - Network accessline 123
LLM_BASE_URL=http://localhost:11434/v1 - Access to secretsline 124
[Credential-related source omitted] - Network accessline 131
npm start # docs/website dev server npm run build # build every package npm run dev:demo # serve IIFE bundle at http://localhost:5174/page-agent.demo.js - Network accessline 132
npm run build # build every package npm run dev:demo # serve IIFE bundle at http://localhost:5174/page-agent.demo.js npm run dev:ext # develop the browser extension (WXT + React) - Network accessline 133
npm run dev:demo # serve IIFE bundle at http://localhost:5174/page-agent.demo.js - Network accessline 141
javascript:(function(){var s=document.createElement('script');s.src=`http://localhost:5174/page-agent.demo.js?t=${Math.random()}`;s.onload=()=>console.log('PageAgent ready!');document.head.appendChild(s);})(); - Access to secretsline 146
[Credential-related source omitted] - Network accessline 171
After Path 3: 1. `npm run dev:demo` prints `Accepting connections at http://localhost:5174` 2. `curl -I http://localhost:5174/page-agent.demo.js` returns `HTTP/1.1 200 OK` with `Content-Type: application/javascript` - Network accessline 172
1. `npm run dev:demo` prints `Accepting connections at http://localhost:5174` 2. `curl -I http://localhost:5174/page-agent.demo.js` returns `HTTP/1.1 200 OK` with `Content-Type: application/javascript` 3. Click the bookmarklet on any site; - Network accessline 173
2. `curl -I http://localhost:5174/page-agent.demo.js` returns `HTTP/1.1 200 OK` with `Content-Type: application/javascript` - Network accessline 188
- Repo: https://github.com/alibaba/page-agent - Network accessline 189
- Docs: https://alibaba.github.io/page-agent/
optional-skills/web-development/publish-site/SKILL.md
- Network accessline 52
|---|---| | Local preview | `python3 -m http.server 8080 --directory dist` | | Shareable preview | `cloudflared tunnel --url http://localhost:8080` | - Network accessline 53
| Local preview | `python3 -m http.server 8080 --directory dist` | | Shareable preview | `cloudflared tunnel --url http://localhost:8080` | | Version a deploy | `git add -A && git commit -m "deploy: <what>" && git tag deploy-YYYYMMDD-HHMM` - Network accessline 54
| Shareable preview | `cloudflared tunnel --url http://localhost:8080` | - Network accessline 59
| Netlify | `netlify deploy --prod --dir dist` | | Rollback | `git checkout <previous-tag> -- . && redeploy` (or provider dashboard) | | Verify live | `curl -sS -o /dev/null -w '%{http_code}' <url>` → expect `200` | - Network accessline 61
| Verify live | `curl -sS -o /dev/null -w '%{http_code}' <url>` → expect `200` | - Network accessline 76
cloudflared tunnel --url http://localhost:8080 - Network accessline 79
Give the user the `https://*.trycloudflare.com` URL and get sign-off before deploying. Kill the tunnel afterwards. - Network accessline 152
1. `curl -sS -o /dev/null -w '%{http_code}' <live-url>` returns `200` (retry over ~2 minutes for a first GitHub Pages deploy). - Network accessline 153
2. `curl -sS <live-url> | head -30` shows the expected `index.html` content — optionally confirm markup with `web_extract` on the live URL. - Network accessline 154
3. For SPAs, also curl one deep route (e.g. `/about`) and confirm it returns `200`, not `404`.
optional-skills/web-development/scrollcraft/SKILL.md
- Network accessline 10
tags: [web-development, landing-page, scrollytelling, animation, design, frontend] category: web-development homepage: https://github.com/nateherkai/scroll-craft - Network accessline 11
category: web-development homepage: https://github.com/nateherkai/scroll-craft related_skills: [] - Network accessline 12
homepage: https://github.com/nateherkai/scroll-craft - Access to secretsline 145
[Credential-related source omitted] - Network accessline 185
cd <build project> && npm i playwright-core # once node <skill>/scripts/serve.mjs --root . --port 4500 & node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/shots - Network accessline 186
node <skill>/scripts/serve.mjs --root . --port 4500 & node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/shots node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/mobile --width 390 --height 844 - Network accessline 187
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/shots - Network accessline 188
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/mobile --width 390 --height 844 - Network accessline 189
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/reduced --reduced-motion - Access to secretsline 233
[Credential-related source omitted]
optional-skills/web-development/scrollcraft/engine/scrollcraft.css
- Network accessline 381
.sc-grain::before { content: ""; position: absolute; inset: -50%; background-image: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='140' height='140'><filter id='n'><feTurbulence type='fractalNoise' baseFrequ - Network accessline 382
content: ""; position: absolute; inset: -50%; background-image: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='140' height='140'><filter id='n'><feTurbulence type='fractalNoise' baseFrequency='0.85' numOctaves - Network accessline 383
background-image: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='140' height='140'><filter id='n'><feTurbulence type='fractalNoise' baseFrequency='0.85' numOctaves='3'/></filter><rect width='140' height='140' fi
optional-skills/web-development/scrollcraft/engine/scrollcraft.js
- Executable scriptline 1
Executable file - Network accessline 589
if (!src) return; V.loading = true; fetch(src).then(function (r) { if (!r.ok) throw new Error(r.status); return r.blob(); }) - Network accessline 590
V.loading = true; fetch(src).then(function (r) { if (!r.ok) throw new Error(r.status); return r.blob(); }) .then(function (blob) { - Network accessline 591
fetch(src).then(function (r) { if (!r.ok) throw new Error(r.status); return r.blob(); })
optional-skills/web-development/scrollcraft/references/device-diag.html
optional-skills/web-development/scrollcraft/references/template.html
- Network accessline 20
<title>BRAND · the one-line promise</title> <meta name="description" content="One sentence a search result would show."> <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'><rect width='32' - Network accessline 21
<meta name="description" content="One sentence a search result would show."> <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'><rect width='32' height='32' rx='7' fill='%230A0807'/></svg>" - Network accessline 22
<link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'><rect width='32' height='32' rx='7' fill='%230A0807'/></svg>">
optional-skills/web-development/scrollcraft/references/verify.md
- Network accessline 11
node <skill>/scripts/serve.mjs --root . --port 4500 & node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/shots - Network accessline 12
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/shots - Network accessline 13
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/mobile --width 390 --height 844 - Network accessline 14
node <skill>/scripts/shoot.mjs --url http://localhost:4500 --out lab/reduced --reduced-motion - Network accessline 376
curl -s http://localhost:45XX | grep -o "<title>.*</title>" - Network accessline 377
curl -s -o /dev/null -w "%{http_code} - Network accessline 378
" http://localhost:45XX/assets/leg01.mp4
optional-skills/web-development/scrollcraft/references/worldflight.md
- Network accessline 333
node scripts/serve.mjs --root builds/<name> --port 45XX node scripts/shoot.mjs --url http://localhost:45XX --out lab/<name>-shots --per-act 8 - Network accessline 334
node scripts/shoot.mjs --url http://localhost:45XX --out lab/<name>-shots --per-act 8 - Network accessline 335
node scripts/shoot.mjs --url http://localhost:45XX --out lab/<name>-reduced --reduced-motion - Network accessline 345
node <skill>/scripts/worldflight-assert.mjs --url http://localhost:45XX
optional-skills/web-development/scrollcraft/scripts/doctor.mjs
- Executable scriptline 1
#!/usr/bin/env node - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 130
[Credential-related source omitted] - Access to secretsline 138
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted]
optional-skills/web-development/scrollcraft/scripts/encode.sh
- Executable scriptline 1
#!/usr/bin/env bash
optional-skills/web-development/scrollcraft/scripts/kie.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 3
* scrollcraft asset generator: kie.ai unified jobs API. * * POST https://api.kie.ai/api/v1/jobs/createTask { model, input } - Network accessline 4
* * POST https://api.kie.ai/api/v1/jobs/createTask { model, input } * GET https://api.kie.ai/api/v1/jobs/recordInfo?taskId=... - Network accessline 5
* POST https://api.kie.ai/api/v1/jobs/createTask { model, input } - Network accessline 6
* GET https://api.kie.ai/api/v1/jobs/recordInfo?taskId=... - Access to secretsline 20
[Credential-related source omitted] - Network accessline 26
const API = "https://api.kie.ai"; - Network accessline 27
const UPLOAD = "https://kieai.redpandaai.co/api/file-base64-upload"; - Access to secretsline 39
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Network accessline 67
const mime = ext === "jpg" ? "image/jpeg" : `image/${ext}`; const dataUrl = `data:${mime};base64,${fs.readFileSync(abs).toString("base64")}`; const res = await fetch(UPLOAD, { - Network accessline 68
const dataUrl = `data:${mime};base64,${fs.readFileSync(abs).toString("base64")}`; const res = await fetch(UPLOAD, { method: "POST", headers: H, - Network accessline 69
const res = await fetch(UPLOAD, { - Network accessline 82
async function createTask(model, input) { const res = await fetch(`${API}/api/v1/jobs/createTask`, { - Network accessline 83
const res = await fetch(`${API}/api/v1/jobs/createTask`, { - Network accessline 94
for (;;) { if (Date.now() - t0 > timeoutMs) throw new Error(`${label}: timed out after ${Math.round((Date.now() - t0) / 1000)}s`); const res = await fetch(`${API}/api/v1/jobs/recordInfo?taskId=${encodeURIComponent(taskId)}`, { heade - Network accessline 95
if (Date.now() - t0 > timeoutMs) throw new Error(`${label}: timed out after ${Math.round((Date.now() - t0) / 1000)}s`); const res = await fetch(`${API}/api/v1/jobs/recordInfo?taskId=${encodeURIComponent(taskId)}`, { headers: H }); c - Network accessline 96
const res = await fetch(`${API}/api/v1/jobs/recordInfo?taskId=${encodeURIComponent(taskId)}`, { headers: H }); - Network accessline 116
async function download(url, out) { fs.mkdirSync(path.dirname(path.resolve(out)), { recursive: true }); const res = await fetch(url); - Network accessline 117
fs.mkdirSync(path.dirname(path.resolve(out)), { recursive: true }); const res = await fetch(url); if (!res.ok) throw new Error(`download ${res.status} ${url}`); - Network accessline 118
const res = await fetch(url); - Network accessline 137
try { if (cmd === "probe") { const r = await fetch(`${API}/api/v1/chat/credit`, { headers: H }); - Network accessline 138
if (cmd === "probe") { const r = await fetch(`${API}/api/v1/chat/credit`, { headers: H }); const j = await r.json(); - Network accessline 139
const r = await fetch(`${API}/api/v1/chat/credit`, { headers: H });
optional-skills/web-development/scrollcraft/scripts/serve.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 49
fs.createReadStream(file).pipe(res); }).listen(PORT, () => { console.log(`scrollcraft: ${ROOT}\n http://localhost:${PORT}`); - Network accessline 50
}).listen(PORT, () => { console.log(`scrollcraft: ${ROOT}\n http://localhost:${PORT}`); }); - Network accessline 51
console.log(`scrollcraft: ${ROOT}\n http://localhost:${PORT}`);
optional-skills/web-development/scrollcraft/scripts/shoot.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 9
* frame the transition may not survive. * * node shoot.mjs --url http://localhost:4500 --out lab/shots --steps 12 - Network accessline 10
* * node shoot.mjs --url http://localhost:4500 --out lab/shots --steps 12 * node shoot.mjs --url ... --width 375 --height 812 --out lab/mobile - Network accessline 11
* node shoot.mjs --url http://localhost:4500 --out lab/shots --steps 12 - Network accessline 38
const URL = arg("--url", "http://localhost:4500");
optional-skills/web-development/scrollcraft/scripts/workspace.mjs
- Executable scriptline 1
#!/usr/bin/env node
optional-skills/web-development/scrollcraft/scripts/worldflight-assert.mjs
- Executable scriptline 1
#!/usr/bin/env node - Network accessline 4
* claims the mode makes, checked one at a time. * * node lab/worldflight-assert.mjs --url http://localhost:4520 - Network accessline 5
* * node lab/worldflight-assert.mjs --url http://localhost:4520 */ - Network accessline 6
* node lab/worldflight-assert.mjs --url http://localhost:4520 - Network accessline 13
const argv = process.argv.slice(2); const arg = (n, d) => { const i = argv.indexOf(n); return i > -1 && argv[i + 1] ? argv[i + 1] : d; }; const URL = arg("--url", "http://localhost:4520"); - Network accessline 15
const URL = arg("--url", "http://localhost:4520");
plugins/google_meet/README.md
- Network accessline 71
hermes meet setup # preflight hermes meet auth # optional hermes meet join https://meet.google.com/abc-defg-hij # transcribe - Network accessline 73
hermes meet join https://meet.google.com/abc-defg-hij # transcribe - Network accessline 80
[Credential-related source omitted] - Network accessline 81
[Credential-related source omitted] - Access to secretsline 81
[Credential-related source omitted] - Network accessline 82
hermes meet join https://meet.google.com/abc-defg-hij --mode realtime - Network accessline 90
[Credential-related source omitted] - Access to secretsline 91
[Credential-related source omitted] - Network accessline 92
hermes meet join https://meet.google.com/abc-defg-hij --mode realtime - Network accessline 125
- URL gate: only `https://meet.google.com/abc-defg-hij`, `/new`, `/lookup/<id>`.
plugins/google_meet/SKILL.md
- Dangerous shell operationline 53
`hermes meet install --realtime` prompts before running `sudo apt-get` (Linux) - Dangerous shell operationline 60
# Linux: sudo apt install pulseaudio-utils - Access to secretsline 63
[Credential-related source omitted] - Network accessline 142
- URL regex: only `https://meet.google.com/...` URLs pass.
plugins/google_meet/__init__.py
- Executable scriptline 1
Executable file - Network accessline 3
Headless Chromium (Playwright) joins the URL, enables live captions and scrapes them into a transcript. Realtime mode adds agent speech (OpenAI Realtime + virtual audio device); remote nodes run the bot on another machine. Explicit-by-desig - Network accessline 4
transcript. Realtime mode adds agent speech (OpenAI Realtime + virtual audio device); remote nodes run the bot on another machine. Explicit-by-design: only joins ``https://meet.google.com/`` URLs passed in — no calendar scanning, auto-dial - Network accessline 5
nodes run the bot on another machine. Explicit-by-design: only joins ``https://meet.google.com/``
plugins/google_meet/_jsonfile.py
- Executable scriptline 1
Executable file
plugins/google_meet/audio_bridge.py
- Executable scriptline 1
Executable file
plugins/google_meet/cli.py
- Executable scriptline 1
Executable file - Network accessline 55
p["install"].add_argument("--yes", "-y", action="store_true", help="Answer yes to all prompts (use with care; will run sudo apt-get or brew without asking).") p["join"].add_argument("url", help="https://mee - Dangerous shell operationline 56
help="Answer yes to all prompts (use with care; will run sudo apt-get or brew without asking).") - Network accessline 56
help="Answer yes to all prompts (use with care; will run sudo apt-get or brew without asking).") p["join"].add_argument("url", help="https://meet.google.com/...") p["join"].add_argument("--guest-name", default="Hermes Agent") - Network accessline 57
p["join"].add_argument("url", help="https://meet.google.com/...") - Network accessline 117
print() all_ok = system_ok and pw_ok and chromium_ok print("ready. Join a meeting: hermes meet join https://meet.google.com/abc-defg-hij" if all_ok - Network accessline 118
all_ok = system_ok and pw_ok and chromium_ok print("ready. Join a meeting: hermes meet join https://meet.google.com/abc-defg-hij" if all_ok else "not ready yet — fix the items above.") - Network accessline 119
print("ready. Join a meeting: hermes meet join https://meet.google.com/abc-defg-hij" if all_ok - Dangerous shell operationline 169
_install_pkgs(" install pulseaudio-utils? this runs `sudo apt-get install -y pulseaudio-utils`", - Network accessline 182
elif not shutil.which("brew"): print(" missing: " + ", ".join(needs) + "\n" " install Homebrew first (https://brew.sh) or install the packages manually.") - Network accessline 183
print(" missing: " + ", ".join(needs) + "\n" " install Homebrew first (https://brew.sh) or install the packages manually.") else: - Network accessline 184
" install Homebrew first (https://brew.sh) or install the packages manually.") - Network accessline 210
) context = browser.new_context() context.new_page().goto("https://accounts.google.com/", wait_until="domcontentloaded") - Network accessline 211
context = browser.new_context() context.new_page().goto("https://accounts.google.com/", wait_until="domcontentloaded") with contextlib.suppress(EOFError): - Network accessline 212
context.new_page().goto("https://accounts.google.com/", wait_until="domcontentloaded")
plugins/google_meet/meet_bot.py
- Executable scriptline 1
Executable file - Network accessline 29
MEET_URL_RE = re.compile( r"^https://meet\.google\.com/([a-z0-9]{3,}-[a-z0-9]{3,}-[a-z0-9]{3,}|lookup/[^/?#]+|new)" - Network accessline 30
r"^https://meet\.google\.com/([a-z0-9]{3,}-[a-z0-9]{3,}-[a-z0-9]{3,}|lookup/[^/?#]+|new)" - Access to secretsline 270
[Credential-related source omitted] - Access to secretsline 305
[Credential-related source omitted] - Access to secretsline 307
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Access to secretsline 336
[Credential-related source omitted] - Access to secretsline 347
[Credential-related source omitted] - Access to secretsline 348
[Credential-related source omitted] - Access to secretsline 455
[Credential-related source omitted] - Access to secretsline 470
[Credential-related source omitted]
plugins/google_meet/node/__init__.py
- Executable scriptline 1
Executable file
plugins/google_meet/node/cli.py
- Executable scriptline 1
Executable file
plugins/google_meet/node/client.py
- Executable scriptline 1
Executable file
plugins/google_meet/node/protocol.py
- Executable scriptline 1
Executable file
plugins/google_meet/node/registry.py
- Executable scriptline 1
Executable file
plugins/google_meet/node/server.py
- Executable scriptline 1
Executable file
plugins/google_meet/process_manager.py
- Executable scriptline 1
Executable file - Access to secretsline 68
[Credential-related source omitted] - Network accessline 70
from plugins.google_meet.meet_bot import _is_safe_meet_url, _meeting_id_from_url if not _is_safe_meet_url(url): return {"ok": False, "error": "refusing: only https://meet.google.com/ URLs are allowed. got: " + repr(url)} - Network accessline 71
if not _is_safe_meet_url(url): return {"ok": False, "error": "refusing: only https://meet.google.com/ URLs are allowed. got: " + repr(url)} if _pid_alive(int((_read_active() or {}).get("pid", 0))): - Network accessline 72
return {"ok": False, "error": "refusing: only https://meet.google.com/ URLs are allowed. got: " + repr(url)} - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 100
[Credential-related source omitted]
plugins/google_meet/realtime/__init__.py
- Executable scriptline 1
Executable file
plugins/google_meet/realtime/openai_client.py
plugins/google_meet/tools.py
- Executable scriptline 1
Executable file - Network accessline 57
"with meet_status and read captions with meet_transcript. Reminder to the agent: you " "should announce yourself in the meeting (there is no automatic consent announcement).", {"url": _str("Full https://meet.google.com/... URL. Requ - Network accessline 58
"should announce yourself in the meeting (there is no automatic consent announcement).", {"url": _str("Full https://meet.google.com/... URL. Required."), "mode": {"type": "string", "enum": ["transcribe", "realtime"], - Network accessline 59
{"url": _str("Full https://meet.google.com/... URL. Required."),
scripts/_activation.sh
scripts/build/windows-deps.ps1
scripts/run-in-hermes-env
scripts/run_tests.sh
- Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
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#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
Executable file; content unavailable - Executable scriptline 1
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Executable file; content unavailable - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted]
scripts/run_tests_parallel.py
- Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 571
pytest exit codes (https://docs.pytest.org/en/stable/reference/exit-codes.html): - Network accessline 571
pytest exit codes (https://docs.pytest.org/en/stable/reference/exit-codes.html): - Access to secretsline 650
[Credential-related source omitted] - Access to secretsline 650
[Credential-related source omitted] - Access to secretsline 1256
[Credential-related source omitted] - Access to secretsline 1256
[Credential-related source omitted] - Access to secretsline 1261
[Credential-related source omitted] - Access to secretsline 1261
[Credential-related source omitted] - Access to secretsline 1277
[Credential-related source omitted] - Access to secretsline 1277
[Credential-related source omitted] - Access to secretsline 1289
[Credential-related source omitted] - Access to secretsline 1289
[Credential-related source omitted] - Access to secretsline 1485
[Credential-related source omitted] - Access to secretsline 1485
[Credential-related source omitted] - Access to secretsline 1488
[Credential-related source omitted] - Access to secretsline 1488
[Credential-related source omitted]
scripts/tool_store_pins.py
setup.py
- Executable scriptline 1
Executable file - Executable scriptline 1
Executable file; content unavailable - Access to secretsline 34
[Credential-related source omitted] - Network accessline 37
"Building wheels or sdists for hermes-agent is not supported.\n" "Hermes is distributed via the shell installer, Docker image, or Nix.\n" "See: https://hermes-agent.nousresearch.com/docs/getting-started/installation\n" - Network accessline 38
"Hermes is distributed via the shell installer, Docker image, or Nix.\n" "See: https://hermes-agent.nousresearch.com/docs/getting-started/installation\n" "\n" - Network accessline 39
"See: https://hermes-agent.nousresearch.com/docs/getting-started/installation\n"
skills/autonomous-ai-agents/claude-code/SKILL.md
- Network accessline 16
Delegate coding tasks to [Claude Code](https://code.claude.com/docs/en/cli-reference) (Anthropic's autonomous coding agent CLI) via the Hermes terminal. Claude Code v2.x can read files, write code, run shell commands, spawn subagents, and m - Access to secretsline 21
[Credential-related source omitted] - Instruction override signalline 119
### Opt-in: --dangerously-skip-permissions (isolated environments only) - Access to secretsline 238
[Credential-related source omitted] - Instruction override signalline 285
|------|--------| | `--dangerously-skip-permissions` | Opt-in only: disables ALL permission prompts (file writes, bash, network). Throwaway worktree / isolated container only — see "Opt-in" above | | `--allow-dangerously-skip-permissions` | - Instruction override signalline 286
| `--dangerously-skip-permissions` | Opt-in only: disables ALL permission prompts (file writes, bash, network). Throwaway worktree / isolated container only — see "Opt-in" above | - Dangerous shell operationline 359
"deny": ["Read(.env)", "Bash(rm -rf *)"] - Dangerous shell operationline 608
"hooks": [{"type": "command", "command": "if echo \"$CLAUDE_TOOL_INPUT\" | grep -q 'rm -rf'; then echo 'Blocked!' && exit 2; fi"}] - Dangerous shell operationline 641
"hooks": [{"type": "command", "command": "if echo \"$CLAUDE_TOOL_INPUT\" | grep -qE 'rm -rf|git push.*--force|:(){ :|:& };:'; then echo 'Dangerous command blocked!' && exit 2; fi"}] - Access to secretsline 707
[Credential-related source omitted] - Instruction override signalline 730
1. **Interactive mode REQUIRES tmux** — Claude Code is a full TUI app. Using `pty=true` alone in Hermes terminal works but tmux gives you `capture-pane` for monitoring and `send-keys` for input, which is essential for orchestration. 2. **Th - Instruction override signalline 731
2. **The `--dangerously-skip-permissions` warning dialog defaults to "No, exit"** — that default is the safe answer; only send Down then Enter when you deliberately opted into the bypass in an isolated environment. Print mode (`-p`) skips t - Access to secretsline 740
[Credential-related source omitted]
skills/autonomous-ai-agents/codex/SKILL.md
- Network accessline 16
Delegate coding tasks to [Codex](https://github.com/openai/codex) via the Hermes terminal. Codex is OpenAI's autonomous coding agent CLI. - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 104
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && codex review --base origin/main", pty=true)
skills/autonomous-ai-agents/computer-use/SKILL.md
- Network accessline 27
Hermes drives [cua-driver](https://github.com/trycua/cua) under the hood. - Network accessline 306
| Click had no effect | Read the structured verdict. `effect:"unverifiable"` → fresh capture/state before retry, even with an escalation hint. `effect:"suspected_noop"` or a structured refusal → climb the recommended ladder: coordinate (px) - Network accessline 307
| Type text disappears into a terminal emulator | cua-driver detects terminals (Ghostty, iTerm2, Terminal.app, Windows Terminal, mintty, etc.) and routes through key-event synthesis — should "just work" on a recent cua-driver. If it doesn't - Dangerous shell operationline 308
| `blocked pattern in type text` | You tried to `type` a shell command matching the dangerous-pattern block list (`curl ... \| bash`, `sudo rm -rf`, etc.). Break the command up or reconsider | - Network accessline 308
| `blocked pattern in type text` | You tried to `type` a shell command matching the dangerous-pattern block list (`curl ... \| bash`, `sudo rm -rf`, etc.). Break the command up or reconsider |
skills/autonomous-ai-agents/hermes-agent/SKILL.md
- Network accessline 9
hermes: tags: [hermes, setup, configuration, multi-agent, spawning, cli, gateway, bots, bot-mode, features, themes, skins, desktop-plugins, tui-widgets, petdex, development] homepage: https://github.com/NousResearch/hermes-agent - Network accessline 10
tags: [hermes, setup, configuration, multi-agent, spawning, cli, gateway, bots, bot-mode, features, themes, skins, desktop-plugins, tui-widgets, petdex, development] homepage: https://github.com/NousResearch/hermes-agent related_ski - Network accessline 11
homepage: https://github.com/NousResearch/hermes-agent - Network accessline 31
**Docs:** https://hermes-agent.nousresearch.com/docs/ - Network accessline 39
- **Every shipped feature, one line each: https://hermes-agent.nousresearch.com/docs/llms.txt.** Start here for any "can Hermes do X?" or "how do I do X?" — it indexes the entire documentation set with a link to the page that answers. It is - Network accessline 41
- Source tree: https://github.com/NousResearch/hermes-agent - Network accessline 48
# Install (shell installer — bootstraps PM, Python, dependencies, and the launcher) curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash - Dangerous shell operationline 49
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash - Network accessline 49
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash - Access to secretsline 72
[Credential-related source omitted] - Network accessline 89
| User wants... | Load | |---|---| | **Anything not listed below — "can Hermes do X?", "how do I set up X?"** | **https://hermes-agent.nousresearch.com/docs/llms.txt** | - Network accessline 90
|---|---| | **Anything not listed below — "can Hermes do X?", "how do I set up X?"** | **https://hermes-agent.nousresearch.com/docs/llms.txt** | | Bots that chat, run routines, or message each other; the Bots tab | docs: `/user-guide/bot-mo - Network accessline 91
| **Anything not listed below — "can Hermes do X?", "how do I set up X?"** | **https://hermes-agent.nousresearch.com/docs/llms.txt** |
skills/autonomous-ai-agents/hermes-agent/references/background-systems.md
- Network accessline 45
User docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/cron - Network accessline 70
Config: `curator.*` (`enabled`, `interval_hours`, `min_idle_hours`, `stale_after_days`, `archive_after_days`, `backup.*`). User docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/curator - Network accessline 72
User docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/curator - Network accessline 101
User docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/kanban
skills/autonomous-ai-agents/hermes-agent/references/cli-reference.md
- Network accessline 3
Live sources when anything looks stale: `hermes --help`, `hermes <command> --help`, https://hermes-agent.nousresearch.com/docs/reference/cli-commands - Network accessline 4
https://hermes-agent.nousresearch.com/docs/reference/cli-commands - Network accessline 57
hermes skills list|browse|search QUERY|inspect ID hermes skills install ID Hub identifier OR a direct https://…/SKILL.md URL - Network accessline 58
hermes skills install ID Hub identifier OR a direct https://…/SKILL.md URL - Network accessline 81
20+ platforms: Telegram, Discord, Slack, WhatsApp (Baileys + Business Cloud API), iMessage (Photon — `hermes photon setup`), Signal, Email, SMS, Matrix, Mattermost, Teams, LINE, SimpleX, ntfy, Google Chat, Home Assistant, DingTalk, Feishu, - Network accessline 82
Docs: https://hermes-agent.nousresearch.com/docs/user-guide/messaging/ - Network accessline 143
| Looking for... | Location | |---|---| | Config options | `hermes config edit` · [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | - Network accessline 144
|---|---| | Config options | `hermes config edit` · [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | | Tools / toolsets | `hermes tools list` · [Tools reference](https://hermes-agent.nousresearch.c - Network accessline 145
| Config options | `hermes config edit` · [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | - Network accessline 146
| Tools / toolsets | `hermes tools list` · [Tools reference](https://hermes-agent.nousresearch.com/docs/reference/tools-reference) | - Network accessline 147
| Skills catalog | `hermes skills browse` · [Skills catalog](https://hermes-agent.nousresearch.com/docs/reference/skills-catalog) | - Network accessline 148
| Provider setup | `hermes model` · [Providers guide](https://hermes-agent.nousresearch.com/docs/integrations/providers) | - Network accessline 149
| Env variables | `hermes config env-path` · [Env vars reference](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) |
skills/autonomous-ai-agents/hermes-agent/references/configuration.md
- Network accessline 3
Edit with `hermes config edit` or `hermes config set section.key value`. Full reference: https://hermes-agent.nousresearch.com/docs/user-guide/configuration - Network accessline 4
Full reference: https://hermes-agent.nousresearch.com/docs/user-guide/configuration - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted]
skills/autonomous-ai-agents/hermes-agent/references/contributor-guide.md
skills/autonomous-ai-agents/hermes-agent/references/native-mcp.md
- Access to secretsline 59
[Credential-related source omitted] - Network accessline 67
mcp_servers: server_name: url: "https://my-server.example.com/mcp" # (required) server URL - Network accessline 68
server_name: url: "https://my-server.example.com/mcp" # (required) server URL headers: # (optional) HTTP headers - Network accessline 69
url: "https://my-server.example.com/mcp" # (required) server URL - Network accessline 151
mcp_servers: remote_api: url: "https://mcp.example.com/mcp" - Network accessline 152
remote_api: url: "https://mcp.example.com/mcp" headers: - Network accessline 153
url: "https://mcp.example.com/mcp" - Access to secretsline 178
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Network accessline 273
mcp_servers: company_api: url: "https://mcp.mycompany.com/v1/mcp" - Network accessline 274
company_api: url: "https://mcp.mycompany.com/v1/mcp" headers: - Network accessline 275
url: "https://mcp.mycompany.com/v1/mcp" - Access to secretsline 299
[Credential-related source omitted] - Network accessline 301
company_api: url: "https://mcp.internal.company.com/mcp" - Network accessline 302
url: "https://mcp.internal.company.com/mcp"
skills/autonomous-ai-agents/hermes-agent/references/petdex.md
skills/autonomous-ai-agents/hermes-agent/references/portal-auth-for-third-party-apps.md
- Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Network accessline 33
Portal at `https://inference-api.nousresearch.com/v1` is an OpenAI-compatible - Access to secretsline 41
[Credential-related source omitted] - Network accessline 58
1. Read Hermes's existing Portal credential out of `~/.hermes/auth.json`. 2. Expose a local OpenAI-compatible endpoint at `http://localhost:NNNN/v1`. 3. Forward incoming requests to `inference-api.nousresearch.com/v1` with that - Network accessline 59
2. Expose a local OpenAI-compatible endpoint at `http://localhost:NNNN/v1`. 3. Forward incoming requests to `inference-api.nousresearch.com/v1` with that bearer attached. - Network accessline 63
Karakeep/OpenWebUI/etc. then point at `http://localhost:NNNN/v1` with any - Network accessline 112
it already uses Portal via Hermes's provider config — done. 2. If separate app: today, paste the static API key from Portal → API Keys. Base URL `https://inference-api.nousresearch.com/v1`. Rate limits are - Network accessline 113
2. If separate app: today, paste the static API key from Portal → API Keys. Base URL `https://inference-api.nousresearch.com/v1`. Rate limits are subscription-tier based, applied per-key. - Network accessline 114
Base URL `https://inference-api.nousresearch.com/v1`. Rate limits are
skills/autonomous-ai-agents/hermes-agent/references/providers-and-models.md
- Network accessline 3
Set via `hermes model` (picker) or `hermes setup`. 35+ provider profiles ship as plugins under `plugins/model-providers/`; user plugins of the same name override. Full docs: https://hermes-agent.nousresearch.com/docs/integrations/providers - Network accessline 5
Full docs: https://hermes-agent.nousresearch.com/docs/integrations/providers - Access to secretsline 11
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted] - Access to secretsline 23
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 27
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 30
GitHub Copilot (`copilot`, `copilot-acp`) is a labs plugin, not core: `hermes plugins install github-copilot`, then `hermes auth add copilot` (https://github.com/NousResearch/hermes-labs-plugins/tree/main/github-copilot). - Network accessline 48
model: qwen3.5:397b provider: custom base_url: "https://ollama.com/v1" - Network accessline 49
provider: custom base_url: "https://ollama.com/v1" theta: - Network accessline 50
base_url: "https://ollama.com/v1" - Network accessline 52
model: theta-1 provider: custom base_url: "https://theta.example.com/v1" - Network accessline 53
[Credential-related source omitted] - Network accessline 54
base_url: "https://theta.example.com/v1" - Access to secretsline 55
[Credential-related source omitted] - Access to secretsline 67
[Credential-related source omitted]
skills/autonomous-ai-agents/hermes-agent/references/security-privacy.md
- Dangerous shell operationline 31
By default (`approvals.mode: smart`), Hermes asks an auxiliary LLM to assess shell commands flagged as destructive (`rm -rf`, `git reset --hard`, etc.). The modes are: - Dangerous shell operationline 55
2. Shell-hook consent (only if present): `rm -f ~/.hermes/shell-hooks-allowlist.json`
skills/autonomous-ai-agents/hermes-agent/references/slash-commands.md
- Access to secretsline 72
[Credential-related source omitted]
skills/autonomous-ai-agents/hermes-agent/references/troubleshooting.md
skills/autonomous-ai-agents/hermes-agent/references/webhooks.md
- Network accessline 51
curl http://localhost:8644/health - Network accessline 86
Full filter syntax: https://hermes-agent.nousresearch.com/docs/user-guide/messaging/webhooks#payload-filters - Network accessline 211
1. **Is the gateway running?** Check with `systemctl --user status hermes-gateway` or `ps aux | grep gateway` 2. **Is the webhook server listening?** `curl http://localhost:8644/health` should return `{"status": "ok"}` - Network accessline 212
2. **Is the webhook server listening?** `curl http://localhost:8644/health` should return `{"status": "ok"}`
skills/autonomous-ai-agents/hermes-agent/references/windows-quirks.md
- Access to secretsline 30
[Credential-related source omitted]
skills/autonomous-ai-agents/hermes-agent/templates/clock.mjs
- Executable scriptline 1
Executable file
skills/autonomous-ai-agents/hermes-agent/templates/plugin.js
- Executable scriptline 1
Executable file
skills/creative/architecture-diagram/SKILL.md
- Network accessline 37
Based on [Cocoon AI's architecture-diagram-generator](https://github.com/Cocoon-AI/architecture-diagram-generator) (MIT).
skills/creative/architecture-diagram/templates/template.html
- Network accessline 5
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>[PROJECT NAME] Architecture Diagram</title> <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="styles - Network accessline 6
<title>[PROJECT NAME] Architecture Diagram</title> <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="stylesheet"> <style> - Network accessline 7
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="stylesheet">
skills/creative/ascii-video/README.md
- Network accessline 5
Built for [Hermes Agent](https://github.com/NousResearch/hermes-agent). Usable in any coding agent. Canonical source lives here; synced to [`NousResearch/hermes-agent/skills/creative/ascii-video`](https://github.com/NousResearch/hermes-agen
skills/creative/ascii-video/references/inputs.md
- Access to secretsline 301
[Credential-related source omitted] - Network accessline 307
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" - Access to secretsline 308
[Credential-related source omitted] - Network accessline 317
}, } resp = requests.post(url, json=data, headers=headers, stream=True) - Network accessline 318
} resp = requests.post(url, json=data, headers=headers, stream=True) resp.raise_for_status() - Network accessline 319
resp = requests.post(url, json=data, headers=headers, stream=True)
skills/creative/ascii-video/references/shaders.md
skills/creative/baoyu-infographic/PORT_NOTES.md
- Network accessline 3
Ported from [JimLiu/baoyu-skills](https://github.com/JimLiu/baoyu-skills) v1.56.1. - Network accessline 35
# Compare versions curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-infographic/SKILL.md | head -5 - Network accessline 36
curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-infographic/SKILL.md | head -5 - Network accessline 39
# Diff reference files diff <(curl -sL https://raw.githubusercontent.com/.../references/layouts/bento-grid.md) references/layouts/bento-grid.md - Network accessline 40
diff <(curl -sL https://raw.githubusercontent.com/.../references/layouts/bento-grid.md) references/layouts/bento-grid.md
skills/creative/baoyu-infographic/SKILL.md
- Network accessline 9
hermes: tags: [infographic, visual-summary, creative, image-generation] homepage: https://github.com/JimLiu/baoyu-skills#baoyu-infographic - Network accessline 10
tags: [infographic, visual-summary, creative, image-generation] homepage: https://github.com/JimLiu/baoyu-skills#baoyu-infographic --- - Network accessline 11
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-infographic - Network accessline 16
Adapted from [baoyu-infographic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.
skills/creative/humanizer/SKILL.md
- Network accessline 3
description: "Humanize text: strip AI-isms and add real voice." version: 2.5.1 author: Siqi Chen (@blader, https://github.com/blader/humanizer), ported by Hermes Agent - Network accessline 4
version: 2.5.1 author: Siqi Chen (@blader, https://github.com/blader/humanizer), ported by Hermes Agent license: MIT - Network accessline 5
author: Siqi Chen (@blader, https://github.com/blader/humanizer), ported by Hermes Agent - Network accessline 10
tags: [writing, editing, humanize, anti-ai-slop, voice, prose, text] category: creative homepage: https://github.com/blader/humanizer - Network accessline 11
category: creative homepage: https://github.com/blader/humanizer related_skills: [songwriting-and-ai-music] - Network accessline 12
homepage: https://github.com/blader/humanizer - Network accessline 643
This skill is ported from [blader/humanizer](https://github.com/blader/humanizer) (MIT licensed), which is itself based on [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProj - Network accessline 645
Original author: Siqi Chen ([@blader](https://github.com/blader)). Original repo: https://github.com/blader/humanizer (version 2.5.1). Ported to Hermes Agent with Hermes-native tool references (`read_file`, `patch`, `write_file`) and guidan
skills/creative/manim-video/README.md
- Network accessline 3
Production pipeline for mathematical and technical animations using [Manim Community Edition](https://www.manim.community/).
skills/creative/manim-video/scripts/setup.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/creative/p5js/README.md
- Network accessline 3
Production pipeline for interactive and generative visual art using [p5.js](https://p5js.org/).
skills/creative/p5js/SKILL.md
- Network accessline 95
|-----------|---------|-----------| | **Color system** | HSB/HSL, RGB, named palettes, procedural harmony, gradient interpolation | `references/color-systems.md` | | **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain wa - Network accessline 96
| **Color system** | HSB/HSL, RGB, named palettes, procedural harmony, gradient interpolation | `references/color-systems.md` | | **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain warping, curl noise | `references/visu - Network accessline 97
| **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain warping, curl noise | `references/visual-effects.md` § Noise | - Network accessline 120
For every project, invent at least one of: - A custom color palette matching the mood (not a preset) - A novel noise field combination (e.g., curl noise + domain warp + feedback) - Network accessline 121
- A custom color palette matching the mood (not a preset) - A novel noise field combination (e.g., curl noise + domain warp + feedback) - A unique particle behavior (custom forces, custom trails, custom spawning) - Network accessline 122
- A novel noise field combination (e.g., curl noise + domain warp + feedback) - Network accessline 184
<title>Project Name</title> <script>p5.disableFriendlyErrors = true;</script> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 185
<script>p5.disableFriendlyErrors = true;</script> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> <!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></scri - Network accessline 186
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 187
<!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></script> --> - Network accessline 188
<!-- <script src="https://unpkg.com/p5.js-svg@1.6.0"></script> --> <!-- SVG export --> - Network accessline 189
<!-- <script src="https://cdn.jsdelivr.net/npm/ccapture.js-npmfixed/build/CCapture.all.min.js"></script> --> <!-- video capture --> - Network accessline 489
1. **Write the HTML file** — single self-contained file, all code inline 2. **Open in browser** — `open sketch.html` (macOS) or `xdg-open sketch.html` (Linux) 3. **Local assets** (fonts, images) require a server: `python -m http.server 8080 - Network accessline 490
2. **Open in browser** — `open sketch.html` (macOS) or `xdg-open sketch.html` (Linux) 3. **Local assets** (fonts, images) require a server: `python -m http.server 8080` in the project directory, then open `http://localhost:8080/sketch.html` - Network accessline 491
3. **Local assets** (fonts, images) require a server: `python -m http.server 8080` in the project directory, then open `http://localhost:8080/sketch.html`
skills/creative/p5js/references/core-api.md
- Network accessline 409
<script src="https://cdn.jsdelivr.net/npm/p5@2/lib/p5.min.js"></script>
skills/creative/p5js/references/export-pipeline.md
skills/creative/p5js/references/interaction.md
- Network accessline 276
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></script>
skills/creative/p5js/references/typography.md
- Network accessline 31
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap" rel="stylesheet">
skills/creative/p5js/references/visual-effects.md
- Network accessline 66
### Curl Noise - Network accessline 68
Divergence-free noise field. Particles following curl noise never converge or diverge — they flow in smooth, swirling patterns. - Network accessline 76
let dndy = (noise(x * scale, y * scale + eps, time) - noise(x * scale, y * scale - eps, time)) / (2 * eps); // Curl = perpendicular to gradient - Network accessline 77
noise(x * scale, y * scale - eps, time)) / (2 * eps); // Curl = perpendicular to gradient return createVector(dndy, -dndx); - Network accessline 78
// Curl = perpendicular to gradient - Network accessline 794
for (let dy = -2; dy <= 2; dy++) { for (let dx = -2; dx <= 2; dx++) { let nc = col + dx, nr = row + dy; - Network accessline 795
for (let dx = -2; dx <= 2; dx++) { let nc = col + dx, nr = row + dy; if (nc >= 0 && nc < cols && nr >= 0 && nr < rows) { - Network accessline 796
let nc = col + dx, nr = row + dy; - Network accessline 797
if (nc >= 0 && nc < cols && nr >= 0 && nr < rows) { - Network accessline 798
let gi = nc + nr * cols; - Network accessline 825
<script src="https://cdn.jsdelivr.net/npm/p5.brush@latest/dist/p5.brush.js"></script> - Network accessline 849
<script src="https://cdn.jsdelivr.net/npm/p5.grain@0.7.0/p5.grain.min.js"></script> - Network accessline 865
<script src="https://cdn.jsdelivr.net/npm/ccapture.js-npmfixed/build/CCapture.all.min.js"></script>
skills/creative/p5js/scripts/export-frames.js
- Executable scriptline 1
#!/usr/bin/env node
skills/creative/p5js/scripts/render.sh
skills/creative/p5js/scripts/serve.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 16
echo "=== p5.js Dev Server ===" echo "Serving: $(cd "$DIR" && pwd)" echo "URL: http://localhost:$PORT" - Network accessline 17
echo "Serving: $(cd "$DIR" && pwd)" echo "URL: http://localhost:$PORT" echo "Press Ctrl+C to stop" - Network accessline 18
echo "URL: http://localhost:$PORT"
skills/creative/p5js/scripts/setup.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 23
else warn "Node.js not found — optional, needed for headless export" echo " Install: https://nodejs.org/ or 'brew install node'" - Network accessline 24
warn "Node.js not found — optional, needed for headless export" echo " Install: https://nodejs.org/ or 'brew install node'" fi - Network accessline 25
echo " Install: https://nodejs.org/ or 'brew install node'"
skills/creative/p5js/templates/viewer.html
- Network accessline 26
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Generative Art Viewer</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 27
<title>Generative Art Viewer</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> <style> - Network accessline 28
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script>
skills/creative/popular-web-designs/SKILL.md
- Network accessline 57
<title>Page Title</title> <!-- Paste the Google Fonts <link> from the template's Hermes notes --> <link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet"> - Network accessline 58
<!-- Paste the Google Fonts <link> from the template's Hermes notes --> <link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet"> <style> - Network accessline 59
<link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet">
skills/creative/popular-web-designs/templates/airbnb.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/airtable.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/bmw.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cal.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Roboto Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Roboto+Mono:wght@400;500;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Roboto+Mono:wght@400;500;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/claude.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/clay.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/clickhouse.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cohere.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/coinbase.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/composio.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,ops - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cursor.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/elevenlabs.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/expo.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/figma.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/framer.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Azeret Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Azeret+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Azeret+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/hashicorp.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/ibm.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'IBM Plex Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wgh - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/intercom.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/kraken.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/linear.app.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/lovable.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/minimax.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mintlify.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/miro.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mistral.ai.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mongodb.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500;600&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500;600&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/notion.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/nvidia.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/ollama.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/opencode.ai.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:w - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/pinterest.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/posthog.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/raycast.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/replicate.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/resend.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/revolut.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/runwayml.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/sanity.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'IBM Plex Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wgh - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;400;500;600;700&family=IBM+Plex+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;400;500;600;700&family=IBM+Plex+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/sentry.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/spacex.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/spotify.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/stripe.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:w - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:wght@300;400;500;600&family=Source+Code+Pro:wght@400;500;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:wght@300;400;500;600&family=Source+Code+Pro:wght@400;500;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/supabase.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/superhuman.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/together.ai.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/uber.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/vercel.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/voltagent.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:w - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/warp.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/webflow.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/wise.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/x.ai.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/zapier.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/email/himalaya/SKILL.md
- Network accessline 9
hermes: tags: [Email, IMAP, SMTP, CLI, Communication] homepage: https://github.com/pimalaya/himalaya - Network accessline 10
tags: [Email, IMAP, SMTP, CLI, Communication] homepage: https://github.com/pimalaya/himalaya prerequisites: - Network accessline 11
homepage: https://github.com/pimalaya/himalaya - Network accessline 39
# Pre-built binary (Linux/macOS — recommended) curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh - Network accessline 40
curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh
skills/email/himalaya/references/configuration.md
- Access to secretsline 51
[Credential-related source omitted] - Network accessline 198
backend.auth.access-token.cmd = "pass show oauth/access-token" backend.auth.refresh-token.cmd = "pass show oauth/refresh-token" backend.auth.auth-url = "https://provider.com/oauth/authorize" - Network accessline 199
backend.auth.refresh-token.cmd = "pass show oauth/refresh-token" backend.auth.auth-url = "https://provider.com/oauth/authorize" backend.auth.token-url = "https://provider.com/oauth/token" - Network accessline 200
backend.auth.auth-url = "https://provider.com/oauth/authorize" - Network accessline 201
backend.auth.token-url = "https://provider.com/oauth/token"
skills/media/youtube-content/SKILL.md
- Network accessline 28
From that checkout, first follow the isolated development-home setup in [Package Management](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow), - Network accessline 29
[Package Management](https://hermes-agent.nousresearch.com/docs/reference/package-management#developer-workflow), - Network accessline 49
# JSON output with metadata python SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" - Network accessline 50
python SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
skills/media/youtube-content/scripts/fetch_transcript.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 63
api = YouTubeTranscriptApi() if languages: result = api.fetch(video_id, languages=languages) - Network accessline 64
if languages: result = api.fetch(video_id, languages=languages) else: - Network accessline 65
result = api.fetch(video_id, languages=languages) - Network accessline 67
result = api.fetch(video_id)
skills/productivity/google-workspace/SKILL.md
- Access to secretsline 11
[Credential-related source omitted] - Network accessline 14
hermes: tags: [Google, Gmail, Calendar, Drive, Sheets, Docs, Contacts, Email, OAuth] homepage: https://github.com/NousResearch/hermes-agent - Network accessline 15
tags: [Google, Gmail, Calendar, Drive, Sheets, Docs, Contacts, Email, OAuth] homepage: https://github.com/NousResearch/hermes-agent related_skills: [himalaya] - Network accessline 16
homepage: https://github.com/NousResearch/hermes-agent - Network accessline 93
> > 1. Create or select a project: > https://console.cloud.google.com/projectselector2/home/dashboard - Network accessline 94
> 1. Create or select a project: > https://console.cloud.google.com/projectselector2/home/dashboard > 2. Enable the required APIs from the API Library: - Network accessline 95
> https://console.cloud.google.com/projectselector2/home/dashboard - Network accessline 96
> 2. Enable the required APIs from the API Library: > https://console.cloud.google.com/apis/library > Enable: Gmail API, Google Calendar API, Google Drive API, - Network accessline 97
> https://console.cloud.google.com/apis/library - Network accessline 99
> Google Sheets API, Google Docs API, People API > 3. Create the OAuth client here: > https://console.cloud.google.com/apis/credentials - Network accessline 100
> 3. Create the OAuth client here: > https://console.cloud.google.com/apis/credentials > Credentials → Create Credentials → OAuth 2.0 Client ID - Network accessline 101
> https://console.cloud.google.com/apis/credentials - Network accessline 103
> 4. Application type: "Desktop app" → Create > 5. If the app is still in Testing, add the user's Google account as a test user here: > https://console.cloud.google.com/auth/audience - Network accessline 104
> 5. If the app is still in Testing, add the user's Google account as a test user here: > https://console.cloud.google.com/auth/audience > Audience → Test users → Add users - Network accessline 105
> https://console.cloud.google.com/auth/audience > Audience → Test users → Add users > 6. Download the JSON file and tell me the file path - Access to secretsline 115
[Credential-related source omitted] - Network accessline 136
Agent rules for this step: - Extract the `auth_url` field and send that exact URL to the user as a single line. - Tell the user that the browser will likely fail on `http://localhost:1` after approval, and that this is expected. - Network accessline 137
- Extract the `auth_url` field and send that exact URL to the user as a single line. - Tell the user that the browser will likely fail on `http://localhost:1` after approval, and that this is expected. - Tell them to copy the ENTIRE redirec - Network accessline 138
- Tell the user that the browser will likely fail on `http://localhost:1` after approval, and that this is expected. - Network accessline 140
- If the user gets `Error 403: access_denied`, send them directly to `https://console.cloud.google.com/auth/audience` to add themselves as a test user. - Network accessline 144
The user will paste back either a URL like `http://localhost:1/?code=4/0A...&scope=...`
skills/productivity/google-workspace/scripts/_hermes_home.py
skills/productivity/google-workspace/scripts/google_api.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 43
[Credential-related source omitted] - Network accessline 45
SCOPES = [ "https://www.googleapis.com/auth/gmail.readonly", - Network accessline 46
"https://www.googleapis.com/auth/gmail.readonly", - Network accessline 47
"https://www.googleapis.com/auth/gmail.send", - Network accessline 48
"https://www.googleapis.com/auth/gmail.modify", - Network accessline 49
"https://www.googleapis.com/auth/calendar", - Network accessline 50
"https://www.googleapis.com/auth/drive", - Network accessline 51
"https://www.googleapis.com/auth/contacts.readonly", - Network accessline 52
"https://www.googleapis.com/auth/spreadsheets", - Network accessline 53
"https://www.googleapis.com/auth/documents", - Access to secretsline 90
[Credential-related source omitted] - Network accessline 1073
"documentId": doc_id, "title": doc.get("title", ""), "url": f"https://docs.google.com/document/d/{doc_id}/edit" if doc_id else "", - Network accessline 1074
"title": doc.get("title", ""), "url": f"https://docs.google.com/document/d/{doc_id}/edit" if doc_id else "", }, indent=2, ensure_ascii=False)) - Network accessline 1075
"url": f"https://docs.google.com/document/d/{doc_id}/edit" if doc_id else "",
skills/productivity/google-workspace/scripts/gws_bridge.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 59
req = urllib.request.Request(token_data["token_uri"], data=params) - Network accessline 60
try: with urllib.request.urlopen(req, timeout=15) as resp: result = json.loads(resp.read()) - Network accessline 61
with urllib.request.urlopen(req, timeout=15) as resp: - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 109
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted]
skills/productivity/google-workspace/scripts/setup.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 50
SCOPES = [ "https://www.googleapis.com/auth/gmail.readonly", - Network accessline 51
"https://www.googleapis.com/auth/gmail.readonly", - Network accessline 52
"https://www.googleapis.com/auth/gmail.send", - Network accessline 53
"https://www.googleapis.com/auth/gmail.modify", - Network accessline 54
"https://www.googleapis.com/auth/calendar", - Network accessline 55
"https://www.googleapis.com/auth/drive", - Network accessline 56
"https://www.googleapis.com/auth/contacts.readonly", - Network accessline 57
"https://www.googleapis.com/auth/spreadsheets", - Network accessline 58
"https://www.googleapis.com/auth/documents", - Network accessline 64
REDIRECT_URI = "http://localhost:1" - Access to secretsline 220
[Credential-related source omitted] - Access to secretsline 221
[Credential-related source omitted] - Network accessline 233
if "installed" not in data and "web" not in data: print("ERROR: Not a Google OAuth client secret file (missing 'installed' key).") print("Download the correct file from: https://console.cloud.google.com/apis/credentials") - Network accessline 234
print("ERROR: Not a Google OAuth client secret file (missing 'installed' key).") print("Download the correct file from: https://console.cloud.google.com/apis/credentials") sys.exit(1) - Network accessline 235
print("Download the correct file from: https://console.cloud.google.com/apis/credentials") - Access to secretsline 353
[Credential-related source omitted] - Network accessline 399
import urllib.request urllib.request.urlopen( - Network accessline 400
urllib.request.urlopen( - Network accessline 401
urllib.request.Request( - Network accessline 402
[Credential-related source omitted] - Access to secretsline 422
[Credential-related source omitted] - Access to secretsline 433
[Credential-related source omitted] - Access to secretsline 434
[Credential-related source omitted]
skills/productivity/maps/scripts/maps_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 34
NOMINATIM_SEARCH = "https://nominatim.openstreetmap.org/search" - Network accessline 35
NOMINATIM_REVERSE = "https://nominatim.openstreetmap.org/reverse" - Network accessline 38
OVERPASS_URLS = [ "https://overpass-api.de/api/interpreter", - Network accessline 39
"https://overpass-api.de/api/interpreter", - Network accessline 40
"https://overpass.kumi.systems/api/interpreter", - Network accessline 43
OVERPASS_API = OVERPASS_URLS[0] OSRM_BASE = "https://router.project-osrm.org/route/v1" - Network accessline 44
OSRM_BASE = "https://router.project-osrm.org/route/v1" - Network accessline 45
TIMEAPI_BASE = "https://timeapi.io/api/timezone/coordinate" - Network accessline 173
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 176
for attempt in range(1, retries + 1): try: with urllib.request.urlopen(req, timeout=15) as resp: - Network accessline 177
try: with urllib.request.urlopen(req, timeout=15) as resp: raw = resp.read().decode("utf-8") - Network accessline 178
with urllib.request.urlopen(req, timeout=15) as resp: - Network accessline 210
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 213
for attempt in range(1, retries + 1): try: with urllib.request.urlopen(req, timeout=15) as resp: - Network accessline 214
try: with urllib.request.urlopen(req, timeout=15) as resp: return resp.read().decode("utf-8") - Network accessline 215
with urllib.request.urlopen(req, timeout=15) as resp: - Network accessline 240
encoded = data_str.encode("utf-8") req = urllib.request.Request( - Network accessline 241
req = urllib.request.Request( - Network accessline 251
for attempt in range(1, retries + 1): try: with urllib.request.urlopen(req, timeout=30) as resp: - Network accessline 252
try: with urllib.request.urlopen(req, timeout=30) as resp: raw = resp.read().decode("utf-8") - Network accessline 253
with urllib.request.urlopen(req, timeout=30) as resp: - Network accessline 458
"maps_url": f"https://www.google.com/maps/search/?api=1&query={el_lat},{el_lon}", - Network accessline 482
place["directions_url"] = ( f"https://www.google.com/maps/dir/?api=1" - Network accessline 483
f"https://www.google.com/maps/dir/?api=1"
skills/productivity/microsoft-office/SKILL.md
skills/productivity/microsoft-office/references/excel.md
- Network accessline 64
| List tables | `xlsx_edit.py f.xlsx --list-tables` | | Defined names | `--define-name "Rates='Data'!$B$2:$B$9"` / `--delete-name Rates` / `xlsx_read.py f.xlsx --names` | | Hyperlink | `--hyperlink "A1=https://example.com|Docs"` | - Network accessline 65
| Defined names | `--define-name "Rates='Data'!$B$2:$B$9"` / `--delete-name Rates` / `xlsx_read.py f.xlsx --names` | | Hyperlink | `--hyperlink "A1=https://example.com|Docs"` | | Cell note | `--note "B2=Check this|Reviewer"`; read via `xlsx - Network accessline 66
| Hyperlink | `--hyperlink "A1=https://example.com|Docs"` |
skills/productivity/microsoft-office/references/revisions-and-comments.md
skills/productivity/microsoft-office/scripts/csv_to_xlsx.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/docx_comments.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 35
W = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 104
t = etree.SubElement(el, q("t")) t.text = s t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 105
t.text = s t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") return right - Network accessline 106
t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve")
skills/productivity/microsoft-office/scripts/docx_common.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/docx_create.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/docx_edit.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 39
W = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 109
pt.text = (pt.text or "") + ((ct.text or "") if ct is not None else "") pt.set("{http://www.w3.org/XML/1998/namespace}space", - Network accessline 110
if ct is not None else "") pt.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 111
pt.set("{http://www.w3.org/XML/1998/namespace}space", - Network accessline 131
elif extra is instr: it = r.makeelement(_q("instrText"), {}) it.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 132
it = r.makeelement(_q("instrText"), {}) it.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") it.text = instr - Network accessline 133
it.set("{http://www.w3.org/XML/1998/namespace}space", "preserve")
skills/productivity/microsoft-office/scripts/docx_read.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/docx_revisions.py
skills/productivity/microsoft-office/scripts/docx_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/docx_validate.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 32
W = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 33
R = "http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 34
PR = "http://schemas.openxmlformats.org/package/2006/relationships" - Possible obfuscationline 39
b"\x01\x00\x00\x00", # EMF - Possible obfuscationline 40
b"\xd7\xcd\xc6\x9a", b"\x01\x00\x09\x00", # WMF variants
skills/productivity/microsoft-office/scripts/pptx_create.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 12
"size": 18, "color": "CC0000", "font": "Arial", "italic": false, "link": "https://example.com/agenda"}], - Network accessline 13
"italic": false, "link": "https://example.com/agenda"}], "background": "1F2937", // solid slide background (hex) - Network accessline 14
"link": "https://example.com/agenda"}],
skills/productivity/microsoft-office/scripts/pptx_edit.py
skills/productivity/microsoft-office/scripts/pptx_from_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/pptx_read.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/pptx_render.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_create.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_edit.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_read.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_recalc.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_restructure.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/scripts/xlsx_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/microsoft-office/tests/test_docx_skill.py
- Executable scriptline 1
Executable file - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 258
W = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Access to secretsline 266
[Credential-related source omitted] - Network accessline 279
t = etree.SubElement(r, q("t")) t.text = text t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 281
t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 291
t = etree.SubElement(r, q("delText")) t.text = text t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve") - Network accessline 293
t.set("{http://www.w3.org/XML/1998/namespace}space", "preserve")
skills/productivity/microsoft-office/tests/test_powerpoint_skill.py
- Executable scriptline 1
Executable file - Access to secretsline 18
[Credential-related source omitted] - Possible obfuscationline 40
data = base64.b64decode( - Possible obfuscationline 251
blue = base64.b64decode( - Access to secretsline 288
[Credential-related source omitted] - Possible obfuscationline 408
blue = base64.b64decode( - Network accessline 442
"slide_number": True, "bullets": [{"text": "Visit example", "link": "https://example.com/info"}]}]}) - Network accessline 443
"bullets": [{"text": "Visit example", "link": "https://example.com/info"}]}]}) out = workdir / "polish.pptx" - Network accessline 444
"link": "https://example.com/info"}]}]}) - Network accessline 456
for p in sh.text_frame.paragraphs for r in p.runs if r.hyperlink.address] assert link_runs[0].hyperlink.address == "https://example.com/info" - Network accessline 458
assert link_runs[0].hyperlink.address == "https://example.com/info" - Network accessline 464
result = run("pptx_edit.py", deck, "--output", str(edited), "--set-background", "0", "004400", "--hyperlink", "1", "Overview", "https://example.com/x", - Network accessline 465
"--set-background", "0", "004400", "--hyperlink", "1", "Overview", "https://example.com/x", "--enable-slide-number", "1", - Network accessline 466
"--hyperlink", "1", "Overview", "https://example.com/x", - Network accessline 480
for p in sh.text_frame.paragraphs for r in p.runs if r.hyperlink.address] assert links == ["https://example.com/x"] - Network accessline 482
assert links == ["https://example.com/x"]
skills/productivity/microsoft-office/tests/test_xlsx_skill.py
- Executable scriptline 1
Executable file - Access to secretsline 26
[Credential-related source omitted] - Network accessline 484
"sheets": [{"name": "D", "cells": { "A1": {"value": "docs", "hyperlink": "https://example.com/docs"}, - Network accessline 485
"A1": {"value": "docs", "hyperlink": "https://example.com/docs"}, "B1": {"value": 0.07, "note": "quarterly rate"}, - Network accessline 486
"hyperlink": "https://example.com/docs"}, - Network accessline 496
wb = load_workbook(book) ws = wb["D"] assert ws["A1"].hyperlink.target == "https://example.com/docs" - Network accessline 497
ws = wb["D"] assert ws["A1"].hyperlink.target == "https://example.com/docs" assert ws["B1"].comment.text == "quarterly rate" - Network accessline 498
assert ws["A1"].hyperlink.target == "https://example.com/docs" - Network accessline 505
"--define-name", "Extra='D'!$C$1", "--delete-name", "Rate", "--hyperlink", "D1=https://example.com/more|More", - Network accessline 506
"--delete-name", "Rate", "--hyperlink", "D1=https://example.com/more|More", "--note", "D1=see more|Reviewer", - Network accessline 507
"--hyperlink", "D1=https://example.com/more|More", - Network accessline 512
assert "Rate" not in wb.defined_names assert wb.defined_names["Extra"].attr_text == "'D'!$C$1" assert ws["D1"].hyperlink.target == "https://example.com/more" - Network accessline 513
assert wb.defined_names["Extra"].attr_text == "'D'!$C$1" assert ws["D1"].hyperlink.target == "https://example.com/more" assert ws["D1"].value == "More" - Network accessline 514
assert ws["D1"].hyperlink.target == "https://example.com/more" - Access to secretsline 569
[Credential-related source omitted]
skills/productivity/pdf/SKILL.md
skills/productivity/pdf/references/ocr-extraction.md
- Network accessline 18
web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 19
web_extract(urls=["https://example.com/report.pdf"]) - Dangerous shell operationline 75
case "$old" in hermes-pdf-pymupdf-venv.??????) rm -rf "$old" ;; esac - Dangerous shell operationline 77
[ "$(readlink hermes-pdf-pymupdf-venv)" = "$stage" ] || rm -rf "$stage" "$stage.lnk" - Dangerous shell operationline 126
case "$old" in hermes-pdf-marker-venv.??????) rm -rf "$old" ;; esac - Dangerous shell operationline 128
[ "$(readlink hermes-pdf-marker-venv)" = "$stage" ] || rm -rf "$stage" "$stage.lnk" - Network accessline 154
# Abstract only (fast) web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 155
web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 157
# Full paper web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 158
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
skills/productivity/pdf/scripts/_raster.py
- Executable scriptline 1
Executable file
skills/productivity/pdf/scripts/extract_marker.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/extract_pymupdf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_create.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_fill_form.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_form_layout.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_make_form.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_merge.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_meta.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_page_image.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_read.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_secure.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_split.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_stamp.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/scripts/pdf_watermark.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/productivity/pdf/tests/test_pdf_skill.py
skills/productivity/teams-meeting-pipeline/SKILL.md
skills/research/arxiv/SKILL.md
- Network accessline 20
| Action | Command | |--------|---------| | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | - Network accessline 21
|--------|---------| | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | | Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` | - Network accessline 22
| Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | - Network accessline 23
| Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` | - Network accessline 24
| Read abstract (web) | `web_extract(urls=["https://arxiv.org/abs/2402.03300"])` | - Network accessline 25
| Read full paper (PDF) | `web_extract(urls=["https://arxiv.org/pdf/2402.03300"])` | - Network accessline 34
curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5" - Dangerous shell operationline 40
curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5&sortBy=submittedDate&sortOrder=descending" | python -c " - Network accessline 40
curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5&sortBy=submittedDate&sortOrder=descending" | python -c " - Network accessline 41
import sys, xml.etree.ElementTree as ET ns = {'a': 'http://www.w3.org/2005/Atom'} root = ET.parse(sys.stdin).getroot() - Network accessline 42
ns = {'a': 'http://www.w3.org/2005/Atom'} - Network accessline 53
print(f' Published: {published} | Categories: {cats}') print(f' Abstract: {summary}...') print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') - Network accessline 54
print(f' Abstract: {summary}...') print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') print() - Network accessline 55
print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') - Network accessline 100
# Latest 10 papers in cs.AI curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI&sortBy=submittedDate&sortOrder=descending&max_results=10" - Network accessline 101
curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI&sortBy=submittedDate&sortOrder=descending&max_results=10" - Network accessline 107
# By arXiv ID curl -s "https://export.arxiv.org/api/query?id_list=2402.03300" - Network accessline 108
curl -s "https://export.arxiv.org/api/query?id_list=2402.03300" - Network accessline 110
# Multiple papers curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001" - Network accessline 111
curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001" - Dangerous shell operationline 120
curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python -c " - Network accessline 120
curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python -c " - Network accessline 121
import sys, xml.etree.ElementTree as ET ns = {'a': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} root = ET.parse(sys.stdin).getroot() - Network accessline 122
ns = {'a': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} - Network accessline 138
print(f' archivePrefix = {{arXiv}},') print(f' primaryClass = {{{primary}}},') print(f' url = {{https://arxiv.org/abs/{raw_id}}}') - Network accessline 139
print(f' primaryClass = {{{primary}}},') print(f' url = {{https://arxiv.org/abs/{raw_id}}}') print('}') - Network accessline 140
print(f' url = {{https://arxiv.org/abs/{raw_id}}}') - Network accessline 151
# Abstract page (fast, metadata + abstract) web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 152
web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 154
# Full paper (PDF → markdown via Firecrawl) web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 155
web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 173
Full list: https://arxiv.org/category_taxonomy - Network accessline 199
# By arXiv ID curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python -m json.tool - Dangerous shell operationline 200
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python -m json.tool - Network accessline 200
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python -m json.tool - Network accessline 202
# By Semantic Scholar paper ID or DOI curl -s "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example?fields=title,citationCount" - Network accessline 203
curl -s "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example?fields=title,citationCount" - Dangerous shell operationline 209
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/citations?fields=title,authors,year,citationCount&limit=10" | python -m json.tool - Network accessline 209
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/citations?fields=title,authors,year,citationCount&limit=10" | python -m json.tool - Dangerous shell operationline 215
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/references?fields=title,authors,year,citationCount&limit=10" | python -m json.tool - Network accessline 215
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/references?fields=title,authors,year,citationCount&limit=10" | python -m json.tool - Dangerous shell operationline 221
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=GRPO+reinforcement+learning&limit=5&fields=title,authors,year,citationCount,externalIds" | python -m json.tool - Network accessline 221
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=GRPO+reinforcement+learning&limit=5&fields=title,authors,year,citationCount,externalIds" | python -m json.tool - Dangerous shell operationline 227
curl -s -X POST "https://api.semanticscholar.org/recommendations/v1/papers/" -H "Content-Type: application/json" -d '{"positivePaperIds": ["arXiv:2402.03300"], "negativePaperIds": []}' | python -m json.tool - Network accessline 227
curl -s -X POST "https://api.semanticscholar.org/recommendations/v1/papers/" \ - Dangerous shell operationline 235
curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python -m json.tool - Network accessline 235
curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python -m json.tool - Network accessline 246
1. **Discover**: `python scripts/search_arxiv.py "your topic" --sort date --max 10` 2. **Assess impact**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID?fields=citationCount,influentialCitationCount"` - Network accessline 247
2. **Assess impact**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID?fields=citationCount,influentialCitationCount"` - Network accessline 248
3. **Read abstract**: `web_extract(urls=["https://arxiv.org/abs/ID"])` 4. **Read full paper**: `web_extract(urls=["https://arxiv.org/pdf/ID"])` 5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/refe - Network accessline 249
4. **Read full paper**: `web_extract(urls=["https://arxiv.org/pdf/ID"])` 5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/references?fields=title,citationCount&limit=20"` 6. **Get recommendations** - Network accessline 250
5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/references?fields=title,citationCount&limit=20"` - Network accessline 252
7. **Track authors**: `curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=NAME"` - Network accessline 264
- Semantic Scholar returns JSON — pipe through `python -m json.tool` for readability - arXiv IDs: old format (`hep-th/0601001`) vs new (`2402.03300`) - PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}` - Network accessline 265
- arXiv IDs: old format (`hep-th/0601001`) vs new (`2402.03300`) - PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}` - HTML (when available): `https://arxiv.org/html/{id}` - Network accessline 266
- PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}` - Network accessline 267
- HTML (when available): `https://arxiv.org/html/{id}` - Network accessline 273
- `arxiv.org/abs/1706.03762v1` points to a **specific** immutable version - When generating citations, preserve the version suffix you actually read to prevent citation drift (a later version may substantially change content) - The API `<id - Network accessline 275
- The API `<id>` field returns the versioned URL (e.g., `http://arxiv.org/abs/1706.03762v7`)
skills/research/arxiv/scripts/search_arxiv.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 18
NS = {'a': 'http://www.w3.org/2005/Atom'} - Network accessline 44
url = "https://export.arxiv.org/api/query?" + "&".join(f"{k}={v}" for k, v in params.items()) - Network accessline 46
req = urllib.request.Request(url, headers={'User-Agent': 'HermesAgent/1.0'}) - Network accessline 47
with urllib.request.urlopen(req, timeout=15) as resp: - Network accessline 57
total = root.find('{http://a9.com/-/spec/opensearch/1.1/}totalResults') - Network accessline 76
print(f" Categories: {cats}") print(f" Abstract: {summary[:300]}{'...' if len(summary) > 300 else ''}") print(f" Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}") - Network accessline 77
print(f" Abstract: {summary[:300]}{'...' if len(summary) > 300 else ''}") print(f" Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}") print() - Network accessline 78
print(f" Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}")
skills/research/grounded-citations/SKILL.md
- Network accessline 61
python "$S" reset # start a clean ledger python "$S" add https://example.com/a --title "A" # prints: [1] - Network accessline 62
python "$S" add https://example.com/a --title "A" # prints: [1] - Network accessline 63
python "$S" add https://example.com/b --title "B" # prints: [2] - Network accessline 167
The quote is rejected unless it appears verbatim in the evidence text (insensitive to whitespace, case, and markdown markup — inline links like `_[ERAP1](https://…)_` in extracted text match the plain prose a reader sees), - Network accessline 168
(insensitive to whitespace, case, and markdown markup — inline links like `_[ERAP1](https://…)_` in extracted text match the plain prose a reader sees), so a paraphrase or misremembered figure cannot masquerade as evidence. - Network accessline 169
`_[ERAP1](https://…)_` in extracted text match the plain prose a reader sees),
skills/research/grounded-citations/references/citation-formats.md
skills/research/grounded-citations/scripts/_hermes_home.py
skills/research/grounded-citations/scripts/sources.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 68
[Credential-related source omitted] - Network accessline 238
# ``web_extract`` returns markdown, so the most citation-worthy sentences are # exactly the ones carrying inline links and emphasis around terms: # "including _[ERAP1](https://…/erap1/)_, _[IL1A](…)_, have also been…" - Network accessline 239
# exactly the ones carrying inline links and emphasis around terms: # "including _[ERAP1](https://…/erap1/)_, _[IL1A](…)_, have also been…" # reads identically to the page a human sees. Matching has to see through that - Network accessline 240
# "including _[ERAP1](https://…/erap1/)_, _[IL1A](…)_, have also been…"
skills/social-media/xurl/SKILL.md
- Network accessline 11
hermes: tags: [twitter, x, social-media, xurl, official-api] homepage: https://github.com/xdevplatform/xurl - Network accessline 12
tags: [twitter, x, social-media, xurl, official-api] homepage: https://github.com/xdevplatform/xurl upstream_skill: https://github.com/openclaw/openclaw/blob/main/skills/xurl/SKILL.md - Network accessline 13
homepage: https://github.com/xdevplatform/xurl - Network accessline 14
upstream_skill: https://github.com/openclaw/openclaw/blob/main/skills/xurl/SKILL.md - Network accessline 58
# Shell script (installs to ~/.local/bin, no sudo, works on Linux + macOS) curl -fsSL https://raw.githubusercontent.com/xdevplatform/xurl/main/install.sh | bash - Dangerous shell operationline 59
curl -fsSL https://raw.githubusercontent.com/xdevplatform/xurl/main/install.sh | bash - Network accessline 59
curl -fsSL https://raw.githubusercontent.com/xdevplatform/xurl/main/install.sh | bash - Network accessline 86
1. Create or open an app at https://developer.x.com/en/portal/dashboard - Network accessline 87
2. Set the redirect URI to `http://localhost:8080/callback` - Access to secretsline 91
[Credential-related source omitted] - Access to secretsline 120
[Credential-related source omitted] - Network accessline 161
Notes: - `POST_ID` accepts full URLs too (e.g. `https://x.com/user/status/1234567890`) — xurl extracts the ID. - Network accessline 162
- `POST_ID` accepts full URLs too (e.g. `https://x.com/user/status/1234567890`) — xurl extracts the ID. - Network accessline 176
xurl reply 1234567890 "Great point!" xurl reply https://x.com/user/status/1234567890 "Agreed!" - Network accessline 177
xurl reply https://x.com/user/status/1234567890 "Agreed!" - Network accessline 189
xurl read 1234567890 xurl read https://x.com/user/status/1234567890 - Network accessline 190
xurl read https://x.com/user/status/1234567890 - Network accessline 300
# Full URLs also work xurl https://api.x.com/2/users/me - Network accessline 301
xurl https://api.x.com/2/users/me - Network accessline 356
xurl read https://x.com/user/status/1234567890 - Network accessline 434
- Upstream CLI: https://github.com/xdevplatform/xurl (X developer platform team, Chris Park et al.) - Network accessline 435
- Upstream agent skill: https://github.com/openclaw/openclaw/blob/main/skills/xurl/SKILL.md
skills/software-development/dogfood/SKILL.md
- Network accessline 64
new_tab('about:blank') # first call only: the hook binds to the current tab cdp('Page.addScriptToEvaluateOnNewDocument', source=HOOK) goto_url("https://example.com/page") - Network accessline 65
cdp('Page.addScriptToEvaluateOnNewDocument', source=HOOK) goto_url("https://example.com/page") wait_for_load() - Network accessline 66
goto_url("https://example.com/page")
skills/software-development/github/references/auth.md
- Dangerous shell operationline 25
3. If `gh` is not installed → use "git-only" method below (no sudo needed) - Network accessline 39
Tell the user to go to: **https://github.com/settings/tokens** - Access to secretsline 54
[Credential-related source omitted] - Network accessline 58
[Credential-related source omitted] - Network accessline 60
git ls-remote https://github.com/<their-username>/<any-repo>.git - Network accessline 90
# Test push access (this should work without any prompts now) git ls-remote https://github.com/<their-username>/<any-repo>.git - Network accessline 91
git ls-remote https://github.com/<their-username>/<any-repo>.git - Access to secretsline 105
[Credential-related source omitted] - Access to secretsline 112
[Credential-related source omitted] - Access to secretsline 115
[Credential-related source omitted] - Network accessline 118
Tell the user to add the public key at: **https://github.com/settings/keys** - Network accessline 133
# Rewrite HTTPS GitHub URLs to SSH automatically git config --global url."git@github.com:".insteadOf "https://github.com/" - Network accessline 134
git config --global url."git@github.com:".insteadOf "https://github.com/" - Network accessline 166
# 1. Request a device code (gh's official client_id) RESP=$(curl -s -X POST -H "Accept: application/json" \ - Network accessline 167
RESP=$(curl -s -X POST -H "Accept: application/json" \ - Network accessline 168
-d "client_id=178c6fc778ccc68e1d6a&scope=repo,read:org,gist" \ https://github.com/login/device/code) DEVICE_CODE=$(echo "$RESP" | sed 's/.*"device_code":"\([^"]*\)".*/\1/') - Network accessline 169
https://github.com/login/device/code) - Network accessline 171
USER_CODE=$(echo "$RESP" | sed 's/.*"user_code":"\([^"]*\)".*/\1/') INTERVAL=$(echo "$RESP" | sed 's/.*"interval":\([0-9]*\).*/\1/'); INTERVAL=${INTERVAL:-5} echo "Tell the user: go to https://github.com/login/device and enter code: $USER_C - Network accessline 173
echo "Tell the user: go to https://github.com/login/device and enter code: $USER_CODE" - Network accessline 177
while true; do sleep "$INTERVAL" POLL=$(curl -s -X POST -H "Accept: application/json" \ - Network accessline 178
sleep "$INTERVAL" POLL=$(curl -s -X POST -H "Accept: application/json" \ -d "client_id=178c6fc778ccc68e1d6a&device_code=${DEVICE_CODE}&grant_type=urn:ietf:params:oauth:grant-type:device_code" \ - Network accessline 179
[Credential-related source omitted] - Access to secretsline 179
[Credential-related source omitted] - Network accessline 181
[Credential-related source omitted] - Access to secretsline 181
[Credential-related source omitted] - Access to secretsline 183
[Credential-related source omitted] - Access to secretsline 186
[Credential-related source omitted] - Access to secretsline 187
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Network accessline 211
[Credential-related source omitted] - Network accessline 212
[Credential-related source omitted] - Network accessline 213
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted] - Access to secretsline 217
[Credential-related source omitted] - Access to secretsline 218
[Credential-related source omitted] - Access to secretsline 235
[Credential-related source omitted] - Access to secretsline 253
[Credential-related source omitted] - Network accessline 255
# Then use in curl calls: - Network accessline 256
[Credential-related source omitted] - Access to secretsline 256
[Credential-related source omitted] - Network accessline 257
https://api.github.com/user - Network accessline 274
# Try gh first, fall back to git + curl if command -v gh &>/dev/null && gh auth status &>/dev/null; then - Access to secretsline 277
[Credential-related source omitted] - Access to secretsline 279
[Credential-related source omitted] - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 282
[Credential-related source omitted] - Access to secretsline 283
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 302
[Credential-related source omitted] - Dangerous shell operationline 303
| `gh: command not found` + no sudo | Use git-only Method 1 above — no installation needed |
skills/software-development/github/references/ci-troubleshooting.md
- Network accessline 11
# With curl — download and extract - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
https://api.github.com/repos/$GH_OWNER/$GH_REPO/actions/runs/<RUN_ID>/logs \ - Network accessline 104
fatal: could not read Username for 'https://github.com': No such device or address - Network accessline 180
[Credential-related source omitted] - Network accessline 181
[Credential-related source omitted] - Network accessline 182
[Credential-related source omitted]
skills/software-development/github/references/code-review.md
- Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 79
[Credential-related source omitted] - Network accessline 130
# Get PR details curl -s \ - Dangerous shell operationline 131
[Credential-related source omitted] - Network accessline 131
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted] - Network accessline 132
[Credential-related source omitted] - Access to secretsline 132
[Credential-related source omitted] - Network accessline 133
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 143
# List changed files curl -s \ - Dangerous shell operationline 144
[Credential-related source omitted] - Network accessline 144
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Network accessline 145
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted] - Network accessline 146
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/files \ - Network accessline 185
[Credential-related source omitted] - Access to secretsline 185
[Credential-related source omitted] - Network accessline 186
[Credential-related source omitted] - Access to secretsline 186
[Credential-related source omitted] - Network accessline 187
https://api.github.com/repos/$OWNER/$REPO/issues/$PR_NUMBER/comments \ - Network accessline 210
# Get the head commit SHA HEAD_SHA=$(curl -s \ - Dangerous shell operationline 211
[Credential-related source omitted] - Network accessline 211
[Credential-related source omitted] - Access to secretsline 211
[Credential-related source omitted] - Network accessline 212
[Credential-related source omitted] - Access to secretsline 212
[Credential-related source omitted] - Network accessline 213
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 216
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted] - Network accessline 217
[Credential-related source omitted] - Access to secretsline 217
[Credential-related source omitted] - Network accessline 218
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/comments \ - Network accessline 238
**With curl — multi-comment review submitted atomically:** - Dangerous shell operationline 241
[Credential-related source omitted] - Network accessline 241
[Credential-related source omitted] - Access to secretsline 241
[Credential-related source omitted] - Network accessline 242
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Network accessline 243
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 246
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 247
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Network accessline 248
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/reviews \ - Network accessline 344
[Credential-related source omitted] - Network accessline 345
[Credential-related source omitted] - Access to secretsline 345
[Credential-related source omitted] - Network accessline 346
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER - Network accessline 348
[Credential-related source omitted] - Network accessline 349
[Credential-related source omitted] - Access to secretsline 349
[Credential-related source omitted] - Network accessline 350
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/files - Network accessline 405
**With curl — atomic review with multiple inline comments:** - Dangerous shell operationline 407
[Credential-related source omitted] - Network accessline 407
[Credential-related source omitted] - Access to secretsline 407
[Credential-related source omitted] - Network accessline 408
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER \ - Network accessline 411
# Build the review JSON — event is APPROVE, REQUEST_CHANGES, or COMMENT curl -s -X POST \ - Network accessline 412
[Credential-related source omitted] - Access to secretsline 412
[Credential-related source omitted] - Network accessline 413
[Credential-related source omitted] - Access to secretsline 413
[Credential-related source omitted] - Network accessline 414
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/reviews \
skills/software-development/github/references/conventional-commits.md
skills/software-development/github/references/github-api-cheatsheet.md
- Network accessline 3
Base URL: `https://api.github.com` - Access to secretsline 5
[Credential-related source omitted] - Access to secretsline 7
[Credential-related source omitted] - Network accessline 92
| Get release | GET | `/repos/{owner}/{repo}/releases/{id}` | | Delete release | DELETE | `/repos/{owner}/{repo}/releases/{id}` | | Upload asset | POST | `https://uploads.github.com/repos/{owner}/{repo}/releases/{id}/assets?name={filename}` - Network accessline 94
| Upload asset | POST | `https://uploads.github.com/repos/{owner}/{repo}/releases/{id}/assets?name={filename}` | - Network accessline 132
[Credential-related source omitted] - Network accessline 133
[Credential-related source omitted] - Access to secretsline 133
[Credential-related source omitted] - Network accessline 135
## Common curl Patterns - Network accessline 138
[Credential-related source omitted] - Network accessline 139
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 140
https://api.github.com/repos/$GH_OWNER/$GH_REPO - Network accessline 142
# POST with JSON body curl -s -X POST \ - Network accessline 143
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Network accessline 144
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Network accessline 145
https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues \ - Network accessline 148
# PATCH (update) curl -s -X PATCH \ - Network accessline 149
[Credential-related source omitted] - Access to secretsline 149
[Credential-related source omitted] - Network accessline 150
[Credential-related source omitted] - Access to secretsline 150
[Credential-related source omitted] - Network accessline 151
https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues/42 \ - Network accessline 154
# DELETE curl -s -X DELETE \ - Network accessline 155
[Credential-related source omitted] - Access to secretsline 155
[Credential-related source omitted] - Access to secretsline 156
[Credential-related source omitted] - Network accessline 157
https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues/42/labels/bug - Network accessline 159
# Parse JSON response with python3 curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])" - Dangerous shell operationline 160
curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])" - Network accessline 160
curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])"
skills/software-development/github/references/issues.md
- Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Network accessline 49
# List open issues curl -s \ - Dangerous shell operationline 50
[Credential-related source omitted] - Network accessline 50
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 51
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 52
"https://api.github.com/repos/$OWNER/$REPO/issues?state=open&per_page=20" \ - Network accessline 60
# Filter by label curl -s \ - Dangerous shell operationline 61
[Credential-related source omitted] - Network accessline 61
[Credential-related source omitted] - Access to secretsline 61
[Credential-related source omitted] - Network accessline 62
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Network accessline 63
"https://api.github.com/repos/$OWNER/$REPO/issues?state=open&labels=bug&per_page=20" \ - Network accessline 70
# View a specific issue curl -s \ - Dangerous shell operationline 71
[Credential-related source omitted] - Network accessline 71
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Network accessline 72
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Network accessline 73
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 84
# Search issues curl -s \ - Dangerous shell operationline 85
[Credential-related source omitted] - Network accessline 85
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted] - Network accessline 86
[Credential-related source omitted] - Access to secretsline 86
[Credential-related source omitted] - Network accessline 87
"https://api.github.com/search/issues?q=authentication+error+repo:$OWNER/$REPO" \ - Network accessline 119
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted] - Network accessline 120
[Credential-related source omitted] - Access to secretsline 120
[Credential-related source omitted] - Network accessline 121
https://api.github.com/repos/$OWNER/$REPO/issues \ - Network accessline 181
# Add labels curl -s -X POST \ - Network accessline 182
[Credential-related source omitted] - Access to secretsline 182
[Credential-related source omitted] - Network accessline 183
[Credential-related source omitted] - Access to secretsline 183
[Credential-related source omitted] - Network accessline 184
https://api.github.com/repos/$OWNER/$REPO/issues/42/labels \ - Network accessline 187
# Remove a label curl -s -X DELETE \ - Network accessline 188
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Network accessline 190
https://api.github.com/repos/$OWNER/$REPO/issues/42/labels/needs-triage - Network accessline 192
# List available labels in the repo curl -s \ - Dangerous shell operationline 193
[Credential-related source omitted] - Network accessline 193
[Credential-related source omitted] - Access to secretsline 193
[Credential-related source omitted] - Network accessline 194
[Credential-related source omitted] - Access to secretsline 194
[Credential-related source omitted] - Network accessline 195
https://api.github.com/repos/$OWNER/$REPO/labels \ - Network accessline 214
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Network accessline 216
https://api.github.com/repos/$OWNER/$REPO/issues/42/assignees \ - Network accessline 231
[Credential-related source omitted] - Access to secretsline 231
[Credential-related source omitted] - Network accessline 232
[Credential-related source omitted] - Access to secretsline 232
[Credential-related source omitted] - Network accessline 233
https://api.github.com/repos/$OWNER/$REPO/issues/42/comments \ - Network accessline 250
# Close curl -s -X PATCH \ - Network accessline 251
[Credential-related source omitted] - Access to secretsline 251
[Credential-related source omitted] - Network accessline 252
[Credential-related source omitted] - Access to secretsline 252
[Credential-related source omitted] - Network accessline 253
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 256
# Reopen curl -s -X PATCH \ - Network accessline 257
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 258
[Credential-related source omitted] - Access to secretsline 258
[Credential-related source omitted] - Network accessline 259
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 298
# With curl curl -s \ - Dangerous shell operationline 299
[Credential-related source omitted] - Network accessline 299
[Credential-related source omitted] - Access to secretsline 299
[Credential-related source omitted] - Network accessline 300
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Network accessline 301
"https://api.github.com/repos/$OWNER/$REPO/issues?labels=needs-triage&state=open" \ - Network accessline 332
# List issue numbers with a label, then close each curl -s \ - Dangerous shell operationline 333
[Credential-related source omitted] - Network accessline 333
[Credential-related source omitted] - Access to secretsline 333
[Credential-related source omitted] - Network accessline 334
[Credential-related source omitted] - Access to secretsline 334
[Credential-related source omitted] - Network accessline 335
"https://api.github.com/repos/$OWNER/$REPO/issues?labels=wontfix&state=open" \ - Network accessline 336
| python -c "import sys,json; [print(i['number']) for i in json.load(sys.stdin)]" \ | while read num; do curl -s -X PATCH \ - Network accessline 337
[Credential-related source omitted] - Network accessline 338
[Credential-related source omitted] - Access to secretsline 338
[Credential-related source omitted] - Access to secretsline 339
[Credential-related source omitted] - Network accessline 340
https://api.github.com/repos/$OWNER/$REPO/issues/$num \ - Network accessline 348
| Action | gh | curl endpoint |
skills/software-development/github/references/pr-workflow.md
- Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted] - Network accessline 123
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Network accessline 124
[Credential-related source omitted] - Access to secretsline 124
[Credential-related source omitted] - Network accessline 125
-H "Accept: application/vnd.github.v3+json" \ https://api.github.com/repos/$OWNER/$REPO/pulls \ -d "{ - Network accessline 126
https://api.github.com/repos/$OWNER/$REPO/pulls \ - Network accessline 159
# Query the combined status curl -s \ - Dangerous shell operationline 160
[Credential-related source omitted] - Network accessline 160
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Network accessline 161
[Credential-related source omitted] - Access to secretsline 161
[Credential-related source omitted] - Network accessline 162
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \ - Network accessline 170
# Also check GitHub Actions check runs (separate endpoint) curl -s \ - Dangerous shell operationline 171
[Credential-related source omitted] - Network accessline 171
[Credential-related source omitted] - Access to secretsline 171
[Credential-related source omitted] - Network accessline 172
[Credential-related source omitted] - Access to secretsline 172
[Credential-related source omitted] - Network accessline 173
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \ - Network accessline 185
SHA=$(git rev-parse HEAD) for i in $(seq 1 20); do STATUS=$(curl -s \ - Network accessline 186
[Credential-related source omitted] - Dangerous shell operationline 187
[Credential-related source omitted] - Network accessline 187
[Credential-related source omitted] - Access to secretsline 187
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Network accessline 189
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \ - Network accessline 220
# List workflow runs on this branch curl -s \ - Dangerous shell operationline 221
[Credential-related source omitted] - Network accessline 221
[Credential-related source omitted] - Access to secretsline 221
[Credential-related source omitted] - Network accessline 222
[Credential-related source omitted] - Access to secretsline 222
[Credential-related source omitted] - Network accessline 223
"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5" \ - Network accessline 230
# Get failed job logs (download as zip, extract, read) RUN_ID=<run_id> curl -s -L \ - Network accessline 231
[Credential-related source omitted] - Network accessline 232
[Credential-related source omitted] - Access to secretsline 232
[Credential-related source omitted] - Network accessline 233
[Credential-related source omitted] - Access to secretsline 233
[Credential-related source omitted] - Network accessline 234
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \ - Network accessline 281
# Merge the PR via API (squash) curl -s -X PUT \ - Network accessline 282
[Credential-related source omitted] - Access to secretsline 282
[Credential-related source omitted] - Network accessline 283
[Credential-related source omitted] - Access to secretsline 283
[Credential-related source omitted] - Network accessline 284
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \ - Network accessline 304
# Auto-merge requires the repo to have it enabled in settings. # This uses the GraphQL API since REST doesn't support auto-merge. PR_NODE_ID=$(curl -s \ - Network accessline 305
[Credential-related source omitted] - Dangerous shell operationline 306
[Credential-related source omitted] - Network accessline 306
[Credential-related source omitted] - Access to secretsline 306
[Credential-related source omitted] - Network accessline 307
[Credential-related source omitted] - Access to secretsline 307
[Credential-related source omitted] - Network accessline 308
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 311
[Credential-related source omitted] - Access to secretsline 311
[Credential-related source omitted] - Network accessline 312
[Credential-related source omitted] - Access to secretsline 312
[Credential-related source omitted] - Network accessline 313
https://api.github.com/graphql \ - Network accessline 337
# 6. Create PR (picks gh or curl based on what's available) - Network accessline 347
| Action | gh | git + curl | - Network accessline 348
[Credential-related source omitted] - Network accessline 349
[Credential-related source omitted] - Access to secretsline 349
[Credential-related source omitted] - Network accessline 350
| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H "Accept: application/vnd.github.diff" ...` | - Network accessline 351
| Add comment | `gh pr comment N --body "..."` | `curl -X POST .../issues/N/comments -d '{"body":"..."}'` | - Network accessline 352
| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{"reviewers":["user"]}'` | - Network accessline 353
| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{"state":"closed"}'` |
skills/software-development/github/references/repo-management.md
- Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 19
[Credential-related source omitted] - Network accessline 27
[Credential-related source omitted] - Network accessline 28
[Credential-related source omitted] - Dangerous shell operationline 29
[Credential-related source omitted] - Network accessline 29
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 49
# Clone via HTTPS (works with credential helper or token-embedded URL) git clone https://github.com/owner/repo-name.git - Network accessline 50
git clone https://github.com/owner/repo-name.git - Network accessline 52
# Clone into a specific directory git clone https://github.com/owner/repo-name.git ./my-local-dir - Network accessline 53
git clone https://github.com/owner/repo-name.git ./my-local-dir - Network accessline 55
# Shallow clone (faster for large repos) git clone --depth 1 https://github.com/owner/repo-name.git - Network accessline 56
git clone --depth 1 https://github.com/owner/repo-name.git - Network accessline 58
# Clone a specific branch git clone --branch develop https://github.com/owner/repo-name.git - Network accessline 59
git clone --branch develop https://github.com/owner/repo-name.git - Network accessline 94
# Create the remote repo via API curl -s -X POST \ - Network accessline 95
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Network accessline 96
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Network accessline 97
https://api.github.com/user/repos \ - Network accessline 106
# Clone it git clone https://github.com/$GH_USER/my-new-project.git - Network accessline 107
git clone https://github.com/$GH_USER/my-new-project.git - Network accessline 113
git add . git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git - Network accessline 114
git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git git push -u origin main - Network accessline 115
git remote add origin https://github.com/$GH_USER/my-new-project.git - Network accessline 122
[Credential-related source omitted] - Access to secretsline 122
[Credential-related source omitted] - Network accessline 123
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Network accessline 124
https://api.github.com/orgs/my-org/repos \ - Network accessline 139
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 140
[Credential-related source omitted] - Access to secretsline 140
[Credential-related source omitted] - Network accessline 141
https://api.github.com/repos/owner/template-repo/generate \ - Network accessline 156
# Create the fork via API curl -s -X POST \ - Network accessline 157
[Credential-related source omitted] - Access to secretsline 157
[Credential-related source omitted] - Access to secretsline 158
[Credential-related source omitted] - Network accessline 159
https://api.github.com/repos/owner/repo-name/forks - Network accessline 161
# Wait a moment for GitHub to create it, then clone sleep 3 git clone https://github.com/$GH_USER/repo-name.git - Network accessline 162
sleep 3 git clone https://github.com/$GH_USER/repo-name.git cd repo-name - Network accessline 163
git clone https://github.com/$GH_USER/repo-name.git - Network accessline 166
# Add the original repo as "upstream" remote git remote add upstream https://github.com/owner/repo-name.git - Network accessline 167
git remote add upstream https://github.com/owner/repo-name.git - Network accessline 199
# View repo details curl -s \ - Dangerous shell operationline 200
[Credential-related source omitted] - Network accessline 200
[Credential-related source omitted] - Access to secretsline 200
[Credential-related source omitted] - Network accessline 201
[Credential-related source omitted] - Access to secretsline 201
[Credential-related source omitted] - Network accessline 202
https://api.github.com/repos/$OWNER/$REPO \ - Network accessline 212
# List your repos curl -s \ - Dangerous shell operationline 213
[Credential-related source omitted] - Network accessline 213
[Credential-related source omitted] - Access to secretsline 213
[Credential-related source omitted] - Network accessline 214
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 215
"https://api.github.com/user/repos?per_page=20&sort=updated" \ - Network accessline 222
# Search repos curl -s \ - Dangerous shell operationline 223
curl -s "https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10" | python -c " - Network accessline 223
curl -s \ - Network accessline 224
"https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10" \ - Network accessline 246
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 247
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Network accessline 248
https://api.github.com/repos/$OWNER/$REPO \ - Network accessline 256
# Update topics curl -s -X PUT \ - Network accessline 257
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 258
[Credential-related source omitted] - Access to secretsline 258
[Credential-related source omitted] - Network accessline 259
-H "Accept: application/vnd.github.mercy-preview+json" \ https://api.github.com/repos/$OWNER/$REPO/topics \ -d '{"names": ["machine-learning", "python", "automation"]}' - Network accessline 260
https://api.github.com/repos/$OWNER/$REPO/topics \ - Network accessline 267
# View current protection curl -s \ - Network accessline 268
[Credential-related source omitted] - Access to secretsline 268
[Credential-related source omitted] - Access to secretsline 269
[Credential-related source omitted] - Network accessline 270
https://api.github.com/repos/$OWNER/$REPO/branches/main/protection - Network accessline 272
# Set up branch protection curl -s -X PUT \ - Network accessline 273
[Credential-related source omitted] - Access to secretsline 273
[Credential-related source omitted] - Network accessline 274
[Credential-related source omitted] - Access to secretsline 274
[Credential-related source omitted] - Network accessline 275
https://api.github.com/repos/$OWNER/$REPO/branches/main/protection \ - Access to secretsline 294
[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Access to secretsline 297
[Credential-related source omitted] - Network accessline 305
# Get the repo's public key for encrypting secrets curl -s \ - Network accessline 306
[Credential-related source omitted] - Access to secretsline 306
[Credential-related source omitted] - Access to secretsline 307
[Credential-related source omitted] - Network accessline 308
https://api.github.com/repos/$OWNER/$REPO/actions/secrets/public-key - Network accessline 329
# Then PUT the encrypted secret curl -s -X PUT \ - Network accessline 330
[Credential-related source omitted] - Access to secretsline 330
[Credential-related source omitted] - Network accessline 331
[Credential-related source omitted] - Access to secretsline 331
[Credential-related source omitted] - Network accessline 332
[Credential-related source omitted] - Access to secretsline 332
[Credential-related source omitted] - Network accessline 335
# List secrets (names only, values hidden) curl -s \ - Dangerous shell operationline 336
[Credential-related source omitted] - Network accessline 336
[Credential-related source omitted] - Access to secretsline 336
[Credential-related source omitted] - Network accessline 337
[Credential-related source omitted] - Access to secretsline 337
[Credential-related source omitted] - Network accessline 338
https://api.github.com/repos/$OWNER/$REPO/actions/secrets \ - Network accessline 362
# Create a release curl -s -X POST \ - Network accessline 363
[Credential-related source omitted] - Access to secretsline 363
[Credential-related source omitted] - Network accessline 364
[Credential-related source omitted] - Access to secretsline 364
[Credential-related source omitted] - Network accessline 365
https://api.github.com/repos/$OWNER/$REPO/releases \ - Network accessline 375
# List releases curl -s \ - Dangerous shell operationline 376
[Credential-related source omitted] - Network accessline 376
[Credential-related source omitted] - Access to secretsline 376
[Credential-related source omitted] - Network accessline 377
[Credential-related source omitted] - Access to secretsline 377
[Credential-related source omitted] - Network accessline 378
https://api.github.com/repos/$OWNER/$REPO/releases \ - Network accessline 385
# Upload a release asset (binary file) RELEASE_ID=<id_from_create_response> curl -s -X POST \ - Network accessline 386
[Credential-related source omitted] - Network accessline 387
[Credential-related source omitted] - Access to secretsline 387
[Credential-related source omitted] - Network accessline 388
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Network accessline 389
-H "Content-Type: application/octet-stream" \ "https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \ --data-binary @./dist/binary-amd64 - Network accessline 390
"https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \ - Network accessline 412
# List workflows curl -s \ - Dangerous shell operationline 413
[Credential-related source omitted] - Network accessline 413
[Credential-related source omitted] - Access to secretsline 413
[Credential-related source omitted] - Network accessline 414
[Credential-related source omitted] - Access to secretsline 414
[Credential-related source omitted] - Network accessline 415
https://api.github.com/repos/$OWNER/$REPO/actions/workflows \ - Network accessline 421
# List recent runs curl -s \ - Dangerous shell operationline 422
[Credential-related source omitted] - Network accessline 422
[Credential-related source omitted] - Access to secretsline 422
[Credential-related source omitted] - Network accessline 423
[Credential-related source omitted] - Access to secretsline 423
[Credential-related source omitted] - Network accessline 424
"https://api.github.com/repos/$OWNER/$REPO/actions/runs?per_page=10" \ - Network accessline 430
# Download failed run logs RUN_ID=<run_id> curl -s -L \ - Network accessline 431
[Credential-related source omitted] - Network accessline 432
[Credential-related source omitted] - Access to secretsline 432
[Credential-related source omitted] - Network accessline 433
[Credential-related source omitted] - Access to secretsline 433
[Credential-related source omitted] - Network accessline 434
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \ - Network accessline 438
# Re-run a failed workflow curl -s -X POST \ - Network accessline 439
[Credential-related source omitted] - Access to secretsline 439
[Credential-related source omitted] - Access to secretsline 440
[Credential-related source omitted] - Network accessline 441
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun - Network accessline 443
# Re-run only failed jobs curl -s -X POST \ - Network accessline 444
[Credential-related source omitted] - Access to secretsline 444
[Credential-related source omitted] - Access to secretsline 445
[Credential-related source omitted] - Network accessline 446
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun-failed-jobs - Network accessline 448
# Trigger a workflow manually (workflow_dispatch) WORKFLOW_ID=<workflow_id_or_filename> curl -s -X POST \ - Network accessline 449
[Credential-related source omitted] - Network accessline 450
[Credential-related source omitted] - Access to secretsline 450
[Credential-related source omitted] - Network accessline 451
[Credential-related source omitted] - Access to secretsline 451
[Credential-related source omitted] - Network accessline 452
https://api.github.com/repos/$OWNER/$REPO/actions/workflows/$WORKFLOW_ID/dispatches \ - Network accessline 468
# Create a gist curl -s -X POST \ - Network accessline 469
[Credential-related source omitted] - Access to secretsline 469
[Credential-related source omitted] - Network accessline 470
[Credential-related source omitted] - Access to secretsline 470
[Credential-related source omitted] - Network accessline 471
https://api.github.com/gists \ - Network accessline 480
# List your gists curl -s \ - Dangerous shell operationline 481
[Credential-related source omitted] - Network accessline 481
[Credential-related source omitted] - Access to secretsline 481
[Credential-related source omitted] - Network accessline 482
[Credential-related source omitted] - Access to secretsline 482
[Credential-related source omitted] - Network accessline 483
https://api.github.com/gists \ - Network accessline 493
| Action | gh | git + curl | - Network accessline 494
|--------|-----|-----------| | Clone | `gh repo clone o/r` | `git clone https://github.com/o/r.git` | | Create repo | `gh repo create name --public` | `curl POST /user/repos` | - Network accessline 495
| Clone | `gh repo clone o/r` | `git clone https://github.com/o/r.git` | | Create repo | `gh repo create name --public` | `curl POST /user/repos` | | Fork | `gh repo fork o/r --clone` | `curl POST /repos/o/r/forks` + `git clone` | - Network accessline 496
| Create repo | `gh repo create name --public` | `curl POST /user/repos` | - Network accessline 497
| Fork | `gh repo fork o/r --clone` | `curl POST /repos/o/r/forks` + `git clone` | - Network accessline 498
| Repo info | `gh repo view o/r` | `curl GET /repos/o/r` | - Network accessline 499
| Edit settings | `gh repo edit --...` | `curl PATCH /repos/o/r` | - Network accessline 500
| Create release | `gh release create v1.0` | `curl POST /repos/o/r/releases` | - Network accessline 501
| List workflows | `gh workflow list` | `curl GET /repos/o/r/actions/workflows` | - Network accessline 502
| Rerun CI | `gh run rerun ID` | `curl POST /repos/o/r/actions/runs/ID/rerun` | - Network accessline 503
| Set secret | `gh secret set KEY` | `curl PUT /repos/o/r/actions/secrets/KEY` (+ encryption) |
skills/software-development/github/scripts/gh-env.sh
- Executable scriptline 1
#!/usr/bin/env bash - Access to secretsline 18
[Credential-related source omitted] - Network accessline 24
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Network accessline 25
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Network accessline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 29
GH_AUTH_METHOD="curl" fi elif [ -f "$HOME/.git-credentials" ]; then - Access to secretsline 31
[Credential-related source omitted] - Network accessline 33
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Network accessline 34
GH_AUTH_METHOD="curl" fi fi - Network accessline 38
# Resolve username for curl method - Network accessline 39
if [ "$GH_AUTH_METHOD" = "curl" ] && [ -z "$GH_USER" ]; then - Dangerous shell operationline 40
[Credential-related source omitted] - Network accessline 40
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 41
https://api.github.com/user 2>/dev/null \ - Network accessline 61
echo "GitHub Auth: $GH_AUTH_METHOD" [ -n "$GH_USER" ] && echo "User: $GH_USER" - Network accessline 62
[ -n "$GH_USER" ] && echo "User: $GH_USER" [ -n "$GH_OWNER_REPO" ] && echo "Repo: $GH_OWNER_REPO" [ "$GH_AUTH_METHOD" = "none" ] && echo "⚠ Not authenticated — see github-auth skill" - Network accessline 64
[ "$GH_AUTH_METHOD" = "none" ] && echo "⚠ Not authenticated — see github-auth skill"
skills/software-development/github/scripts/git-credential-token.py
skills/software-development/requesting-code-review/SKILL.md
skills/software-development/spike/SKILL.md
- Network accessline 81
- `web_search("python websocket streaming libraries 2025")` — find candidates - `web_extract(urls=["https://websockets.readthedocs.io/..."])` — read the actual docs (returns markdown) - Network accessline 82
- `web_extract(urls=["https://websockets.readthedocs.io/..."])` — read the actual docs (returns markdown) - Network accessline 197
Adapted from the GSD (Get Shit Done) project's `/gsd-spike` workflow — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)). The full GSD system offers persistent spike state, MANIFEST tracki
skills/web/blocked-page-recovery/SKILL.md
- Access to secretsline 26
[Credential-related source omitted] - Network accessline 34
python3 scripts/recover_page.py "https://example.com/blocked-article" --json - Network accessline 58
# Discovery: returns closest snapshot URL + timestamp as JSON curl -sL "https://archive.org/wayback/available?url={URL}" - Network accessline 59
curl -sL "https://archive.org/wayback/available?url={URL}" - Network accessline 66
curl -sL "https://web.archive.org/cdx/search/cdx?url={URL}&output=json&limit=10" - Network accessline 81
for d in archive.ph archive.md archive.li archive.is; do curl -sL --max-time 20 "https://$d/newest/{URL}" -o ~/.hermes/cache/scratch/page.html \ - Network accessline 82
curl -sL --max-time 20 "https://$d/newest/{URL}" -o ~/.hermes/cache/scratch/page.html \ - Access to secretsline 90
[Credential-related source omitted] - Network accessline 97
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted]
skills/web/blocked-page-recovery/scripts/recover_page.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 7
[Credential-related source omitted] - Network accessline 61
retries_on_429: int = 2, ) -> tuple[int, bytes]: req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT, **(headers or {})}) - Network accessline 62
) -> tuple[int, bytes]: req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT, **(headers or {})}) for attempt in range(retries_on_429 + 1): - Network accessline 63
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT, **(headers or {})}) - Network accessline 64
for attempt in range(retries_on_429 + 1): try: with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 65
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.status, resp.read() - Network accessline 66
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 80
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 81
try: with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.status, resp.read(), resp.geturl() - Network accessline 82
with urllib.request.urlopen(req, timeout=timeout) as resp: - Network accessline 114
snap_url = None snap_ts = None discovery = "https://archive.org/wayback/available?url=" + urllib.parse.quote(url, safe="") - Network accessline 115
snap_ts = None discovery = "https://archive.org/wayback/available?url=" + urllib.parse.quote(url, safe="") status, raw = _fetch(discovery, timeout) - Network accessline 116
discovery = "https://archive.org/wayback/available?url=" + urllib.parse.quote(url, safe="") - Network accessline 123
if closest.get("available") and closest.get("url"): snap_url = closest["url"].replace( "http://web.archive.org", "https://web.archive.org" - Network accessline 124
snap_url = closest["url"].replace( "http://web.archive.org", "https://web.archive.org" ) - Network accessline 125
"http://web.archive.org", "https://web.archive.org" - Network accessline 132
snap_url = "https://web.archive.org/web/2/" + url - Network accessline 148
def try_archive_today(url: str, timeout: int) -> dict | None: for host in ARCHIVE_TODAY_HOSTS: fetch_url = f"https://{host}/newest/{url}" - Network accessline 149
for host in ARCHIVE_TODAY_HOSTS: fetch_url = f"https://{host}/newest/{url}" status, body = _fetch(fetch_url, timeout) - Network accessline 150
fetch_url = f"https://{host}/newest/{url}" - Access to secretsline 167
[Credential-related source omitted] - Network accessline 169
[Credential-related source omitted] - Network accessline 170
[Credential-related source omitted] - Network accessline 171
[Credential-related source omitted] - Network accessline 177
"provenance": "live", "snapshot_timestamp": None, "source_url": "https://r.jina.ai/" + url, - Network accessline 178
"snapshot_timestamp": None, "source_url": "https://r.jina.ai/" + url, "body": body, - Network accessline 179
"source_url": "https://r.jina.ai/" + url,
tests/skills/test_unbroker_skill.py
- Executable scriptline 1
Executable file - Executable scriptline 1
Executable file; content unavailable - Access to secretsline 56
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Network accessline 172
return {"id": bid, "name": bid.title(), "priority": "high", "search": {"by": ["name"]}, "optout": {"method": "web_form", "url": f"https://{bid}.example/optout", - Network accessline 173
"search": {"by": ["name"]}, "optout": {"method": "web_form", "url": f"https://{bid}.example/optout", "requires": requires or {}, "inputs": ["full_name"], "owns": owns or [], - Network accessline 174
"optout": {"method": "web_form", "url": f"https://{bid}.example/optout", - Network accessline 313
out = legal.render_optout_email(b, {"full_name": "Jane Q. Public", "contact_email": "jane@example.com", "listing_urls": ["https://www.spokeo.com/jane"]}) - Network accessline 314
"contact_email": "jane@example.com", "listing_urls": ["https://www.spokeo.com/jane"]}) assert "Jane Q. Public" in out and "https://www.spokeo.com/jane" in out - Network accessline 315
"listing_urls": ["https://www.spokeo.com/jane"]}) - Network accessline 316
assert "Jane Q. Public" in out and "https://www.spokeo.com/jane" in out - Network accessline 337
Find your information and opt out of [people search](https://www.beenverified.com/app/optout/search). - Network accessline 340
[Find your information](https://www.mylife.com), and then [opt out](https://www.mylife.com/privacyrequest). - Network accessline 343
To opt out, [upload an ID](https://pimeyes.com/en/opt-out-request-form). - Access to secretsline 391
[Credential-related source omitted] - Access to secretsline 395
[Credential-related source omitted] - Network accessline 466
def test_verification_link_from_messages_is_domain_scoped(): broker = {"id": "spokeo", "name": "Spokeo", "search": {"url": "https://www.spokeo.com/"}, - Network accessline 467
broker = {"id": "spokeo", "name": "Spokeo", "search": {"url": "https://www.spokeo.com/"}, "optout": {"url": "https://www.spokeo.com/optout"}} - Network accessline 468
"search": {"url": "https://www.spokeo.com/"}, - Network accessline 469
"optout": {"url": "https://www.spokeo.com/optout"}} - Network accessline 470
phish = {"from": "phisher@evil.example", "subject": "verify now", "text": "click https://evil.example/optout/verify?x=1"} real = {"from": "no-reply@spokeo.com", "subject": "Confirm your opt out", - Network accessline 471
"text": "click https://evil.example/optout/verify?x=1"} - Network accessline 472
real = {"from": "no-reply@spokeo.com", "subject": "Confirm your opt out", "text": "Confirm here: https://www.spokeo.com/optout/verify/abc123"} hit = emailer.link_from_messages([phish, real], broker) - Network accessline 473
"text": "Confirm here: https://www.spokeo.com/optout/verify/abc123"} - Network accessline 475
assert hit["link"] == "https://www.spokeo.com/optout/verify/abc123" - Access to secretsline 525
[Credential-related source omitted] - Network accessline 647
"--email", "jane@example.com", "--consent"])["subject_id"] _run(["record", sid, "radaris", "found", "--found", "true", "--evidence", '{"listing_urls": ["https://radaris.com/p/x"]}']) - Network accessline 648
_run(["record", sid, "radaris", "found", "--found", "true", "--evidence", '{"listing_urls": ["https://radaris.com/p/x"]}']) shown = _run(["show", sid, "radaris"]) - Network accessline 649
"--evidence", '{"listing_urls": ["https://radaris.com/p/x"]}']) - Network accessline 651
assert shown["broker"] == "radaris" and shown["state"] == "found" assert shown["found"] is True assert shown["evidence"].get("listing_urls") == ["https://radaris.com/p/x"] - Network accessline 653
assert shown["evidence"].get("listing_urls") == ["https://radaris.com/p/x"] - Network accessline 724
out = _run(["dnc", sid]) assert out["eligible"] is True assert out["dnc_url"] == "https://www.donotcall.gov/register.html" - Network accessline 725
assert out["eligible"] is True assert out["dnc_url"] == "https://www.donotcall.gov/register.html" assert "72 hours" in " ".join(out["steps"]) - Network accessline 726
assert out["dnc_url"] == "https://www.donotcall.gov/register.html" - Network accessline 777
try: _run(["render-email", sid, "spokeo", "--kind", "ccpa_indirect", "--listing", "https://www.spokeo.com/x"]) - Network accessline 778
_run(["render-email", sid, "spokeo", "--kind", "ccpa_indirect", "--listing", "https://www.spokeo.com/x"]) except SystemExit as e: - Network accessline 779
"--listing", "https://www.spokeo.com/x"]) - Network accessline 786
try: _run(["send-email", sid, "spokeo", "--kind", "generic", "--listing", "https://www.spokeo.com/x"]) - Network accessline 787
_run(["send-email", sid, "spokeo", "--kind", "generic", "--listing", "https://www.spokeo.com/x"]) except SystemExit as e: - Network accessline 788
"--listing", "https://www.spokeo.com/x"]) - Network accessline 794
out = _run(["render-email", sid, "spokeo", "--kind", "generic", "--listing", "https://www.spokeo.com/x"]) - Network accessline 795
"--listing", "https://www.spokeo.com/x"]) - Network accessline 803
d = dossier.load(sid) fields, _disclosed = pdd._email_request(d, brokers.get("spokeo"), "ccpa_indirect", ["https://www.spokeo.com/x"], None) - Network accessline 804
fields, _disclosed = pdd._email_request(d, brokers.get("spokeo"), "ccpa_indirect", ["https://www.spokeo.com/x"], None) assert "None" not in " ".join(fields["my_identifiers"]) - Network accessline 805
["https://www.spokeo.com/x"], None) - Network accessline 935
assert brokers.get("radaris").get("defunct") and not brokers.get("usphonebook").get("defunct") for bid in ("radaris", "usphonebook"): _run(["record", sid, bid, "found", "--found", f"https://{bid}.example/x"]) - Network accessline 936
for bid in ("radaris", "usphonebook"): _run(["record", sid, bid, "found", "--found", f"https://{bid}.example/x"]) assert _run(["record", sid, bid, "submitted"])["next_recheck_at"] - Network accessline 937
_run(["record", sid, bid, "found", "--found", f"https://{bid}.example/x"]) - Network accessline 977
def test_synthesize_steps_keep_gate_quirks_within_cap(): r = {"optout_url": "https://x.example/optout", - Network accessline 978
r = {"optout_url": "https://x.example/optout",
tools/mcp_oauth.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 8
[Credential-related source omitted] - Network accessline 254
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - Access to secretsline 379
[Credential-related source omitted] - Access to secretsline 383
[Credential-related source omitted] - Access to secretsline 543
[Credential-related source omitted] - Network accessline 760
_SSH_HINT_LOOPBACK = ( " Remote session detected. After you authorize, the provider redirects to\n" " http://{host}:{port}/callback\n" - Network accessline 761
" Remote session detected. After you authorize, the provider redirects to\n" " http://{host}:{port}/callback\n" " which only the listener on THIS machine can receive. Two options:\n" - Network accessline 762
" http://{host}:{port}/callback\n" - Network accessline 772
" then open the URL above and let it redirect normally.\n" "\n" " See: https://hermes-agent.nousresearch.com/docs/guides/oauth-over-ssh\n") - Network accessline 774
" See: https://hermes-agent.nousresearch.com/docs/guides/oauth-over-ssh\n") - Network accessline 939
_CIMD_CLIENT_METADATA_URL = "https://nousresearch.github.io/hermes-agent/docs/oauth/client-metadata.json" - Access to secretsline 1005
[Credential-related source omitted] - Network accessline 1098
return cfg.get("redirect_uri") or f"http://{cfg.get('redirect_host') or '127.0.0.1'}:{port}/callback" - Access to secretsline 1128
[Credential-related source omitted] - Access to secretsline 1141
[Credential-related source omitted] - Access to secretsline 1161
[Credential-related source omitted] - Access to secretsline 1172
[Credential-related source omitted] - Access to secretsline 1196
[Credential-related source omitted] - Access to secretsline 1203
[Credential-related source omitted] - Access to secretsline 1226
[Credential-related source omitted] - Access to secretsline 1242
[Credential-related source omitted]
tools/mcp_tool.py
- Executable scriptline 1
#!/usr/bin/env python3 - Instruction override signalline 518
# Trust-tier gating (``trust: full | untrusted``): on an untrusted server every write-capable # call (discovery-time ``readOnlyHint`` not exactly True; malformed fails closed) needs approval # before the RPC fires. A lying readOnlyHint can - Instruction override signalline 519
# call (discovery-time ``readOnlyHint`` not exactly True; malformed fails closed) needs approval # before the RPC fires. A lying readOnlyHint can only skip approval for calls the operator was # already warned about, never widen access. Miss - Instruction override signalline 520
# before the RPC fires. A lying readOnlyHint can only skip approval for calls the operator was
tools/skill_manager_tool.py
- Executable scriptline 1
#!/usr/bin/env python3
website/scripts/generate-skill-docs.py
- Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
Executable file; content unavailable - Executable scriptline 1
Executable file; content unavailable - Executable scriptline 1
Executable file; content unavailable - Executable scriptline 1
Executable file; content unavailable - Executable scriptline 1
Executable file; content unavailable - Network accessline 237
source_dir = "skills" if meta["source_kind"] == "bundled" else "optional-skills" base = f"https://github.com/NousResearch/hermes-agent/blob/main/{source_dir}/{meta['rel_path']}" - Network accessline 238
base = f"https://github.com/NousResearch/hermes-agent/blob/main/{source_dir}/{meta['rel_path']}"
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 27 facts
- Contacts ethereum-rpc.publicnode.com, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:23High confidence
- Contacts etherscan.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:26High confidence
- Contacts bsc-dataseed1.binance.org, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:31High confidence
- Contacts bscscan.com, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:34High confidence
- Contacts mainnet.base.org, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:39High confidence
- Contacts basescan.org, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:42High confidence
- Contacts arb1.arbitrum.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:47High confidence
- Contacts arbiscan.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:50High confidence
- Contacts polygon-rpc.com, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:55High confidence
- Contacts polygonscan.com, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:58High confidence
- Contacts mainnet.optimism.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:63High confidence
- Contacts optimistic.etherscan.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:66High confidence
- Contacts api.avax.network, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:71High confidence
- Contacts snowtrace.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:74High confidence
- Contacts mainnet.era.zksync.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:79High confidence
- Contacts explorer.zksync.io, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:82High confidence
- Contacts api.coingecko.com, which SKILL.md, the README and the plugin manifest do not mentionevm/scripts/evm_client.py:545High confidence
- Contacts api.coingecko.com, which SKILL.md, the README and the plugin manifest do not mentionsolana/scripts/solana_client.py:185High confidence
- Contacts www.bing.com, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/moodboard.mjs:70High confidence
- Contacts www.pinterest.com, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/moodboard.mjs:90High confidence
- Contacts www.awwwards.com, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/refscout.mjs:286High confidence
- Contacts api.polyhaven.com, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:96High confidence
- Contacts polyhaven.com, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:116High confidence
- Contacts api.iconify.design, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:146High confidence
- Contacts icon-sets.iconify.design, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:162High confidence
- Contacts api.openverse.org, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:265High confidence
- Contacts coverr.co, which SKILL.md, the README and the plugin manifest do not mentionauteur/scripts/source.mjs:281High confidence
Signal confidence: 473 facts
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in code of a file that runsactivate:1High confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in a comment of a file that runsactivate.fish:1Low confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Executable script in code of a file that runshermes:1High confidence
- Instruction override signal in code of the SKILL.mdantigravity-cli/SKILL.md:88Low confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:149High confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:150High confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:151High confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:188High confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:189High confidence
- Instruction override signal in prose of the SKILL.mdantigravity-cli/SKILL.md:190High confidence
- Network access in prose of documentationantigravity-cli/references/cli-docs.md:8Low confidence
- Dangerous shell operation in prose of documentationantigravity-cli/references/cli-docs.md:9Low confidence
- Network access in prose of documentationantigravity-cli/references/cli-docs.md:9Low confidence
- Network access in prose of documentationantigravity-cli/references/cli-docs.md:10Low confidence
- Network access in prose of documentationantigravity-cli/references/cli-docs.md:11Low confidence
- Instruction override signal in prose of documentationantigravity-cli/references/cli-docs.md:48Low confidence
- Instruction override signal in prose of documentationantigravity-cli/references/cli-docs.md:49Low confidence
- Instruction override signal in prose of documentationantigravity-cli/references/cli-docs.md:50Low confidence
- Network access in prose of the SKILL.mdblackbox/SKILL.md:16Low confidence
- Network access in prose of the SKILL.mdblackbox/SKILL.md:22Low confidence
- Network access in prose of the SKILL.mdblackbox/SKILL.md:23Low confidence
- Network access in prose of the SKILL.mdblackbox/SKILL.md:24Low confidence
- Network access in code of the SKILL.mdblackbox/SKILL.md:85High confidence
- Network access in prose of the SKILL.mdgrok/SKILL.md:16Low confidence
- Network access in prose of the SKILL.mdgrok/SKILL.md:36Low confidence
- Dangerous shell operation in prose of the SKILL.mdgrok/SKILL.md:37Low confidence
- Network access in prose of the SKILL.mdgrok/SKILL.md:37Low confidence
- Access to secrets in prose of the SKILL.mdgrok/SKILL.md:54Low confidence
- Network access in code of the SKILL.mdgrok/SKILL.md:205High confidence
and 423 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md optional-skills/autonomous-ai-agents/agent-merge-conflict-arbiter/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
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