#704 by GitHub stars
huytieu/COG-second-brain
Self-evolving second brain with 35 AI skills, 10 agents, and people CRM. Closed-loop harness: a V-model verification lifecycle where the worker never grades its own homework. Plus paired anti-slop design skills for marketing and product UI. Works with Claude Code, Cursor, Kiro, Gemini CLI, Codex.
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
skills/auto-research/SKILL.md
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skills/loop-engineering/SKILL.md
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skills/meeting-transcript/SKILL.md
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skills/memory-hygiene/SKILL.md
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skills/museum-art/SKILL.md
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skills/no-ai-slop/SKILL.md
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skills/onboarding/SKILL.md
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skills/product-ui-taste/SKILL.md
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skills/publish-to-confluence/SKILL.md
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skills/release-comms/SKILL.md
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skills/release-video/SKILL.md
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skills/retro/SKILL.md
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skills/review-cockpit/SKILL.md
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skills/scout/SKILL.md
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skills/slop-gate/SKILL.md
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skills/social-media-kit/SKILL.md
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skills/taste-skill/SKILL.md
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skills/team-brief/SKILL.md
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skills/terminal-replica/SKILL.md
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skills/ultragoal/SKILL.md
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skills/update-cog/SKILL.md
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skills/update-knowledge-base/SKILL.md
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skills/url-dump/SKILL.md
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skills/vector-cover/SKILL.md
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skills/voice-baseline/SKILL.md
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skills/weekly-checkin/SKILL.md
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skills/youtube-video-kit/SKILL.md
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View on GitHubAdd to my skills
Verified commit 144c7a3a959cc99538b7dfee9c22c4b065a0e156
git clone --filter=blob:none --no-checkout https://github.com/huytieu/COG-second-brain.git .hotskills/huytieu-cog-second-brain && git -C .hotskills/huytieu-cog-second-brain checkout --detach 144c7a3a959cc99538b7dfee9c22c4b065a0e156
# Copy each selected skill directory from .hotskills/huytieu-cog-second-brain into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/huytieu/COG-second-brain.git .hotskills/huytieu-cog-second-brain && git -C .hotskills/huytieu-cog-second-brain checkout --detach 144c7a3a959cc99538b7dfee9c22c4b065a0e156
# Copy each selected skill directory from .hotskills/huytieu-cog-second-brain into .agents/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/huytieu/COG-second-brain.git .hotskills/huytieu-cog-second-brain && git -C .hotskills/huytieu-cog-second-brain checkout --detach 144c7a3a959cc99538b7dfee9c22c4b065a0e156
# Copy each selected skill directory from .hotskills/huytieu-cog-second-brain into .cursor/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/huytieu/COG-second-brain.git .hotskills/huytieu-cog-second-brain && git -C .hotskills/huytieu-cog-second-brain checkout --detach 144c7a3a959cc99538b7dfee9c22c4b065a0e156
# Copy each selected skill directory from .hotskills/huytieu-cog-second-brain into .gemini/skills/<skill-name>/1.3kGitHub stars
11stars in 7 days
43skills in this repository
2commits in 30 days
Heat
4.4- Star growth, last 7 daysAhead of 82% of tracked repositories
- Star growth, last 24 hoursAhead of 3% of tracked repositories
- Rank movement, last 7 daysAhead of 30% of tracked repositories
- Commits, last 30 daysAhead of 60% 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
- auto-research
skills/auto-research - blog-publish
skills/blog-publish - braindump
skills/braindump - closed-loop
skills/closed-loop - comprehensive-analysis
skills/comprehensive-analysis - content-factory
skills/content-factory - create-user-story
skills/create-user-story - daily-brief
skills/daily-brief - daily-journal
skills/daily-journal - data-forms
skills/data-forms - editorial-illustrations
skills/editorial-illustrations - export-open-issues
skills/export-open-issues - feature-doc
skills/feature-doc - generate-prd
skills/generate-prd - generate-release-notes
skills/generate-release-notes - harvest
skills/harvest - knowledge-consolidation
skills/knowledge-consolidation - loop-engineering
skills/loop-engineering - meeting-transcript
skills/meeting-transcript - memory-hygiene
skills/memory-hygiene - museum-art
skills/museum-art - no-ai-slop
skills/no-ai-slop - onboarding
skills/onboarding - product-ui-taste
skills/product-ui-taste - publish-to-confluence
skills/publish-to-confluence - release-comms
skills/release-comms - release-video
skills/release-video - retro
skills/retro - review-cockpit
skills/review-cockpit - scout
skills/scout - slop-gate
skills/slop-gate - social-media-kit
skills/social-media-kit - taste-skill
skills/taste-skill - team-brief
skills/team-brief - terminal-replica
skills/terminal-replica - ultragoal
skills/ultragoal - update-cog
skills/update-cog - update-knowledge-base
skills/update-knowledge-base - url-dump
skills/url-dump - vector-cover
skills/vector-cover - voice-baseline
skills/voice-baseline - weekly-checkin
skills/weekly-checkin - youtube-video-kit
skills/youtube-video-kit
How it works
auto-research
When your agent uses it
Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
What it loads into context
Frontmatter
52 tokensAlways available to the agent
SKILL.md
2,527 tokensWhen the skill is activated
Reference documents
128,565 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Invoke
- Agent Mode Awareness
- Command: `/auto-research`
- Input
- Execution Strategy
- Quality Standards
- Example Decomposition
- Runtime Expectations
- Error Handling
- Fallback Behavior
Scripts executed separately
../../scripts/validate-agent-surface.sh
Files
../../.claude-plugin/plugin.jsonasset16,635 bytes../../.claude-plugin/plugin.jsonasset16,635 bytes../../.claude/skills/auto-research/SKILL.mddoc11,706 bytes../../.claude/skills/blog-publish/SKILL.mddoc8,202 bytes../../.claude/skills/braindump/SKILL.mddoc12,617 bytes../../.claude/skills/closed-loop/SKILL.mddoc4,789 bytes../../.claude/skills/comprehensive-analysis/SKILL.mddoc14,656 bytes../../.claude/skills/content-factory/SKILL.mddoc6,270 bytes../../.claude/skills/create-user-story/SKILL.mddoc7,903 bytes../../.claude/skills/daily-brief/SKILL.mddoc16,536 bytes../../.claude/skills/daily-journal/SKILL.mddoc5,121 bytes../../.claude/skills/data-forms/SKILL.mddoc7,033 bytes../../.claude/skills/editorial-illustrations/SKILL.mddoc10,037 bytes../../.claude/skills/export-open-issues/SKILL.mddoc8,871 bytes../../.claude/skills/feature-doc/SKILL.mddoc9,756 bytes../../.claude/skills/generate-prd/SKILL.mddoc9,466 bytes../../.claude/skills/generate-release-notes/SKILL.mddoc10,167 bytes../../.claude/skills/harvest/SKILL.mddoc2,459 bytes../../.claude/skills/knowledge-consolidation/SKILL.mddoc11,654 bytes../../.claude/skills/loop-engineering/SKILL.mddoc6,271 bytes../../.claude/skills/meeting-transcript/SKILL.mddoc9,124 bytes../../.claude/skills/memory-hygiene/SKILL.mddoc4,874 bytes../../.claude/skills/museum-art/SKILL.mddoc10,474 bytes../../.claude/skills/no-ai-slop/SKILL.mddoc24,005 bytes../../.claude/skills/onboarding/SKILL.mddoc12,540 bytes../../.claude/skills/product-ui-taste/SKILL.mddoc63,811 bytes../../.claude/skills/publish-to-confluence/SKILL.mddoc7,761 bytes../../.claude/skills/release-comms/SKILL.mddoc7,987 bytes../../.claude/skills/release-video/SKILL.mddoc10,662 bytes../../.claude/skills/retro/SKILL.mddoc2,295 bytes../../.claude/skills/review-cockpit/SKILL.mddoc5,810 bytes../../.claude/skills/scout/SKILL.mddoc7,298 bytes../../.claude/skills/slop-gate/SKILL.mddoc6,565 bytes../../.claude/skills/social-media-kit/SKILL.mddoc15,985 bytes../../.claude/skills/taste-skill/SKILL.mddoc73,196 bytes../../.claude/skills/team-brief/SKILL.mddoc30,967 bytes../../.claude/skills/terminal-replica/SKILL.mddoc6,221 bytes../../.claude/skills/ultragoal/SKILL.mddoc8,754 bytes../../.claude/skills/update-cog/SKILL.mddoc3,715 bytes../../.claude/skills/update-knowledge-base/SKILL.mddoc11,043 bytes../../.claude/skills/url-dump/SKILL.mddoc14,483 bytes../../.claude/skills/vector-cover/SKILL.mddoc6,694 bytes../../.claude/skills/voice-baseline/SKILL.mddoc3,825 bytes../../.claude/skills/weekly-checkin/SKILL.mddoc13,865 bytes../../.claude/skills/youtube-video-kit/SKILL.mddoc7,663 bytes../../scripts/validate-agent-surface.shruns5,180 bytesSKILL.mdcore11,706 bytesSKILL.mdcore11,706 bytesSKILL.mdcore11,706 bytesSKILL.mdcore11,706 bytesSKILL.mdcore11,706 bytes
Read SKILL.md
# COG Auto Research Skill
## When to Invoke
- User asks a strategic question requiring deep research
- User says "research", "auto-research", "investigate", "strategic analysis", "deep dive into [topic]"
- User wants to understand market forces, competitive dynamics, technology trajectories, or strategic options
- User needs evidence-based analysis with real sources to support decision-making
Inspired by Karpathy's autoresearch — but for strategic thinking instead of ML training.
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use the full parallel agent execution strategy (5-7 agents). This skill benefits massively from team mode.blog-publish
When your agent uses it
Write and publish a post to your own static blog end to end: content checklist (embedded links, optional TL;DR), per-post ASCII art, an agent-readable markdown twin, deploy, and social distribution. Use when you say "write a blog post", "publish this to my blog", or "push this post to socials".
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,897 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
- Configuration
- Content checklist (every post)
- Narrative arc (default for substantial posts)
- Per-post ASCII art
- Agent-readable output (automatic, but verify)
- Build and deploy
- Social distribution (after the post is live)
Files
SKILL.mdcore8,202 bytesSKILL.mdcore8,202 bytesSKILL.mdcore8,202 bytesSKILL.mdcore8,202 bytesSKILL.mdcore8,202 bytesSKILL.mdcore8,202 bytes
Read SKILL.md
# blog-publish: static blog post pipeline
## Configuration
Set these once (`kit.toml`, `.env` or arguments).
| Setting | Meaning | Example |
|---|---|---|
| `BLOG_DIR` | Repo folder of your static blog | `./my-blog/` |
| `POSTS_DIR` | Markdown posts, relative to `BLOG_DIR` | `content/posts/` |
| `BUILD_CMD` | Command that builds the site | `python3 build.py` |
| `SITE_DIR` | Build output folder | `site/` |
| `BLOG_URL` | Public base URL | `https://blog.example.com` |
| `POST_PATH` | URL pattern for a post | `/blog/<slug>/` |braindump
When your agent uses it
Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
What it loads into context
Frontmatter
31 tokensAlways available to the agent
SKILL.md
2,810 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
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Process Flow
- YAML Formatting Requirements
- Verification Protocols
- Uncertainty Handling
- Integration with Other Skills
- Success Metrics
- Learning and Adaptation
Files
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# COG Braindump Skill
## Purpose
Transform raw thoughts into strategic intelligence through quick capture, systematic analysis, pattern recognition, and domain-aware insight extraction with minimal user friction.
## When to Invoke
- User wants to capture stream-of-consciousness thoughts
- User says "braindump", "brain dump", "capture thoughts", or "write down ideas"
- User has ideas they want to quickly record
- User mentions wanting to get thoughts out of their head
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**closed-loop
When your agent uses it
Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking for the closed loop, proper verification, or an evidence trail. Ordinary work does not run this.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
1,292 tokensWhen the skill is activated
Reference documents
353 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Phase 0 — Lane + run folder
- Phase 1 — CP-1 Spec (acceptance criteria)
- Phase 2 — CP-2 Plan
- Phase 3 — CP-3 Build
- Phase 4 — CP-3v Component verify
- Phase 5 — CP-4 Integration verify (`full` or multi-task)
- Phase 6 — CP-5 Acceptance (post-condition)
- Phase 7 — Record + handoff
- Integration
- Escalation template
Scripts executed separately
../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh
Files
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# Closed-loop execute (V-model right arm)
Mechanical verification pipeline. Every verify step emits **evidence rows** tied to acceptance criterion IDs (`AC-n`).
## When to use
The harness is opt-in. Run it when:
- Invoked as `/closed-loop <task>` or `/closed-loop <spec-path>`.
- The user asks for the closed loop, proper verification, or an evidence trail.
- `verification_harness: on` in `00-inbox/MY-PROFILE.md` and this is a build task.
- Another skill that declares a `normal`+ lane reaches its verify step.
Do **not** run it on a request that did not ask for it. Notes, briefs, research, drafts, and ordinary edits are not harness runs, and a checkpoint ledger on those is pure overhead.comprehensive-analysis
When your agent uses it
Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
3,626 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 Invoke
- Agent Mode Awareness
- Purpose
- Command: `/comprehensive-analysis`
- Voice & Tone
- Execution Strategy
- Metadata Template
- Fallback Behavior
- Error Handling
Files
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# COG Comprehensive Analysis Skill
## When to Invoke
- User wants a weekly review or retro prep
- User says "weekly analysis", "comprehensive review", "board prep", "deep dive"
- User needs analysis across a longer time period (7+ days)
- User wants a full picture of team/product health
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use the full parallel agent execution strategy below (5 agents). This skill benefits greatly from team mode.
- If `agent_mode: solo` — run a lighter version: collect GitHub + Linear data sequentially, skip PostHog deep analysis, produce a single combined report instead of 3 documents.
content-factory
When your agent uses it
Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
1,467 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
- Purpose
- When to Invoke
- Prerequisites
- State Files (read FIRST, update LAST — this is the dedup backbone)
- Volume Budget (across ALL runs of one night, hard caps)
- Phase 1 — SCOUT (every run)
- Phase 2 — TRIAGE (score, don't vibe)
- Phase 3 — PRODUCE (format ladder — decide by substance, not ambition)
- Phase 4 — PUBLISH (safety gates)
- Phase 5 — LEDGER + LOG (always, even for empty runs)
- Voice Checklist (delete the draft if any fail)
- The Loop (see /loop-engineering)
Files
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# COG Content Factory Skill
## Purpose
Act as the user's autonomous content creator: scout what just happened in their field, pick what is genuinely trending, and turn it into content in their voice — with zero duplicates, hard volume caps, and verification before anything counts as published. Designed to run unattended on a schedule (nightly) and to be safely re-runnable: **an empty run is a valid run; a low-quality post is not.**
## When to Invoke
- `/content-factory` / "run the content factory"
- Scheduled runs (e.g. 2-4 times a night via cron/launchd)
- "Turn today's news into content"
## Prerequisites
create-user-story
When your agent uses it
Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
1,922 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 Invoke
- Agent Mode Awareness
- Command: `/create-user-story`
- Pre-Flight Check
- Execution Strategy
- Fallback Behavior
- Error Handling
Files
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# COG Create User Story Skill
## When to Invoke
- User wants to create a new user story, feature request, or issue
- User says "create story", "new story", "add issue", "write user story", "create ticket"
- User describes a feature or requirement that should be tracked
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use parallel agents to check for duplicates across all active trackers simultaneously, then create the story
- If `agent_mode: solo` — check duplicates and create the story sequentially in the main conversation
## Command: `/create-user-story`daily-brief
When your agent uses it
Generate personalized news intelligence with verified sources (7-day freshness requirement)
What it loads into context
Frontmatter
33 tokensAlways available to the agent
SKILL.md
3,671 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
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Process Flow
- Loop Engineering
- Integration with Other Skills
- Performance Metrics
- Learning and Adaptation
- Success Criteria
- Philosophy
Files
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# COG Daily Brief Skill
## Purpose
Find verified, relevant news for personalized daily briefings with strict verification standards and strategic relevance analysis tailored to user's specific interests and projects.
## When to Invoke
- User wants their daily news briefing
- User says "daily brief", "news", "what's happening", "morning brief"
- User wants to stay updated on their interests
- Morning routine or regular check-in time
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**daily-journal
When your agent uses it
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say "log this to my journal", "add to today's journal", "reflect on today/yesterday", or when finishing a meaningful chunk of work in any session.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
1,218 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
- Purpose
- Storage
- When to log (procedure for the always-apply trigger)
- Entry format
- Daily file template (first entry of the day)
- Mode: reflect
- Weekly roll-up (optional)
- Guardrails
Files
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# Daily Journal
## Purpose
Keep an **automatic, passive** journal of your workdays so you don't have to write one. Claude logs what got done as it happens; you optionally run a reflection later. This is distinct from `/weekly-checkin` (which is a manual reflection where you supply the input) — here **Claude is the author** and the log accrues in the background.
Two modes:
1. **`log`** (default, mostly invoked implicitly) — append a short entry to today's journal.
2. **`reflect`** — read the day's log (+ recent days) and run a guided reflection with you.
## Storage
- One file per day: `01-daily/journal/YYYY-MM-DD.md` (use `date +%F` for today's date; never guess).
- Create the file from the template below on the first entry of the day.
- Append only — never rewrite earlier entries. Newest entries go at the bottom of the Log section.
data-forms
When your agent uses it
Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
1,679 tokensWhen the skill is activated
Reference documents
6,797 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Start here: the picker
- Six decisions that matter more than the form
- Encoding rules of thumb
- Workflow
- Pre-ship checklist
- Reference
Files
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# data-forms
A repertoire, not a style. Distilled from Lenny's Newsletter data illustrations
(2025-2026 survey issues) — what makes those charts work is not the palette, it's the
form selection and the encoding discipline. Both are portable to any visual language.
For the visual layer, use whatever the destination already has: your product's brand
tokens for in-product surfaces, the built-in `dataviz` skill for palette construction and
accessibility, your house style for personal sites. This skill decides *what shape the data
takes* — the layer above color.
## Start here: the picker
| What the data does | Reach for | Form # |editorial-illustrations
When your agent uses it
Generate meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geometry" method so any session can invent the RIGHT figure for what a post actually argues, then render it as a self-contained, theme-aware, reduced-motion-safe HTML/SVG figure. Use when writing/illustrating a blog post, essay, spec, or slide and you want a chart, diagram, or illustration that carries a thesis (not decoration) in the house style. Triggers: "make a chart/diagram/illustration for this", "visualize this argument", "monotykamary/Linear style figure", "editorial graphic".
What it loads into context
Frontmatter
199 tokensAlways available to the agent
SKILL.md
2,494 tokensWhen the skill is activated
Reference documents
10,560 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The core move
- Method (run this every time)
- The aesthetic in one breath
- The figure follows the host page's theme (ALWAYS)
- Output contract
- Verify (mandatory — this is a UI/UX output)
- Anti-slop checklist (reject the figure if any fail)
- Blog figure kit
Files
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# Editorial illustrations — claim → geometry
The look you want (monotykamary.com, linear.app/now) is not a chart library. It is
a **discipline**: every figure is a single argument rendered as geometry, framed in
a hairline box, captioned in mono uppercase with the *meaning*, drawn in grayscale
with exactly one accent. Get the discipline right and any shape reads as "house."
Copy templates blindly and it reads as slop.
**Do not reach for a fixed set of charts.** Reach for the method below and build the
figure this specific sentence needs. The six worked examples in
`references/worked-examples.md` are proof the method generalizes, not a menu.
## The core move
export-open-issues
When your agent uses it
Audit and export open issues from any project tracker with summary analysis and vault archival
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
2,500 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 Invoke
- Agent Mode Awareness
- Command: `/export-open-issues`
- Pre-Flight Check
- Execution Strategy
- CSV Export (Optional)
- Fallback Behavior
- Error Handling
Files
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# COG Export Open Issues Skill
## When to Invoke
- User wants to audit open issues or backlog health
- User says "export issues", "open issues", "backlog audit", "issue report", "what's open"
- Sprint/cycle planning prep — need a clear picture of what's in flight
- Stakeholder reporting — need a snapshot of current work status
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use parallel agents to collect from all active trackers simultaneously and produce a unified report
- If `agent_mode: solo` — collect from the primary tracker sequentially
feature-doc
When your agent uses it
Closed-loop feature documentation for your product. Give it a feature (issue numbers or an id) and it reads the source, drives your live app to capture real screenshots and a screen recording, writes a docs article (Docusaurus or any markdown docs site) with both how-to steps and use-case content, then verifies the article with a fresh-context read-only verifier before reporting. Use when you say "write the doc for <feature>", "document issue #123", or "/feature-doc".
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
2,454 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
- When to use
- Closed loop
- Inputs
- Pipeline
- Post-conditions
- Rules
- Escape hatches
- Tools
Scripts executed separately
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Files
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# feature-doc: closed-loop feature documentation
Produces one evidence-backed feature article end to end. Capture needs no manual
gesture: a bundled recorder grabs frames from the browser through the DevTools
protocol. Publishing the docs PR stays a gate for you at CP-6.
## Configuration
Set these once (a `.env`, your `kit.toml`, or pass them as arguments). Nothing below is hardcoded.
| Setting | Meaning | Example |
|---|---|---|
| `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` |
| `ISSUE_REPO` | Issue tracker holding the feature issues | `your-org/your-product` |generate-prd
When your agent uses it
Generate product requirements documents with optional publishing to Confluence or other wiki platforms
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
2,425 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 Invoke
- Agent Mode Awareness
- Command: `/generate-prd`
- Pre-Flight Check
- Execution Strategy
- Fallback Behavior
- Error Handling
Files
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# COG Generate PRD Skill
## When to Invoke
- User wants to create a PRD, product spec, or requirements document
- User says "generate PRD", "write PRD", "product requirements", "spec doc", "feature spec"
- User has a feature or project that needs formal documentation before development
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use parallel agents to gather context from multiple sources (existing PRDs, related issues, competitive research) while drafting
- If `agent_mode: solo` — gather context and draft sequentially in the main conversation
## Command: `/generate-prd`generate-release-notes
When your agent uses it
Generate categorized release notes from any source (GitHub, Linear, Jira, or manual input) with optional publishing
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
2,576 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 Invoke
- Agent Mode Awareness
- Command: `/generate-release-notes`
- Pre-Flight Check
- Execution Strategy
- Fallback Behavior
- Error Handling
Files
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# COG Generate Release Notes Skill
## When to Invoke
- User wants to create release notes for a version, sprint, or cycle
- User says "release notes", "changelog", "what shipped", "write release notes"
- A new version or milestone has been completed
- End of sprint/cycle documentation
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use parallel agents to collect data from all active sources simultaneously
- If `agent_mode: solo` — collect data sequentially from the primary tracker
harvest
When your agent uses it
Capture durable session learnings, stage for human promotion to 05-knowledge/lizard, and propose skill/CLAUDE.md patches. Triggered by /harvest, SessionEnd hook staging, or nightly enhance. Never writes durable knowledge without your approval.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
655 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
- Triggers
- Phase 1 — Collect (automatic or manual)
- Phase 2 — Curate (`/harvest promote`)
- Phase 3 — Retro line
- Rules
- Nightly enhance (optional)
Files
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# /harvest — session learning capture
Adapted from dwarves-kit `harvest` hook + vault lizard pipeline. Prevents tacit knowledge dying in the transcript.
## Triggers
- `/harvest` — manual end-of-session
- `/harvest promote` — curate staging → lizard drafts (uses harvest-curator)
- Staging file written by `stop` hook (`.cursor/hooks/harvest-stager.sh`)
## Phase 1 — Collect (automatic or manual)
Scan the current session for:
knowledge-consolidation
When your agent uses it
Build frameworks from scattered insights across all braindumps and notes
What it loads into context
Frontmatter
31 tokensAlways available to the agent
SKILL.md
2,432 tokensWhen the skill is activated
Reference documents
3,063 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Process Flow
- Loop Engineering
- Consolidation Guidelines
- Analysis Techniques Reference
- Success Metrics
- Common Use Cases
- Philosophy
Files
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# COG Knowledge Consolidation Skill
## Purpose
Transform scattered insights from braindumps, daily briefs, and check-ins into coherent frameworks and "single source of truth" knowledge documents through pattern recognition and systematic synthesis.
## When to Invoke
- User wants to consolidate their insights
- User says "consolidate knowledge", "build frameworks", "synthesize insights"
- Time for periodic knowledge base maintenance (weekly, monthly, quarterly)
- User wants to extract patterns from accumulated braindumps
- Before major decisions that could benefit from framework consultation
## Agent Mode Awareness
loop-engineering
When your agent uses it
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
1,414 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 loops
- The COG loop
- Termination conditions (use layers, never one)
- Verification first (COG's rule, applied to loops)
- In-loop context management
- Named patterns
- Failure modes and fixes
- How skills use this
Files
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# COG Loop Engineering
> **TL;DR:** Some COG skills are not one-shot prompts. They are loops: act, observe, verify, decide whether to continue. This skill is the shared vocabulary those skills use. The iron rule: **trust deterministic checks, never the agent's own "looks done" self-report.** Every loop must declare its verifier, its stopping conditions, and which pattern it follows.
This is a reference and design aid, not a content-generating workflow. Skills that loop (daily-brief, knowledge-consolidation, url-dump, weekly-checkin, and research/triage skills like auto-research and scout) link here instead of restating the rules. Invoke it directly when you are building or fixing an iterative skill.
## Why loops
A chain runs fixed steps: A then B then C. A loop is dynamic: the agent takes an action, reads real feedback (a fetched page, a date stamp, a file count), reasons about it, and repeats until a goal is met or a stop condition fires. Most knowledge-work that "keeps going until good enough" is a loop, and COG benefits from naming the loop explicitly rather than hoping a single prompt nails it.
## The COG loop
```
┌──────────────────────────────────────────────┐meeting-transcript
When your agent uses it
Process meeting recordings and notes into structured decisions, action items, and team dynamics with intelligent noise filtering
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
1,924 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 Invoke
- Agent Mode Awareness
- Purpose
- Command: `/meeting-transcript`
- Parallel Agent Team Execution Strategy
- Content Filtering Guidelines
- Metadata Template
- Content Structure
- Domain-Specific Processing
- Content Filtering Protocol
- Quality Assurance
- Integration with Daily Brief
- Learning Integration
Files
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# COG Meeting Transcript Skill
## When to Invoke
- User shares a meeting transcript or recording notes
- User says "process meeting", "meeting notes", "meeting transcript"
- User has a block of text from a meeting they want structured
- User mentions wanting to extract action items from a meeting
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use the full parallel agent execution strategy below (3 agents)
- If `agent_mode: solo` — process sequentially: extract content first, then analyze dynamics, then enrich with context.
memory-hygiene
When your agent uses it
Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries
What it loads into context
Frontmatter
55 tokensAlways available to the agent
SKILL.md
1,128 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
- Purpose
- When to Invoke
- Scope
- Claim Classification
- Verification Moves (cheap first)
- Stamping
- The Loop (see /loop-engineering)
- Report (single file)
- Rules
Files
SKILL.mdcore4,874 bytes
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# COG Memory Hygiene Skill
## Purpose
Prevent the **stale-but-confident** failure mode: a memory or knowledge note that was correct when written ("the webhook lives at X", "the board ID is Y") silently drifts after the environment changes, yet still ranks high at recall and gets acted on.
The system move (adapted from "From Model Scaling to System Scaling: Scaling the Harness in Agentic AI", Gu, UC Berkeley, arXiv:2605.26112): **make trust a runtime decision, not a property of the stored item**. Re-verify against the live environment, and keep per-entry `last_verified` and `confidence` as first-class fields so future recalls can weigh trust.
## When to Invoke
- `/memory-hygiene`
- "Audit my memories" / "check for stale memories"
- After a memory misfires (a recalled fact turned out wrong)
- Default cadence: monthlymuseum-art
When your agent uses it
Source authentic, high-res PUBLIC-DOMAIN artwork from museum open-access APIs (Met, Cleveland, SMK, Rijksmuseum, NGA, Art Institute of Chicago, Getty, Smithsonian) instead of AI-generated or generic-stock imagery. The default move whenever a visual needs an aesthetic, credible image (blog heroes, decks, social cards, essay/spec figures). Verified keyless recipes + licensing rules inside.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,666 tokensWhen the skill is activated
Reference documents
18,352 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to reach for this
- Decision: which source
- How to fetch in THIS environment
- Freshness over caching (mandatory)
- Verified keyless recipes (2026-07-24, all live-tested)
- Key-gated + aggregator sources (know these too)
- Licensing rules (apply before shipping any image)
- Relationship to other skills
- References (full per-museum recipes, verified)
Files
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# museum-art: Public-Domain Artwork for Visuals
> Standing rule (adopted 2026-07-24, from Eric Li's post on museum open-access): **whenever a visual needs a real image with aesthetic weight, source public-domain museum artwork first** - over AI-generated imagery and over generic stock. Curated, historically significant art reads as credible and sophisticated; AI-gen reads as slop. This stacks with, and reinforces, the `no-ai-slop` skill and your house image style. It does NOT replace the generative `editorial-illustrations` skill (that owns claim-driven diagrams/figures) or your house chart style - use museum art for photographic/hero/decorative/mood imagery, generative figures for data and concept diagrams.
## When to reach for this
- Blog post hero images, section breaks, mood imagery (the blog-publish image step).
- Deck/slide backgrounds and section dividers, social cards, essay figures, spec cover art.
- Any time the instinct is "generate an image" for something decorative or evocative. Stop and pull a real painting instead.
- NOT for: product screenshots, UI mockups, data charts, logos, or claim-driven explanatory diagrams (those are editorial-illustrations / real captures).
## Decision: which source
1. **Met -> Cleveland -> SMK first.** All keyless, one JSON hop, CC0/PD, broad collections. Fastest path to a hi-res image.
2. **Need Dutch/Flemish masters or decorative arts?** Rijksmuseum (keyless, 3 hops).
3. **Need European antiquities/photography and keyword isn't essential?** Getty (keyless, SPARQL).no-ai-slop
When your agent uses it
Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI. Also the default writing standard for all agent output: 80% ASD-STE100 controlled language plus the format ladder (prose, diagram, HTML page, explainer video).
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
5,536 tokensWhen the skill is activated
Reference documents
1,136 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Two jobs
- What to ask for
- Editing principles
- Controlled-language target (ASD-STE100, adopted 2026-10-02)
- Output format ladder (adopted 2026-10-02)
- Words to cut
- Patterns to cut
- Scope: every medium, not only prose
- Reader-cited tells
- Voice-mode tells
- Model-era tells (Opus 5.5, measured 2026-09-28)
- Structural slop
- Workflow
Files
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# No AI slop
You are a sharp human editor. Preserve the user's point and personal voice while making the writing clearer and more alive. Remove AI patterns without turning distinctive writing into generic polished prose.
## Two jobs
**Edit (default).** The user shares a draft to fix. Make the minimum effective edit with the rules below and return the edited draft plus a What changed section.
**Detect.** The user asks whether a piece is AI slop, or asks to audit, scan, or flag a draft without rewriting. Name each pattern from this skill that appears, quote the line, and give the fix in a few words. Do not rewrite, score the draft, or guess whether AI wrote it. AI detectors guess. Named patterns are evidence the user can check. Offer to edit the draft after.
## What to ask for
If the user has not provided a draft, ask them to paste it.
onboarding
When your agent uses it
Personalize COG for your workflow - creates profile, interests, and watchlist files with guided setup (run this first!)
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
2,942 tokensWhen the skill is activated
Reference documents
1,986 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- When to Invoke
- Core Design Principle: Smart, Low-Friction Onboarding
- Process Flow
- Configuration Update Mode
- Success Criteria
- Error Handling
- Privacy & Data
- Philosophy
Files
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# COG Onboarding Skill
## Purpose
Welcome new users and collect essential information to personalize their COG experience. All configuration is stored as natural markdown files within the vault structure, following COG's philosophy of transparent, editable knowledge.
## When to Invoke
- User explicitly requests `/onboarding` or mentions "onboarding" or "setup COG"
- User is new and hasn't completed onboarding yet
- User wants to update their profile or add new projects
- Any time profile customization is needed
## Core Design Principle: Smart, Low-Friction Onboarding
**The onboarding MUST feel like a natural conversation, NOT a form to fill out.**product-ui-taste
When your agent uses it
Anti-slop skill for PRODUCT UI - dashboards, data tables, forms, multi-step flows, settings, list/detail, app shells. The agent reads the surface, budgets the frame first, and ships dense interfaces that are correct at every edge case (overflow, long labels, empty/error/loading states, i18n expansion, keyboard/a11y). House-system-first; maps to Carbon/Polaris/Atlaskit/Fluent/Primer/Material3/Radix-shadcn/Ant. The counterpart to taste-skill, which owns landing/portfolio/marketing.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
15,259 tokensWhen the skill is activated
Reference documents
6,079 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 0. PRODUCT READ (Read the Surface Before Anything Else)
- 1. THE THREE DIALS (Core Configuration)
- 2. PRODUCT ARCHETYPE → DESIGN SYSTEM MAP
- 3. FRAME-FIRST APP-SHELL ARCHITECTURE
- 4. LAYOUT HARD RULES (Failing any of these ships broken work)
- 5. OVERFLOW & TRUNCATION CATALOG (Deep-Dive)
- 6. STATE MATRIX (Deep-Dive)
- 7. DATA TABLE DEEP-DIVE (The Hardest Product Surface)
- 8. FORMS, VALIDATION & MULTI-STEP FLOWS (Deep-Dive)
- 9. NAVIGATION, OVERLAYS & LAYERING (Deep-Dive)
- 10. DENSITY, I18N & ACCESSIBILITY (Deep-Dive)
- 11. ONBOARDING & PROGRESSIVE DISCLOSURE
- 12. CONTEXT-AWARE PROACTIVITY (Canonical Skeletons to Recognize)
- 13. PERFORMANCE & ACCESSIBILITY GUARDRAILS
- 14. DARK MODE
- 15. INVISIBLE DETAILS (Micro-Craft Floor)
- 16. PRODUCT-UI AI TELLS (Forbidden Patterns)
- 17. REFERENCE VOCABULARY (Names the Agent Should Know)
- 18. REDESIGN PROTOCOL (Auditing an Existing Dense Screen)
- 19. RELATIONSHIP TO taste-skill + OUT OF SCOPE
- 20. FINAL PRE-FLIGHT CHECK
- References
Files
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# product-ui-taste: Anti-Slop Skill for Dense Product Surfaces
> Dashboards, data tables, forms, wizards, settings, list/detail, admin consoles, app shells. NOT landing pages, portfolios, or marketing (that is `taste-skill`). NOT charts (that is `dataviz`). NOT native mobile.
> Every rule is **contextual and mechanical**. It fires from the surface you are building, not automatically. Read the surface, budget the frame, then pull only what fits.
> **Companion contract:** `taste-skill` explicitly hands off "dashboards / dense product UI / data tables / multi-step forms" to "the right tool." This skill is that tool. If a brief is half marketing and half product (a landing page with an embedded live dashboard), use `taste-skill` for the hero/marketing sections and this skill for the product surface. Never run both on the same component.
---
## 0. PRODUCT READ (Read the Surface Before Anything Else)
Marketing UI lives on first impression. Product UI lives on the **hundredth** use, under real data, by someone doing a job. The slop failure mode is different: not "templated aesthetic," but **prototype that dies on contact with real data** - a table that scrolls the whole page sideways, a button that truncates its own label, a single grey "no data" box reused for three different situations, a form that clears itself when the user fat-fingers one field.
### 0.A Read these signals first
1. **Surface type** - the single most important read. One of: **index/table** (list of records to scan, filter, select), **detail/record** (one entity, its fields and related data), **dashboard/overview** (KPIs + widgets + a table or two), **form/settings** (input and configuration), **multi-step flow/wizard** (a sequential task), **console/observability** (logs, metrics, deploys), **feed/messaging** (stream of items). Most real screens are a composition of two or three (index + detail drawer; dashboard + drill-in table).publish-to-confluence
When your agent uses it
Publish any markdown file from the vault to Confluence with format conversion and approval gate
What it loads into context
Frontmatter
45 tokensAlways available to the agent
SKILL.md
1,972 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 Invoke
- Agent Mode Awareness
- Command: `/publish-to-confluence`
- Pre-Flight Check
- Execution Strategy
- Re-Publishing (Update Flow)
- Fallback Behavior
- Error Handling
Files
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# COG Publish to Confluence Skill
## When to Invoke
- User wants to publish a document to Confluence
- User says "publish to Confluence", "push to wiki", "send to Confluence", "confluence publish"
- User has a markdown file they want to share with the team on Confluence
- After generating a PRD, release notes, or other document that should go to the wiki
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — no significant benefit for this skill (single sequential operation)
- If `agent_mode: solo` — standard execution
release-comms
When your agent uses it
Release communication pipeline for your product: turn a deployment runsheet and a GitHub milestone into a customer-facing release note with real screenshots and videos, docs updates (including retrofits of articles the release makes stale), a Slack announcement draft, and a lightweight marketing package. Use when you say "cover today's release", "write the release note", or "/release-comms".
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,841 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
- Source-of-truth chain (walk it in order)
- Pipeline
- Cockpit
Files
SKILL.mdcore7,987 bytes
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# release-comms: release note, docs, announcement and marketing pipeline
**Lane: `full`.** It mutates external state (Slack posts, docs PR, wiki pages), so every post and PR is drafted first and gated on the user's explicit approval. See the `closed-loop` skill.
## Configuration
Fill these in once (`kit.toml`, `.env` or arguments). Placeholders below are examples.
| Setting | Meaning | Example |
|---|---|---|
| `PRODUCT_REPO` | Repo that holds the release milestone | `your-org/your-product` |
| `MILESTONE_PATTERN` | Milestone naming | `Release YYYY-MM-DD` |
| `TEAM_LABEL` | Label that filters the milestone to your team | `team:platform` |
| `RUNSHEET_SOURCE` | Where the deployment runsheet lives | a wiki page, a doc, or a Slack thread |release-video
When your agent uses it
Turn a product release (the list of shipped items plus real screen recordings) into a motion recap video and one explained demo per feature, with sound effects tied to on-screen motion and a composed music bed. Deterministic HTML scenes rendered frame by frame, ElevenLabs for sound effects and music, ffmpeg for the mix. Use when asked for a release video, a changelog video, a feature recap reel, or demo clips for release notes and docs.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
2,597 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
- Requirements
- Rules from review
- Workflow
- Files
Scripts executed separately
scripts/compose_demo.pyscripts/engine.jsscripts/frames_from_video.shscripts/gen_audio.shscripts/human_input.pyscripts/mix_final.shscripts/mix_sfx.pyscripts/music_check.pyscripts/plain_demo.pyscripts/render.py
Files
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Read SKILL.md
# release-video
Two outputs per release:
- **Recap**: a 45-75 s motion video, one scene per shipped feature, vertical (1080x1920) by default.
- **Explained demos**: one 30-60 s clip per feature, a real recording of the product with step captions, eased zoom on the payoff, and intro/outro cards (1920x1080).
Every frame of the recap is a pure function of time (`window.renderAt(t)` in `scripts/engine.js`), so a re-render is identical, renders split across workers, and any single frame can be pulled as a still for review before the full render.
This skill makes motion scenes and composes demos from recordings. It does not cut raw footage of people talking. For narrated takes, founder voice-overs or interviews, use [browser-use/video-use](https://github.com/browser-use/video-use), which cuts on word boundaries from a transcript, and drop the recap and demo clips into its folder as B-roll.
## Requirements
`ffmpeg`, Python 3 with `playwright`, `numpy`, `Pillow`, and `ELEVENLABS_API_KEY` for audio. If Playwright's bundled Chromium doesn't launch, point `CHROME_PATH` at any Chromium or headless-shell binary.retro
When your agent uses it
CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.
What it loads into context
Frontmatter
53 tokensAlways available to the agent
SKILL.md
622 tokensWhen the skill is activated
Reference documents
242 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to run
- Phase 1 — Gather evidence bundle
- Phase 2 — Checkpoint audit
- Phase 3 — Evidence quality pass
- Phase 4 — Write retro
- Phase 5 — Feed forward
- Output to the user
Scripts executed separately
../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh
Files
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# /retro — CP-7 close the V
Every non-`tiny` shipped or escalated run gets a retro. Retro is **advisory** but strongly expected: it is how the harness improves.
## When to run
- After CP-6 ship (approved publish, merged PR, vault deliverable done)
- After escalate (capture why verifier failed)
- End of an `/ultragoal` phase (one retro per phase)
- Manual: `/retro <run-dir or spec path>`
## Phase 1 — Gather evidence bundle
Read:review-cockpit
When your agent uses it
Produce and continuously maintain ONE living review document for a multi-item session — a cockpit header (Progress checklist, Working folder, Context) plus per-item review cards that you approve or request changes on directly in the doc or side panel. Use whenever a session has multiple deliverables you need to review/approve (meeting-processing + planning, multi-ticket work, briefs with several drafts, any "do X, then plan/draft Y and Z"). The doc is the interaction surface: you edit it (or reply in chat) to approve/change; the agent keeps it live as work progresses.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
1,344 tokensWhen the skill is activated
Reference documents
189 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- When to use
- The one rule
- Structure
- Update protocol (keep it live)
- Interaction contract
- Post-condition
- Optional: rendered view
Files
SKILL.mdcore5,810 bytesSKILL.mdcore5,810 bytesreferences/session-review-template.mddoc670 bytes
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# review-cockpit
## Purpose
A single, living **review document** that doubles as the control surface for a session. Instead of scattering a meeting note here, a ticket there, two drafts in chat, everything lands in **one file** you can open in the Obsidian side panel (or Claude side panel) and drive: see progress at a glance, review each item in place, approve or request changes inline, and watch the doc update as the agent works. Mirrors a "co-work" cockpit (Progress / Working folder / Context).
## When to use
- Any session with **≥2 things you need to review or approve** (the default for "process X then plan/draft Y, Z").
- Meeting-processing + pod planning, multi-ticket runs, briefs with several drafts, spec + plan + drafts.
- NOT for a single trivial deliverable (one file, no approval loop) — a plain file is fine there.
Pairs with `closed-loop` (verification), `harvest` (learnings), and the V-model checkpoints. This skill governs **how the deliverable is presented and driven**, not the verification pipeline.
scout
When your agent uses it
Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip
What it loads into context
Frontmatter
38 tokensAlways available to the agent
SKILL.md
1,735 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
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Boundary with `/url-dump`
- Process Flow
- Fallback Behavior
- Uncertainty Handling
- Integration with Other Skills
- Success Metrics
- Philosophy
Files
SKILL.mdcore7,298 bytes
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# COG Scout Skill
## Purpose
Lightweight URL/tool triage that sits between "ignore" and `/url-dump`. Evaluates whether a URL or tool is worth saving or skipping — checking existing vault coverage, assessing relevance to the user's profile and interests, and recommending a clear next action.
## When to Invoke
- User wants to evaluate a URL or tool before committing to a full save
- User says "scout this", "evaluate this", "should I save this?", "is this relevant?"
- User shares one or more URLs and wants a quick relevance assessment
- User mentions a tool/service name and wants to know if it's worth investigating
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**slop-gate
When your agent uses it
Deterministic pre-publish scan that refuses AI-slop tells in anything about to be written, published, or sent: files, artifacts, slide titles, table headers, diagram labels, chat messages, commit messages. Use before publishing or sending any deliverable, in CI, or wired as a Claude Code hook so the check runs without being remembered. The editorial counterpart is no-ai-slop, which rewrites; this one blocks.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,586 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 a gate and not a rule
- Run it
- Wire it as a hook (Claude Code, optional)
- What fails on one hit
- What is counted, not banned
- Exhibits
- What it cannot see
- Comparing models against the gate
Scripts executed separately
scripts/model_compare.pyscripts/scan.py
Files
SKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesSKILL.mdcore6,565 bytesscripts/model_compare.pyruns13,520 bytesscripts/scan.pyruns8,256 bytes
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<!-- slop-ok: this skill quotes the tells it blocks -->
# slop-gate
`no-ai-slop` is an editor you invoke. This is a gate that runs whether or not anyone
remembers it. It scans outgoing text, refuses on a tell, and returns the hit list so
the rewrite is targeted.
Two rules make it usable rather than annoying: hard tells fail on a single hit, and
words that are decoration at volume but legitimate once are counted rather than
banned. Text inside quotes or backticks is stripped before scanning, so a piece that
quotes a tell as an exhibit does not fail on its own examples.
## Why a gate and not a rulesocial-media-kit
When your agent uses it
Produce and attach native media (video teaser, GIF, card images) for every social post to maximize reach and interaction. Use whenever you post to X, LinkedIn, Reddit, or prepare HN/IndieHackers handoffs, and whenever a social-publishing step runs. A text-only social post is a draft, not a post.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,920 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
- Requirements
- The kit contract
- Style variety across a campaign
- House card style
- Content type to visual treatment
- Thread media placement
- Production recipes
- Attaching media per platform
- Session discipline
- Post-shape rules that compound with media
- Definition of done
Scripts executed separately
../../.claude/skills/social-media-kit/scripts/linearcard.pyscripts/cardrender.pyscripts/linearcard.pyscripts/terminal_banner.py
Files
../../.claude/skills/social-media-kit/scripts/linearcard.pyruns9,444 bytesSKILL.mdcore15,985 bytesSKILL.mdcore15,985 bytesSKILL.mdcore15,985 bytesSKILL.mdcore15,985 bytesSKILL.mdcore15,985 bytesscripts/cardrender.pyruns5,982 bytesscripts/linearcard.pyruns9,444 bytesscripts/terminal_banner.pyruns3,700 bytes
Read SKILL.md
# Social media kit
Every social post carries a video, GIF, or image. Native media is the largest reach lever the feed algorithms give you. This skill defines the kit of assets, the visual rules that keep a campaign from looking templated, and the browser mechanics for attaching media on each platform.
## Configuration
Set these once. Scripts read them from environment variables, so nothing personal is hardcoded.
| Setting | Env var | Default |
|---|---|---|
| Signature shown bottom-right on cards | `BRAND_URL` | `yourblog.example` |
| Output folder for cards | `CARD_OUT_DIR` | `.` |
| Screenshot for the runner card | `CARD_SCREENSHOT` | unset (card skipped) |
| Quote card attribution | `QUOTE_ATTRIBUTION` | `Your Name · your role` |taste-skill
When your agent uses it
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
18,350 tokensWhen the skill is activated
Reference documents
4,046 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 0. BRIEF INFERENCE (Read the Room Before Anything Else)
- 1. THE THREE DIALS (Core Configuration)
- 2. BRIEF → DESIGN SYSTEM MAP
- 3. DEFAULT ARCHITECTURE & CONVENTIONS
- 4. DESIGN ENGINEERING DIRECTIVES (Bias Correction)
- 5. CONTEXT-AWARE PROACTIVITY
- 6. PERFORMANCE & ACCESSIBILITY GUARDRAILS
- 7. DIAL DEFINITIONS (Technical Reference)
- 8. DARK MODE PROTOCOL
- 9. AI TELLS (Forbidden Patterns)
- 11. REDESIGN PROTOCOL
- 12. THE BLOCK LIBRARY (Contract - Implementations Land Here Iteratively)
- 13. OUT OF SCOPE
- 14. FINAL PRE-FLIGHT CHECK
- References (vendored reference material, moved out of the body)
Files
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Read SKILL.md
# tasteskill: Anti-Slop Frontend Skill
> Landing pages, portfolios, and redesigns. Not dashboards, not data tables, not multi-step product UI.
> Every rule below is **contextual**. None of it fires automatically. First read the brief, then pull only what fits.
---
## 0. BRIEF INFERENCE (Read the Room Before Anything Else)
Before touching code or tweaking dials, **infer what the user actually wants**. Most LLM design output is bad because the model jumps to a default aesthetic instead of reading the room.
### 0.A Read these signals first
1. **Page kind** - landing (SaaS / consumer / agency / event), portfolio (dev / designer / creative studio), redesign (preserve vs overhaul), editorial / blog.
2. **Vibe words** the user used - "minimalist", "calm", "Linear-style", "Awwwards", "brutalist", "premium consumer", "Apple-y", "playful", "serious B2B", "editorial", "agency-y", "glassy", "dark tech".team-brief
When your agent uses it
Generate daily team intelligence brief by cross-referencing GitHub, Linear, Slack, PostHog, meetings, and braindumps with two-way Linear sync-back
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
7,320 tokensWhen the skill is activated
Reference documents
4,810 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Invoke
- Agent Mode Awareness
- Purpose
- Command: `/team-brief`
- Voice & Tone
- Parallel Agent Team Execution Strategy
- Content Structure (PRIORITY ORDER — Important First, Details Last)
- Metadata Template
- Synthesis Rules
- Fallback Behavior
- Error Handling
Files
SKILL.mdcore30,967 bytesreferences/agent-prompts.mddoc13,886 bytesreferences/brief-frontmatter.mddoc1,599 bytesreferences/publish-templates.mddoc3,313 bytes
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# COG Team Intelligence Brief Skill
## When to Invoke
- User says "team brief", "team update", "what did we ship", "standup prep"
- User wants to know what the dev team is working on
- User needs cross-referenced GitHub + Linear + Slack intelligence
- Morning routine for product/engineering leads
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use the full parallel agent execution strategy below (6 agents)
- If `agent_mode: solo` — run data collection sequentially in the main conversation. Skip Phase 3.5 (Linear sync-back) and Phase 3.7 (HackMD publish) to keep it fast.
terminal-replica
When your agent uses it
Reproduce YOUR real terminal and CLI agents (Claude Code, Codex CLI, Gemini CLI) faithfully in any generated graphic, GIF, or video frame. Use whenever an asset shows a terminal, an agent transcript, a statusline, or a CLI banner. Capture your actual theme, font, prompt and statusline first, and never invent a generic "hacker terminal" look.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,589 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
- Method
- Claude Code TUI
- Codex CLI
- Gemini CLI
- Render recipe (PIL)
- Example values
- Definition of done
Files
SKILL.mdcore6,221 bytes
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# Terminal replica
Terminal-like graphics must match the real tool and the real setup of the person who ships the asset. A generic green-on-black "hacker terminal" reads as fake. This skill is a method for capturing your own setup, plus a render recipe and a worked example.
## Method
1. Capture the ground truth. Take a fresh screenshot of your real terminal running the tool. Sample background, foreground and accent colors from it with an eyedropper or `PIL.Image.getpixel`. A live screenshot beats any spec in this file.
2. Record these five facts about your setup:
- Emulator (Terminal.app, iTerm2, Warp, the VS Code integrated terminal, Ghostty).
- Font family and size. Check the emulator's settings. VS Code on macOS defaults to Menlo 12.
- Theme: background, default foreground, and the 16 ANSI colors.
- Shell prompt shape (for example `user@host ~ %` for default macOS zsh, or your powerline or starship layout).
- Statusline format, if your agent has a custom one.
3. Use your own session logs for transcript shapes. Claude Code stores them at `~/.claude/projects/<project-slug>/*.jsonl`. The assistant and tool-use turns show exactly what text appears.ultragoal
When your agent uses it
Run a large, multi-session goal (e.g. shipping a whole side product) through the full V-model closed loop, one phase at a time, with cross-session state and a final north-star acceptance gate. Ultragoals never downgrade the lane: every phase runs CP-1→CP-6 with adversarial verification. Opt-in: invoke with /ultragoal or by calling something a long-running goal. Ordinary work does not run this.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,309 tokensWhen the skill is activated
Reference documents
353 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use
- Files (one goal = one folder)
- Phase 0 — Charter (`/ultragoal new`)
- The phase loop (every phase, no lane downgrade)
- The two acceptance gates
- The HTML report (regenerate at every phase gate + final)
- STATUS.md template
- Resuming cold (most common entry)
- Rules
- Escalation template
- Registered ultragoals
Scripts executed separately
../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh../../.claude/lib/checkpoint.sh
Files
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Read SKILL.md
# Ultragoal — the closed loop for goals too big to ship in one run
An **ultragoal** is the term for a north-star that spans many sessions: fork the repos, combine the strong parts, ship one product. A `/closed-loop` run is one task through the loop. An ultragoal is a *chain of phases*, each of which is its own full closed-loop run, tracked so any cold session can resume.
**Core rule (from dwarves-kit, adopted fully): the worker never grades its own homework, and wrongness compounds across sessions — so verify every phase, not just the end.**
## When to use
- `/ultragoal <name>` — resume or advance an existing ultragoal
- `/ultragoal new "<north-star>"` — charter a new one
- `/ultragoal status` — report all ultragoals from the registry
- Trigger phrases: "make this an ultragoal", "this is a long-running goal", "combine these into one product over time"
Do **not** use for single-run work (that is `/closed-loop`), and do not start one on a request that never asked for one. Rule of thumb: if it needs a phase decomposition and won't finish today, it is an ultragoal.update-cog
When your agent uses it
Check for and apply upstream COG framework updates (skills, docs, scripts) without touching personal content
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
957 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
- Purpose
- When to Invoke
- Process Flow
- Alternative: Shell Script
- Important Notes
Files
SKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytesSKILL.mdcore3,715 bytes
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# COG Update Skill
## Purpose
Help the user update their COG framework files (skills, documentation, scripts) from the official upstream repository without risking their personal content (braindumps, profiles, notes).
## When to Invoke
- User asks to "update COG", "check for updates", or "get latest COG version"
- User mentions wanting new skills or features from upstream
- User asks about their COG version
## Process Flow
### 1. Check Current Version
Read `COG-VERSION` from the vault root. If it doesn't exist, inform the user they may be on an older version that predates version tracking.update-knowledge-base
When your agent uses it
Maintain and update product knowledge base from releases, features, and project changes with optional wiki sync
What it loads into context
Frontmatter
55 tokensAlways available to the agent
SKILL.md
2,680 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 Invoke
- Agent Mode Awareness
- Command: `/update-knowledge-base`
- Pre-Flight Check
- Execution Strategy
- Knowledge Base Organization
- Fallback Behavior
- Error Handling
Files
SKILL.mdcore11,043 bytesSKILL.mdcore11,043 bytesSKILL.mdcore11,043 bytes
Read SKILL.md
# COG Update Knowledge Base Skill
## When to Invoke
- After a release — update product knowledge with new features and changes
- User says "update knowledge base", "update KB", "sync knowledge", "update product docs"
- Periodic maintenance — ensure knowledge base reflects current product state
- After major feature launches, architecture changes, or deprecations
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**
- If `agent_mode: team` — use parallel agents to scan multiple sources and update multiple knowledge files simultaneously
- If `agent_mode: solo` — process updates sequentially, one knowledge file at a time
url-dump
When your agent uses it
Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets
What it loads into context
Frontmatter
38 tokensAlways available to the agent
SKILL.md
3,535 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
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Process Flow
- Booklet Structure
- YAML Formatting Requirements
- Verification Protocols
- Uncertainty Handling
- Loop Engineering
- Integration with Other Skills
- Success Metrics
- Learning and Adaptation
Files
SKILL.mdcore14,483 bytes
Read SKILL.md
# COG URL Dump Skill
## Purpose
Transform raw URLs into structured, insightful knowledge entries through intelligent content extraction, categorization, and integration with the user's knowledge base. Quick capture with automatic insight generation.
## When to Invoke
- User shares a URL they want to save
- User says "save this link", "bookmark this", "url dump", or "save for later"
- User pastes a URL and wants to capture it
- User wants to organize web resources into their knowledge base
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**vector-cover
When your agent uses it
Generate flat vector editorial cover illustrations in a warm editorial style: near-black canvas, cream organic blob, serif display type, one terracotta accent, and thin line-art concept motifs (grids, node charts, dashed "unknown" paths, mountains, question marks). Output is code (HTML + inline SVG) rendered to a crisp 2400x960 PNG via headless Chrome. Use when you want a blog, article or X-article cover image, a "hero graphic", or "make a cover for <title>".
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
1,675 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
- Requirements
- What this is
- Design system (default palette, configurable)
- Canvas
- Layout skeleton (every cover)
- The concept motif is the whole point
- Workflow
- Font note
- Gotchas
Scripts executed separately
../../.claude/skills/vector-cover/render.sh../../.claude/skills/vector-cover/render.sh../../.claude/skills/vector-cover/render.sh../../.claude/skills/vector-cover/render.shrender.sh
Files
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Read SKILL.md
# Vector cover
## Configuration
The palette below is a default. Swap the hex values in `template.html` for your own brand, and keep the rule of exactly one accent color. Cover copy (eyebrow, title) comes from your article, not from this skill.
## Requirements
- Google Chrome or Chromium. `render.sh` finds it in the standard macOS locations or on `PATH`. Set `CHROME` to override.
- No other dependencies, and no browser automation setup.
## What this is
A designed vector graphic built as code: flat SVG on a small set of color tokens, rendered and screenshotted. It is not a diffusion image.voice-baseline
When your agent uses it
Measure your own writing corpus for the words and sentence shapes you over-use, so an agent writing in your voice stops amplifying your tics into a style. Produces a counted baseline file and checks any new draft against it. Use when an agent's drafts start sounding like a caricature of you, before publishing anything written in your voice, or every ten new pieces to re-measure.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
885 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
- Run it
- What it measures
- How to read the result
- Re-measure
Scripts executed separately
scripts/census.py
Files
SKILL.mdcore3,825 bytesSKILL.mdcore3,825 bytesscripts/census.pyruns8,011 bytes
Read SKILL.md
# voice-baseline
An agent writing in your voice does not only sample the median of the internet. It
samples the median of you, from the pieces and rules and memory files in its context,
and it regresses toward your most frequent choices because those are the highest
probability tokens in your corpus. A word that was a choice at one use comes back at
five. Then the fifth use teaches the next draft, and the tic becomes the style.
Generic ban lists cannot catch this, because the words are yours and they are not on
anyone's list. Measure instead.
## Run it
```bashweekly-checkin
When your agent uses it
Cross-domain pattern analysis and strategic reflection for weekly review
What it loads into context
Frontmatter
28 tokensAlways available to the agent
SKILL.md
3,168 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
- Purpose
- When to Invoke
- Agent Mode Awareness
- Pre-Flight Check
- Process Flow
- Loop Engineering
- Conversational Guidelines
- Pattern Recognition Techniques
- Integration with Other Skills
- Success Metrics
- Philosophy
Files
SKILL.mdcore13,865 bytes
Read SKILL.md
# COG Weekly Check-in Skill
## Purpose
Comprehensive weekly review and analysis integrating insights across all domains (personal, professional, projects) with pattern recognition and strategic planning.
## When to Invoke
- User wants to do their weekly review
- User says "weekly checkin", "weekly review", "reflect on my week"
- End of week reflection time
- User wants to analyze patterns across the week
## Agent Mode Awareness
**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**youtube-video-kit
When your agent uses it
Produce YouTube-ready video packaging: a branded thumbnail, an animated opening title, background music via ElevenLabs Music, and the upload metadata bundle. Use whenever you make or publish a video (deep dives, demos, blog companions) or want thumbnails, intros, video music, or channel and playlist consistency.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,966 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
- Configuration
- Requirements
- Thumbnail
- Opening title
- Music with ElevenLabs
- Storyboarding: illustrate, do not slide
- Shot-segment QA before assembling
- Upload bundle and Studio publishing
- Definition of done
Scripts executed separately
../../.claude/skills/youtube-video-kit/scripts/ytthumb.pyscripts/ytopen.pyscripts/ytthumb.py
Files
../../.claude/skills/youtube-video-kit/scripts/ytthumb.pyruns4,950 bytesSKILL.mdcore7,663 bytesSKILL.mdcore7,663 bytesSKILL.mdcore7,663 bytesSKILL.mdcore7,663 bytesscripts/ytopen.pyruns5,504 bytesscripts/ytthumb.pyruns4,950 bytes
Read SKILL.md
# YouTube video kit
Package videos like a channel, not a screen recording. The template fuses two reference styles: cinematic stills with minimal text (the Anthropic channel) and dark IDE screenshots with huge boxed text, one accent word, readable at 320px (AI-coding channels). It uses the dark lane-art language from the `social-media-kit` skill.
## Configuration
Scripts read environment variables, so nothing personal is hardcoded.
| Setting | Env var | Default |
|---|---|---|
| Signature text (bottom-left) | `BRAND_URL` | `yourblog.example` |
| Avatar PNG with a white background (optional) | `AVATAR_PATH` | unset (no avatar drawn) |
| Accent word color | `ACCENT` | `D97757` (terracotta, configurable) |
| Fonts | `HEADLINE_FONT`, `MONO_FONT` | macOS Helvetica Neue and Menlo, DejaVu on Linux |Skills included
- auto-research
- blog-publish
- braindump
- closed-loop
- comprehensive-analysis
- content-factory
- create-user-story
- daily-brief
- daily-journal
- data-forms
- editorial-illustrations
- export-open-issues
- feature-doc
- generate-prd
- generate-release-notes
- harvest
- knowledge-consolidation
- loop-engineering
- meeting-transcript
- memory-hygiene
- museum-art
- no-ai-slop
- onboarding
- product-ui-taste
- publish-to-confluence
- release-comms
- release-video
- retro
- review-cockpit
- scout
- slop-gate
- social-media-kit
- taste-skill
- team-brief
- terminal-replica
- ultragoal
- update-cog
- update-knowledge-base
- url-dump
- vector-cover
- voice-baseline
- weekly-checkin
- youtube-video-kit
Repository facts
- License
- MIT
- Created
- Oct 15, 2025
- Last pushed
- Oct 23, 2026
- Forks
- 163
- Open issues
- 9
- Project activity
- Active
Rank over the last 12 weeks
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 3, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 139 notes
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- No broad tool permissions
- 1 dangerous shell commandin 1 file: scripts/validate-agent-surface.sh:105
- 550 network referencesin 54 files: taste-skill/references/canonical-sources.md:7 product-ui-taste/references/canonical-sources.md:9 museum-art/references/rijksmuseum.md:9 and 51 more
- 1 obfuscation signalin 1 file: feature-doc/tools/screenrec.py:80
- No instruction overrides
- 53 executable scriptsin 29 files: .claude/lib/checkpoint.sh:1 .claude/skills/feature-doc/tools/recompose.py:1 .claude/skills/vector-cover/render.sh:1 and 26 more
- 115 references to secretsin 30 files: .claude/skills/feature-doc/tools/recompose.py:29 feature-doc/tools/recompose.py:29 feature-doc/tools/videokit.py:30 and 27 more
- No requests to vote in Hot
Show all 720 signals by file
.claude-plugin/plugin.json
- Network accessline 5
"author": { "name": "Huy Tieu", "url": "https://github.com/huytieu" - Network accessline 6
"name": "Huy Tieu", "url": "https://github.com/huytieu" }, - Network accessline 7
"url": "https://github.com/huytieu" - Network accessline 8
}, "homepage": "https://github.com/huytieu/COG-second-brain", "repository": { - Network accessline 9
"homepage": "https://github.com/huytieu/COG-second-brain", - Network accessline 10
"repository": { "type": "git", "url": "https://github.com/huytieu/COG-second-brain" - Network accessline 11
"type": "git", "url": "https://github.com/huytieu/COG-second-brain" }, - Network accessline 12
"url": "https://github.com/huytieu/COG-second-brain"
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.claude/skills/blog-publish/SKILL.md
- Network accessline 20
| `BUILD_CMD` | Command that builds the site | `python3 build.py` | | `SITE_DIR` | Build output folder | `site/` | | `BLOG_URL` | Public base URL | `https://blog.example.com` | - Network accessline 21
| `SITE_DIR` | Build output folder | `site/` | | `BLOG_URL` | Public base URL | `https://blog.example.com` | | `POST_PATH` | URL pattern for a post | `/blog/<slug>/` | - Network accessline 22
| `BLOG_URL` | Public base URL | `https://blog.example.com` | - Network accessline 83
Post-condition: fetch the live page and confirm the new post is there before reporting success: `curl -s $BLOG_URL/blog/<slug>/ | grep '<title>'`. Builds can take a few minutes. - Network accessline 84
`curl -s $BLOG_URL/blog/<slug>/ | grep '<title>'`. Builds can take a few minutes.
.claude/skills/content-factory/SKILL.md
- Network accessline 69
1. **Environment gate**: if the publishing surface is unavailable, or the user appears to be actively using the browser/machine — DO NOT publish. Write finished bundles to `04-projects/content-factory/out/<date>/` and let a later run (or th - Network accessline 70
2. **Post-condition check (mandatory)**: after every publish, observe the artifact — screenshot the live post, curl the live URL — before recording it as published. Never report success from the publish call alone.
.claude/skills/daily-brief/SKILL.md
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projects_referenced: ["project1"] items_count: [number] dedup_urls: ["https://primary-source-url-for-each-story-covered"] - Network accessline 166
items_count: [number] dedup_urls: ["https://primary-source-url-for-each-story-covered"] --- - Network accessline 167
dedup_urls: ["https://primary-source-url-for-each-story-covered"]
.claude/skills/feature-doc/SKILL.md
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| Setting | Meaning | Example | |---|---|---| | `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | - Network accessline 23
|---|---|---| | `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | | `ISSUE_REPO` | Issue tracker holding the feature issues | `your-org/your-product` | - Network accessline 24
| `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | - Network accessline 118
## Escape hatches - **Auth wall:** stop and ask the user to sign in. Never enter credentials. - **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the b - Network accessline 119
- **Auth wall:** stop and ask the user to sign in. Never enter credentials. - **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the browser prompts for - Network accessline 120
- **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the browser prompts for permission, the user clicks Allow.
.claude/skills/feature-doc/tools/recompose.py
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.claude/skills/memory-hygiene/SKILL.md
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- Paths and files: `ls` / `test -e`. Skills, commands, and agents named in an entry must still exist at the stated path. - URLs: resolve with a HEAD/GET (`curl -sI`); flag 404 or redirect-to-login. - Network accessline 44
- URLs: resolve with a HEAD/GET (`curl -sI`); flag 404 or redirect-to-login.
.claude/skills/museum-art/SKILL.md
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## How to fetch in THIS environment - Use **WebFetch** to hit the JSON search endpoint, then WebFetch/download the returned image URL. These APIs are server-side reachable; no browser needed. - **Exception - Art Institute of Chicago images: - Network accessline 25
- Use **WebFetch** to hit the JSON search endpoint, then WebFetch/download the returned image URL. These APIs are server-side reachable; no browser needed. - **Exception - Art Institute of Chicago images:** the JSON API (`api.artic.edu`) is - Network accessline 26
- **Exception - Art Institute of Chicago images:** the JSON API (`api.artic.edu`) is fine, but the image host `www.artic.edu/iiif/...` 403s scripted/curl fetches via a Cloudflare bot challenge. Use the JSON metadata from AIC, but download t - Network accessline 37
### 1. The Met - best all-around default - Base: `https://collectionapi.metmuseum.org/public/collection/v1/` - no key, 80 req/s, CC0 where `isPublicDomain:true`. - Network accessline 38
- Base: `https://collectionapi.metmuseum.org/public/collection/v1/` - no key, 80 req/s, CC0 where `isPublicDomain:true`. - Network accessline 39
- Search: `GET /search?q=<term>&hasImages=true&isPublicDomain=true` -> `{total, objectIDs[]}` - Object: `GET /objects/{id}` -> read `primaryImage` (full-res JPEG, static, no IIIF hop) or `primaryImageSmall` (web-large). - Example: `https:// - Network accessline 41
- Example: `https://collectionapi.metmuseum.org/public/collection/v1/search?q=sunflowers&hasImages=true&isPublicDomain=true` - Network accessline 43
### 2. Cleveland Museum of Art - only one with archival TIFF - Base: `https://openaccess-api.clevelandart.org/api/artworks/` - no key, CC0. - Network accessline 44
- Base: `https://openaccess-api.clevelandart.org/api/artworks/` - no key, CC0. - Network accessline 49
### 3. SMK (Denmark) - one-hop, broad European/Nordic - Base: `https://api.smk.dk/api/v1` - no key. License: Public Domain Mark 1.0 (functionally CC0). - Network accessline 50
- Base: `https://api.smk.dk/api/v1` - no key. License: Public Domain Mark 1.0 (functionally CC0). - Network accessline 55
### 4. Rijksmuseum - Dutch/Flemish masters (keyless, 3 hops) - Base: `https://data.rijksmuseum.nl/search/collection` (Linked Art, no key). - Network accessline 56
- Base: `https://data.rijksmuseum.nl/search/collection` (Linked Art, no key). - Network accessline 60
### 5. NGA (Washington) - bulk/offline, no live search - CSV: `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - filter `openaccess=1`. - Network accessline 61
- CSV: `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - filter `openaccess=1`. - Network accessline 62
- Image (IIIF): `https://api.nga.gov/iiif/{uuid}/full/full/0/default.jpg` - Network accessline 65
### 6. Art Institute of Chicago - Impressionism/European (image host caveat) - JSON: `GET https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id` - no key, CC0. - Image: build `https://www.a - Network accessline 66
- JSON: `GET https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id` - no key, CC0. - Network accessline 67
- Image: build `https://www.artic.edu/iiif/2/{image_id}/full/843,/0/default.jpg` - **but Cloudflare 403s scripted fetches**; download via browser or use another museum for bytes. - Network accessline 69
### 7. Getty - European painting/photography/antiquities (SPARQL) - Base: `https://data.getty.edu/museum/collection/` - no key. No keyword search; use SPARQL with `?obj crm:P138i_has_representation ?img` which returns a ready IIIF image URL - Network accessline 70
- Base: `https://data.getty.edu/museum/collection/` - no key. No keyword search; use SPARQL with `?obj crm:P138i_has_representation ?img` which returns a ready IIIF image URL in the same query. - Network accessline 73
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- **Wikimedia Commons** (keyless, best aggregator fallback): `https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch=<term>&gsrnamespace=6&prop=imageinfo&iiprop=url|extmetadata`; license per file in `extmetadata.Lic - Network accessline 76
- **Wellcome Collection** (keyless): `https://api.wellcomecollection.org/catalogue/v2/works?query=<term>`; CC0/PD medical/scientific/historical imagery. - Network accessline 77
- **Internet Archive** (keyless): `https://archive.org/advancedsearch.php?q=<term>&fl[]=identifier&output=json`; PD book illustrations, engravings, historical photos, ephemera. - Network accessline 78
- **Europeana** (free key): `https://api.europeana.eu/record/v2/search.json?wskey=<KEY>&query=<term>`; 50M+ items across European institutions. - Network accessline 79
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- **Yale LUX** (keyless, Linked Art like Getty): `https://lux.collections.yale.edu/api/search/...`; strong British art + rare books. - Network accessline 81
- **Paris Musees** (keyless, explicit CC0): `parismuseescollections.paris.fr` / `opendata.paris.fr`; French painting/decorative arts. - **NYPL Digital Collections** (free key): `https://api.repository.library.nyc/...`; PD prints, maps, phot - Network accessline 82
- **NYPL Digital Collections** (free key): `https://api.repository.library.nyc/...`; PD prints, maps, photos, illustrations. - Network accessline 83
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.claude/skills/no-ai-slop/SKILL.md
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Default target for every explanation, report, spec, chat reply, and ticket the agent writes: about 80% of the way to ASD-STE100 (Simplified Technical English, the controlled language from aerospace maintenance manuals). Source: Karpathy, 20
.claude/skills/release-comms/SKILL.md
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2. **GitHub milestone.** The milestone filtered by `TEAM_LABEL` is the release manifest. Issues may still be open, so closed does not equal shipped. 3. **Issue bodies and comments.** This is the media source. Dev-test comments ("Dev test: p - Network accessline 36
3. **Issue bodies and comments.** This is the media source. Dev-test comments ("Dev test: passed on prod") and QA comments ("Verified PASSED on staging") carry screenshots and demo videos as GitHub attachment URLs. For a private repo these - Network accessline 37
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Check the Content-Type with `curl -sI` for the right extension. GitHub videos are usually QuickTime `.mov`. Treat a comment-level "Verified PASSED" as the ship signal, because issues often stay open until a later close step.
.claude/skills/release-video/SKILL.md
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This skill makes motion scenes and composes demos from recordings. It does not cut raw footage of people talking. For narrated takes, founder voice-overs or interviews, use [browser-use/video-use](https://github.com/browser-use/video-use), - Access to secretsline 19
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.claude/skills/social-media-kit/scripts/linearcard.py
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.claude/skills/taste-skill/SKILL.md
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1. **Image-generation tool first.** If ANY image-gen tool is available in the environment (`generate_image`, MCP image tool, IDE-integrated gen, OpenAI image tools, etc.) you MUST use it to create section-specific assets: hero photography, - Network accessline 268
2. **Real web images second.** When no gen tool is available, use real photography sources. Acceptable defaults: * `https://picsum.photos/seed/{descriptive-seed}/{w}/{h}` for placeholder photography (seed should describe the section, e.g - Network accessline 269
* `https://picsum.photos/seed/{descriptive-seed}/{w}/{h}` for placeholder photography (seed should describe the section, e.g. `marrow-cookware-kitchen`) - Network accessline 276
**Real company logos for social proof.** When the brief calls for a "Trusted by / Used by / Customers" logo wall, do NOT default to plain text wordmarks (`<span>Acme Co</span>` styled in a row). Use real SVG logos: * **Source: Simple Icons* - Network accessline 277
* **Source: Simple Icons** (`https://cdn.simpleicons.org/{slug}/ffffff` for any color, or `simple-icons` npm package). Covers most known brands. - Network accessline 500
* **Hand-rolled decorative SVGs strongly discouraged** as default (see Section 4.8). * **NO div-based fake screenshots.** Never build a fake product UI out of `<div>` rectangles to simulate a screenshot. Use real images, generated images, o - Network accessline 501
* **NO div-based fake screenshots.** Never build a fake product UI out of `<div>` rectangles to simulate a screenshot. Use real images, generated images, or skip the preview. * **NO broken Unsplash links.** Use `https://picsum.photos/seed/{ - Network accessline 502
* **NO broken Unsplash links.** Use `https://picsum.photos/seed/{descriptive-string}/{w}/{h}`, or generated photo placeholders, or actual assets.
.claude/skills/team-brief/SKILL.md
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2. For each matched Linear issue: - Use `mcp__claude_ai_Linear__create_attachment` to attach the PR URL to the issue (if not already linked). Use format: - url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - Network accessline 164
- Use `mcp__claude_ai_Linear__create_attachment` to attach the PR URL to the issue (if not already linked). Use format: - url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - title: "PR #XXX: [PR title]" - Network accessline 165
- url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - Access to secretsline 227
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**API Details:** - Base URL: `https://api.hackmd.io/v1` - Network accessline 230
- Base URL: `https://api.hackmd.io/v1` - Network accessline 235
1. **Create the note** via `POST https://api.hackmd.io/v1/notes` with: - Network accessline 248
2. **Update the note content** via `PATCH https://api.hackmd.io/v1/notes/<id>` with the full brief markdown content: - Network accessline 249
- Read the saved brief from `[CUSTOMIZE: path/to/briefs/]daily-brief-YYYY-MM-DD.md` - Use Python to JSON-encode the content and write to a temp file - Send via `curl -X PATCH ... -d @/tmp/hackmd-payload.json` - Network accessline 251
- Send via `curl -X PATCH ... -d @/tmp/hackmd-payload.json` - Network accessline 253
3. **Store the HackMD link** for use in the Slack message AND the local brief file: - The `publishLink` from step 1 (format: `https://hackmd.io/@[CUSTOMIZE: your-hackmd-username]/<shortId>`) - Network accessline 254
- The `publishLink` from step 1 (format: `https://hackmd.io/@[CUSTOMIZE: your-hackmd-username]/<shortId>`) - Network accessline 286
- If there's nothing urgent, keep it short: "Good day, we shipped X, nothing on fire." - Reference specific people only when it's constructive (celebrating a win or when they're the clear owner of a follow-up). Don't publicly call people - Network accessline 287
- Reference specific people only when it's constructive (celebrating a win or when they're the clear owner of a follow-up). Don't publicly call people out for missing deadlines. - **ALWAYS embed GitHub links for PR references.** Use Slac - Network accessline 288
- **ALWAYS embed GitHub links for PR references.** Use Slack link format: `<https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX>`. Never use bare `#930` — make it clickable.
.claude/skills/ultragoal/SKILL.md
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│ (always run for ultragoals — cross-phase regression is the main risk) ▼ CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return - Network accessline 70
▼ CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return │ EVIDENCE AC-n | CP-5 | PASS | <observation> | <artifact> - Network accessline 71
CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return
.claude/skills/update-cog/SKILL.md
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# Add the upstream remote if not already present git remote get-url cog-upstream 2>/dev/null || \ git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git - Network accessline 26
git remote get-url cog-upstream 2>/dev/null || git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git - Network accessline 27
git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git
.claude/skills/url-dump/SKILL.md
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# CORRECT type: "url-bookmark" url: "https://example.com/path?query=value" - Network accessline 352
type: "url-bookmark" url: "https://example.com/path?query=value" tags: ["#bookmark", "#article", "#ai"] - Network accessline 353
url: "https://example.com/path?query=value" - Network accessline 358
# INCORRECT type: url-bookmark url: https://example.com/path?query=value - Network accessline 359
type: url-bookmark url: https://example.com/path?query=value tags: [#bookmark, #article, #ai] - Network accessline 360
url: https://example.com/path?query=value
.claude/skills/vector-cover/render.sh
.claude/skills/youtube-video-kit/SKILL.md
.claude/skills/youtube-video-kit/scripts/ytthumb.py
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scripts/validate-agent-surface.sh
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#!/usr/bin/env bash - Network accessline 54
with open('plugin.json') as f: data = json.load(f) assert data['$schema'] == 'https://agent-plugins.org/schemas/1.0.0/plugin.schema.json' - Network accessline 55
data = json.load(f) assert data['$schema'] == 'https://agent-plugins.org/schemas/1.0.0/plugin.schema.json' assert data['name'] == 'cog-second-brain' - Network accessline 56
assert data['$schema'] == 'https://agent-plugins.org/schemas/1.0.0/plugin.schema.json' - Dangerous shell operationline 105
rm -f "$manifest_tmp"
skills/blog-publish/SKILL.md
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| `BUILD_CMD` | Command that builds the site | `python3 build.py` | | `SITE_DIR` | Build output folder | `site/` | | `BLOG_URL` | Public base URL | `https://blog.example.com` | - Network accessline 21
| `SITE_DIR` | Build output folder | `site/` | | `BLOG_URL` | Public base URL | `https://blog.example.com` | | `POST_PATH` | URL pattern for a post | `/blog/<slug>/` | - Network accessline 22
| `BLOG_URL` | Public base URL | `https://blog.example.com` | - Network accessline 83
Post-condition: fetch the live page and confirm the new post is there before reporting success: `curl -s $BLOG_URL/blog/<slug>/ | grep '<title>'`. Builds can take a few minutes. - Network accessline 84
`curl -s $BLOG_URL/blog/<slug>/ | grep '<title>'`. Builds can take a few minutes.
skills/closed-loop/references/spec-template.md
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Every criterion is **falsifiable**: a named observation either happens or it does not. "Works well" is not a criterion. "`curl -s <url> | grep -q '<string>'` exits 0" is. - Network accessline 15
Every criterion is **falsifiable**: a named observation either happens or it does not. "Works well" is not a criterion. "`curl -s <url> | grep -q '<string>'` exits 0" is. - Network accessline 16
"Works well" is not a criterion. "`curl -s <url> | grep -q '<string>'` exits 0" is. - Network accessline 16
"Works well" is not a criterion. "`curl -s <url> | grep -q '<string>'` exits 0" is.
skills/content-factory/SKILL.md
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1. **Environment gate**: if the publishing surface is unavailable, or the user appears to be actively using the browser/machine — DO NOT publish. Write finished bundles to `04-projects/content-factory/out/<date>/` and let a later run (or th - Network accessline 70
2. **Post-condition check (mandatory)**: after every publish, observe the artifact — screenshot the live post, curl the live URL — before recording it as published. Never report success from the publish call alone.
skills/daily-brief/SKILL.md
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projects_referenced: ["project1"] items_count: [number] dedup_urls: ["https://primary-source-url-for-each-story-covered"] - Network accessline 166
items_count: [number] dedup_urls: ["https://primary-source-url-for-each-story-covered"] --- - Network accessline 167
dedup_urls: ["https://primary-source-url-for-each-story-covered"]
skills/editorial-illustrations/assets/gallery.html
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<meta name="viewport" content="width=device-width, initial-scale=1"> <title>Editorial illustrations — claim → geometry</title> <link rel="preconnect" href="https://fonts.googleapis.com"> - Network accessline 6
<title>Editorial illustrations — claim → geometry</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=Inter:wght@400;480;540&family=JetBrains+Mono:wght@400;700&display=swap" rel="stylesheet">
skills/editorial-illustrations/references/blog-figure-kit.md
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Capture tweets from the official embed in a headless browser at 2x, light theme: `https://platform.twitter.com/embed/Tweet.html?id=<id>&theme=light&dnt=true&hideThread=true`, then screenshot the first `article` with Playwright (`device_scal - Network accessline 56
`https://platform.twitter.com/embed/Tweet.html?id=<id>&theme=light&dnt=true&hideThread=true`, then screenshot the first `article` with Playwright (`device_scale_factor=2`, viewport 560 wide) and save as JPEG q90. This renders logged out and
skills/feature-doc/SKILL.md
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| Setting | Meaning | Example | |---|---|---| | `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | - Network accessline 23
|---|---|---| | `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | | `ISSUE_REPO` | Issue tracker holding the feature issues | `your-org/your-product` | - Network accessline 24
| `APP_URL` | Start page of the live app to drive | `https://app.example.com/projects/1` | - Network accessline 118
## Escape hatches - **Auth wall:** stop and ask the user to sign in. Never enter credentials. - **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the b - Network accessline 119
- **Auth wall:** stop and ask the user to sign in. Never enter credentials. - **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the browser prompts for - Network accessline 120
- **CDP will not connect:** point your harness at the browser's remote-debugging URL (for example `http://localhost:9222`) and reload it. If the browser prompts for permission, the user clicks Allow.
skills/feature-doc/tools/arcade-record.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 11
Usage: export BU_CDP_URL=http://localhost:9222 # your daily Chrome w/ debugging - Network accessline 12
export BU_CDP_URL=http://localhost:9222 # your daily Chrome w/ debugging - Network accessline 25
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skills/feature-doc/tools/recompose.py
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#!/usr/bin/env python3 - Access to secretsline 29
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skills/feature-doc/tools/screenrec.py
- Executable scriptline 1
Executable file - Network accessline 8
# # Run THROUGH browser-harness (helpers in scope): # SR_OUT=/path/out SR_URL=https://... SR_STEPS='[...]' browser-harness < screenrec.py - Network accessline 9
# Run THROUGH browser-harness (helpers in scope): # SR_OUT=/path/out SR_URL=https://... SR_STEPS='[...]' browser-harness < screenrec.py # - Network accessline 10
# SR_OUT=/path/out SR_URL=https://... SR_STEPS='[...]' browser-harness < screenrec.py - Access to secretsline 23
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open(p, "wb").write(base64.b64decode(r["data"]))
skills/feature-doc/tools/videokit.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 30
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skills/memory-hygiene/SKILL.md
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- Paths and files: `ls` / `test -e`. Skills, commands, and agents named in an entry must still exist at the stated path. - URLs: resolve with a HEAD/GET (`curl -sI`); flag 404 or redirect-to-login. - Network accessline 44
- URLs: resolve with a HEAD/GET (`curl -sI`); flag 404 or redirect-to-login.
skills/museum-art/SKILL.md
- Network accessline 24
## How to fetch in THIS environment - Use **WebFetch** to hit the JSON search endpoint, then WebFetch/download the returned image URL. These APIs are server-side reachable; no browser needed. - **Exception - Art Institute of Chicago images: - Network accessline 25
- Use **WebFetch** to hit the JSON search endpoint, then WebFetch/download the returned image URL. These APIs are server-side reachable; no browser needed. - **Exception - Art Institute of Chicago images:** the JSON API (`api.artic.edu`) is - Network accessline 26
- **Exception - Art Institute of Chicago images:** the JSON API (`api.artic.edu`) is fine, but the image host `www.artic.edu/iiif/...` 403s scripted/curl fetches via a Cloudflare bot challenge. Use the JSON metadata from AIC, but download t - Network accessline 37
### 1. The Met - best all-around default - Base: `https://collectionapi.metmuseum.org/public/collection/v1/` - no key, 80 req/s, CC0 where `isPublicDomain:true`. - Network accessline 38
- Base: `https://collectionapi.metmuseum.org/public/collection/v1/` - no key, 80 req/s, CC0 where `isPublicDomain:true`. - Network accessline 39
- Search: `GET /search?q=<term>&hasImages=true&isPublicDomain=true` -> `{total, objectIDs[]}` - Object: `GET /objects/{id}` -> read `primaryImage` (full-res JPEG, static, no IIIF hop) or `primaryImageSmall` (web-large). - Example: `https:// - Network accessline 41
- Example: `https://collectionapi.metmuseum.org/public/collection/v1/search?q=sunflowers&hasImages=true&isPublicDomain=true` - Network accessline 43
### 2. Cleveland Museum of Art - only one with archival TIFF - Base: `https://openaccess-api.clevelandart.org/api/artworks/` - no key, CC0. - Network accessline 44
- Base: `https://openaccess-api.clevelandart.org/api/artworks/` - no key, CC0. - Network accessline 49
### 3. SMK (Denmark) - one-hop, broad European/Nordic - Base: `https://api.smk.dk/api/v1` - no key. License: Public Domain Mark 1.0 (functionally CC0). - Network accessline 50
- Base: `https://api.smk.dk/api/v1` - no key. License: Public Domain Mark 1.0 (functionally CC0). - Network accessline 55
### 4. Rijksmuseum - Dutch/Flemish masters (keyless, 3 hops) - Base: `https://data.rijksmuseum.nl/search/collection` (Linked Art, no key). - Network accessline 56
- Base: `https://data.rijksmuseum.nl/search/collection` (Linked Art, no key). - Network accessline 60
### 5. NGA (Washington) - bulk/offline, no live search - CSV: `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - filter `openaccess=1`. - Network accessline 61
- CSV: `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - filter `openaccess=1`. - Network accessline 62
- Image (IIIF): `https://api.nga.gov/iiif/{uuid}/full/full/0/default.jpg` - Network accessline 65
### 6. Art Institute of Chicago - Impressionism/European (image host caveat) - JSON: `GET https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id` - no key, CC0. - Image: build `https://www.a - Network accessline 66
- JSON: `GET https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id` - no key, CC0. - Network accessline 67
- Image: build `https://www.artic.edu/iiif/2/{image_id}/full/843,/0/default.jpg` - **but Cloudflare 403s scripted fetches**; download via browser or use another museum for bytes. - Network accessline 69
### 7. Getty - European painting/photography/antiquities (SPARQL) - Base: `https://data.getty.edu/museum/collection/` - no key. No keyword search; use SPARQL with `?obj crm:P138i_has_representation ?img` which returns a ready IIIF image URL - Network accessline 70
- Base: `https://data.getty.edu/museum/collection/` - no key. No keyword search; use SPARQL with `?obj crm:P138i_has_representation ?img` which returns a ready IIIF image URL in the same query. - Network accessline 73
[Credential-related source omitted] - Network accessline 74
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Network accessline 75
- **Wikimedia Commons** (keyless, best aggregator fallback): `https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch=<term>&gsrnamespace=6&prop=imageinfo&iiprop=url|extmetadata`; license per file in `extmetadata.Lic - Network accessline 76
- **Wellcome Collection** (keyless): `https://api.wellcomecollection.org/catalogue/v2/works?query=<term>`; CC0/PD medical/scientific/historical imagery. - Network accessline 77
- **Internet Archive** (keyless): `https://archive.org/advancedsearch.php?q=<term>&fl[]=identifier&output=json`; PD book illustrations, engravings, historical photos, ephemera. - Network accessline 78
- **Europeana** (free key): `https://api.europeana.eu/record/v2/search.json?wskey=<KEY>&query=<term>`; 50M+ items across European institutions. - Network accessline 79
[Credential-related source omitted] - Network accessline 80
- **Yale LUX** (keyless, Linked Art like Getty): `https://lux.collections.yale.edu/api/search/...`; strong British art + rare books. - Network accessline 81
- **Paris Musees** (keyless, explicit CC0): `parismuseescollections.paris.fr` / `opendata.paris.fr`; French painting/decorative arts. - **NYPL Digital Collections** (free key): `https://api.repository.library.nyc/...`; PD prints, maps, phot - Network accessline 82
- **NYPL Digital Collections** (free key): `https://api.repository.library.nyc/...`; PD prints, maps, photos, illustrations. - Network accessline 83
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted]
skills/museum-art/references/_synthesis.md
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| 3 | **SMK (National Gallery of Denmark)** | Yes, no key | Public Domain Mark (functionally CC0) | `GET /art/search?keys=*&filters=[public_domain:true]&filters=[has_image:true]` (repeat `filters=`, don't concatenate!) → read `image_native` - Network accessline 12
| 4 | **Rijksmuseum** | Yes, no key | CC0/PD (mostly), check per-object | `GET /search/collection?type=painting&imageAvailable=true` → walk object → VisualItem → DigitalObject → `access_point[0].id` is the ready IIIF URL (3 sequential GETs) - Network accessline 13
| 5 | **NGA (National Gallery of Art, DC)** | Yes, no key | CC0 (dataset); per-image `openaccess` flag gates rights | Download `published_images.csv` from GitHub, filter `openaccess=1`, then `GET https://api.nga.gov/iiif/{uuid}/full/full/0/ - Network accessline 14
| 6 | **Art Institute of Chicago** | Yes, no key | CC0 | `GET /artworks/search?query[term][is_public_domain]=true&fields=...,image_id` → build `https://www.artic.edu/iiif/2/{image_id}/full/full/0/default.jpg` — **but** the image host (`www. - Access to secretsline 16
[Credential-related source omitted] - Network accessline 38
- **Wikimedia Commons API** — `https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch=<term>&gsrnamespace=6&prop=imageinfo&iiprop=url|extmetadata` (fully keyless). Aggregates PD/CC-licensed images from dozens of mus - Network accessline 39
- **Europeana API** — `https://api.europeana.eu/record/v2/search.json?wskey=<KEY>&query=<term>` — aggregates 50M+ items from thousands of European cultural institutions in one schema; **requires a free API key** (instant self-serve signup, - Network accessline 40
[Credential-related source omitted] - Network accessline 41
- **Yale (LUX / Yale Center for British Art)** — `https://lux.collections.yale.edu/api/search/...` (Linked Art model, similar shape to Getty's) — keyless, aggregates Yale University Art Gallery + Yale Center for British Art + Beinecke; stro - Network accessline 42
- **Paris Musées (Paris city museums collections)** — open-data portal at `https://www.parismuseescollections.paris.fr/en/collections` with a CC0 bulk dataset also mirrored on Paris's open-data platform (`opendata.paris.fr`) — keyless, Fren - Network accessline 43
- **NYPL Digital Collections API** — `https://api.repository.library.nyc/...` — **requires a free key** (instant signup via `api.repository.library.nyc/register`). Huge trove of digitized public-domain prints, maps, photographs, illustratio - Network accessline 44
- **Wellcome Collection API** — `https://api.wellcomecollection.org/catalogue/v2/works?query=<term>` — keyless, no key needed. CC0/PD medical, historical, and scientific imagery — a good niche source for essay figures on health/science topi - Network accessline 45
- **Internet Archive** — `https://archive.org/advancedsearch.php?q=<term>&fl[]=identifier&output=json` — keyless. Not a museum but a deep well of PD book illustrations, historical photographs, and scanned ephemera; useful when the need is " - Network accessline 46
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted]
skills/museum-art/references/artic.md
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## Base URL `https://api.artic.edu/api/v1` - Network accessline 6
`https://api.artic.edu/api/v1` - Network accessline 8
Official docs: https://api.artic.edu/docs/ (confirmed live and current). - Network accessline 26
GET https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id,artist_display&limit=20 - Network accessline 34
https://www.artic.edu/iiif/2/{image_id}/full/843,/0/default.jpg - Network accessline 36
- `843,` = width 843px, height auto — this is the museum's own site default and the most-likely-cached size. - For actual full/max resolution, replace the size segment with `full` (i.e. `.../full/full/0/default.jpg`) — larger, uncached, slo - Network accessline 38
- The IIIF base (`https://www.artic.edu/iiif/2`) is also returned dynamically in every API response under `config.iiif_url` — prefer reading it from there over hardcoding, in case they ever move image hosting. - Network accessline 44
https://api.artic.edu/api/v1/artworks/search?query[term][is_public_domain]=true&fields=id,title,image_id,artist_display&limit=3 - Network accessline 49
- id 20684, "Paris Street; Rainy Day", Gustave Caillebotte, image_id `f8fd76e9-c396-5678-36ed-6a348c904d27` - Total public-domain-with-fields matches in collection: 61,568 (paginated, 20,523 pages at limit=3) - `config.iiif_url` in response - Network accessline 51
- `config.iiif_url` in response: `https://www.artic.edu/iiif/2` - Network accessline 55
https://www.artic.edu/iiif/2/6644829f-f292-c5c4-a73c-0356a6fdbf0d/full/843,/0/default.jpg - Network accessline 60
GET https://api.artic.edu/api/v1/artworks/28560?fields=id,title,image_id - Network accessline 66
- **JSON API: verified working.** Both the search endpoint and the single-artwork endpoint were fetched live and returned real, current data (61,568 public-domain artworks, correct image_id, correct CC0 license text in `info`). - **IIIF ima - Network accessline 67
- **IIIF image URL: correct per official docs, but NOT independently confirmed as a working binary download by this research pass.** Direct `curl`/WebFetch requests to `www.artic.edu/iiif/2/.../default.jpg` were blocked with `HTTP 403` + `c
skills/museum-art/references/cleveland.md
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## TL;DR - **Base URL:** `https://openaccess-api.clevelandart.org/api/artworks/` - Network accessline 4
- **Base URL:** `https://openaccess-api.clevelandart.org/api/artworks/` - Network accessline 9
## API Basics - Docs: https://openaccess-api.clevelandart.org/ (interactive docs, last updated 2025-07-11) - Network accessline 10
- Docs: https://openaccess-api.clevelandart.org/ (interactive docs, last updated 2025-07-11) - Network accessline 11
- GitHub mirror (full dataset dump, JSON/CSV, updated weekly): https://github.com/ClevelandMuseumArt/openaccess - Network accessline 12
- Program overview / license page: https://www.clevelandart.org/open-access - Network accessline 39
Image CDN host: `https://openaccess-cdn.clevelandart.org/...` — direct hotlinkable JPEG/TIFF, no auth. - Network accessline 45
GET https://openaccess-api.clevelandart.org/api/artworks/?cc0=1&has_image=1&limit=10 - Network accessline 61
https://openaccess-api.clevelandart.org/api/artworks/?cc0=1&has_image=1&limit=3 - Network accessline 68
"title": "Nathaniel Hurd", "share_license_status": "CC0", "images.web.url": "https://openaccess-cdn.clevelandart.org/1915.534/1915.534_web.jpg" - Network accessline 69
"share_license_status": "CC0", "images.web.url": "https://openaccess-cdn.clevelandart.org/1915.534/1915.534_web.jpg" }, - Network accessline 70
"images.web.url": "https://openaccess-cdn.clevelandart.org/1915.534/1915.534_web.jpg" - Network accessline 74
"title": "Stag at Sharkey's", "share_license_status": "CC0", "images.web.url": "https://openaccess-cdn.clevelandart.org/1922.1133/1922.1133_web.jpg" - Network accessline 75
"share_license_status": "CC0", "images.web.url": "https://openaccess-cdn.clevelandart.org/1922.1133/1922.1133_web.jpg" } - Network accessline 76
"images.web.url": "https://openaccess-cdn.clevelandart.org/1922.1133/1922.1133_web.jpg" - Network accessline 83
https://openaccess-cdn.clevelandart.org/1915.534/1915.534_web.jpg - Network accessline 100
## Sources - https://openaccess-api.clevelandart.org/ (API docs) - Network accessline 101
- https://openaccess-api.clevelandart.org/ (API docs) - Network accessline 102
- https://www.clevelandart.org/open-access (license/program page) - Network accessline 103
- https://github.com/ClevelandMuseumArt/openaccess (dataset mirror) - Network accessline 104
- Live verification: `GET https://openaccess-api.clevelandart.org/api/artworks/?cc0=1&has_image=1&limit=3` fetched 2026-07-24, returned real JSON. - Network accessline 105
- Live verification: `https://openaccess-cdn.clevelandart.org/1915.534/1915.534_web.jpg` fetched 2026-07-24, confirmed real JPEG (image/jpeg, 396.3KB).
skills/museum-art/references/getty.md
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## TL;DR - **Base URL (Linked Art / JSON-LD API):** `https://data.getty.edu/museum/collection/` - **API key:** NOT required. All endpoints below returned `HTTP 200` with a plain unauthenticated `curl`. - Network accessline 4
- **Base URL (Linked Art / JSON-LD API):** `https://data.getty.edu/museum/collection/` - **API key:** NOT required. All endpoints below returned `HTTP 200` with a plain unauthenticated `curl`. - **License:** Dataset/collection metadata is * - Network accessline 5
- **API key:** NOT required. All endpoints below returned `HTTP 200` with a plain unauthenticated `curl`. - **License:** Dataset/collection metadata is **CC0 1.0** (confirmed live in every record's `subject_to` "License for Collection Metad - Network accessline 6
- **License:** Dataset/collection metadata is **CC0 1.0** (confirmed live in every record's `subject_to` "License for Collection Metadata" block, and in the official docs text). Images come from the **Getty Open Content Program** — most are - Network accessline 7
- **Images:** served via **IIIF Image API** at `https://media.getty.edu/iiif/image/<image-id>/...` — full resolution, no key, `Access-Control-Allow-Origin: *`. - Network accessline 8
- **Search:** there is **no keyword/full-text REST search endpoint** (the docs explicitly say so — see Gotchas). Discovery is via the public **SPARQL endpoint** `https://data.getty.edu/museum/collection/sparql`, which I ran live and got rea - Network accessline 12
## 1. What the API actually is Official docs: https://data.getty.edu/museum/collection/docs/ (a Nuxt SPA; scraped its raw payload to get the real text since WebFetch truncates it — Bash `curl` worked fine). - Network accessline 13
Official docs: https://data.getty.edu/museum/collection/docs/ (a Nuxt SPA; scraped its raw payload to get the real text since WebFetch truncates it — Bash `curl` worked fine). - Network accessline 15
- Model: **Linked.Art** (a CIDOC-CRM profile) + **JSON-LD**. - Entity types: `object`, `place`, `document`, `group`, `person`, `exhibition`, `activity`. - Record URL pattern: `https://data.getty.edu/museum/collection/<ENTITY_TYPE>/<ENTITY_I - Network accessline 16
- Entity types: `object`, `place`, `document`, `group`, `person`, `exhibition`, `activity`. - Record URL pattern: `https://data.getty.edu/museum/collection/<ENTITY_TYPE>/<ENTITY_ID>` — returns JSON-LD directly, no `Accept` header needed. - - Network accessline 17
- Record URL pattern: `https://data.getty.edu/museum/collection/<ENTITY_TYPE>/<ENTITY_ID>` — returns JSON-LD directly, no `Accept` header needed. - Network accessline 18
- Change tracking: ActivityStreams feed at `https://data.getty.edu/museum/collection/activity-stream`. - Network accessline 19
- Graph queries: public **SPARQL** endpoint + a browser UI at `https://data.getty.edu/museum/collection/sparql-ui`. - Network accessline 20
- Images: Getty-wide **IIIF** API at `https://media.getty.edu` (Image API 2.1.1 + Presentation API 2.1.1/3). - Network accessline 27
## 3. License — precisely - **Dataset/metadata:** CC0 1.0 Universal, unconditionally, per Getty's own statement and confirmed live in every object record I pulled (`subject_to` → `Right` → `classified_as` → `http://creativecommons.org/publi - Network accessline 28
- **Dataset/metadata:** CC0 1.0 Universal, unconditionally, per Getty's own statement and confirmed live in every object record I pulled (`subject_to` → `Right` → `classified_as` → `http://creativecommons.org/publicdomain/zero/1.0/`, displa - Network accessline 41
GET https://data.getty.edu/museum/collection/sparql?query=<url-encoded SPARQL> - Network accessline 46
PREFIX crm: <http://www.cidoc-crm.org/cidoc-crm/> - Network accessline 57
GET https://data.getty.edu/museum/collection/object/<uuid-from-step-A> - Network accessline 66
https://media.getty.edu/iiif/image/<image-id>/full/full/0/default.jpg # full native resolution - Network accessline 67
https://media.getty.edu/iiif/image/<image-id>/full/!600,/0/default.jpg # thumbnail, max 600px, aspect preserved - Network accessline 68
https://media.getty.edu/iiif/image/<image-id>/<x,y,w,h>/<w,h>/0/default.jpg # cropped region (used on Getty's own site) - Network accessline 77
https://data.getty.edu/museum/collection/sparql?query=PREFIX%20crm%3A%20%3Chttp%3A//www.cidoc-crm.org/cidoc-crm/%3E%0ASELECT%20%3Fobj%20%3Flabel%20%3Fimg%20WHERE%20%7B%0A%20%20%3Fobj%20a%20crm%3AE22_Human-Made_Object%20.%0A%20%20%3Fobj%20rd - Network accessline 81
{"obj":"https://data.getty.edu/museum/collection/object/84eb7a1d-f806-4da9-a0ed-77d6b355df7e", - Network accessline 83
"img":"https://media.getty.edu/iiif/image/f45355f5-8ae6-4813-bf7a-dc94997f76f0/full/full/0/default.jpg"} - Network accessline 88
https://data.getty.edu/museum/collection/object/c88b3df0-de91-4f5b-a9ef-7b2b9a6d8abb - Network accessline 91
**exampleImageUrl** (full resolution — verified with `curl -I`: `HTTP/2 200`, `content-type: image/jpeg`, `content-disposition: inline; filename="8c255d80-7382-46db-9fa8-892c0d37247e_9021x7122.jpg"`, `access-control-allow-origin: *`): - Network accessline 93
https://media.getty.edu/iiif/image/8c255d80-7382-46db-9fa8-892c0d37247e/full/full/0/default.jpg - Network accessline 106
## Sources - https://data.getty.edu/museum/collection/docs/ (API docs, scraped via curl on `_payload.json`) - Network accessline 107
- https://data.getty.edu/museum/collection/docs/ (API docs, scraped via curl on `_payload.json`) - Network accessline 108
- https://www.getty.edu/projects/open-content-program/ (Open Content Program description) - Network accessline 109
- https://www.getty.edu/projects/open-data-apis/ (open data/APIs overview)
skills/museum-art/references/met.md
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## Base URL `https://collectionapi.metmuseum.org/public/collection/v1/` - Network accessline 6
`https://collectionapi.metmuseum.org/public/collection/v1/` - Network accessline 8
Docs: https://metmuseum.github.io/ (official, GitHub Pages) - Network accessline 9
Initiative overview: https://www.metmuseum.org/hubs/open-access - Network accessline 10
Repo: https://github.com/metmuseum/openaccess - Network accessline 35
GET https://collectionapi.metmuseum.org/public/collection/v1/search?q=<term>&hasImages=true&isPublicDomain=true - Network accessline 41
GET https://collectionapi.metmuseum.org/public/collection/v1/objects/{objectID} - Network accessline 56
https://collectionapi.metmuseum.org/public/collection/v1/search?q=sunflowers&hasImages=true&isPublicDomain=true - Network accessline 62
https://collectionapi.metmuseum.org/public/collection/v1/objects/436535 - Network accessline 64
Verified live via curl — returns real JSON: - Network accessline 68
- `department`: European Paintings - `isPublicDomain`: `true` - `primaryImage`: `https://images.metmuseum.org/CRDImages/ep/original/DP-42549-001.jpg` - Network accessline 69
- `isPublicDomain`: `true` - `primaryImage`: `https://images.metmuseum.org/CRDImages/ep/original/DP-42549-001.jpg` - `primaryImageSmall`: `https://images.metmuseum.org/CRDImages/ep/web-large/DP-42549-001.jpg` - Network accessline 70
- `primaryImage`: `https://images.metmuseum.org/CRDImages/ep/original/DP-42549-001.jpg` - Network accessline 71
- `primaryImageSmall`: `https://images.metmuseum.org/CRDImages/ep/web-large/DP-42549-001.jpg` - Network accessline 75
https://images.metmuseum.org/CRDImages/ep/original/DP-42549-001.jpg - Network accessline 77
`curl -sI` on this URL returned `HTTP/2 200`, `content-type: image/jpeg`, `content-length: 8291194` (~8.3MB) — confirmed real, downloadable, full-resolution JPEG. - Network accessline 84
- **`primaryImage` can be empty string** even for public-domain objects that haven't been photographed — always check `primaryImage !== ""` (or non-null) before using. - **No IIIF sizing params** — unlike some museum APIs (e.g. Smithsonian, - Network accessline 85
- **No IIIF sizing params** — unlike some museum APIs (e.g. Smithsonian, some Europeana sources), the Met does not expose a dynamic image resize API. You get exactly two fixed sizes baked into the URL (`original`, `web-large`) plus optional - Network accessline 86
- **Attribution not legally required (CC0)** but recommended: "Image courtesy of The Metropolitan Museum of Art" / link back to the object's page (`https://www.metmuseum.org/art/collection/search/{objectID}`).
skills/museum-art/references/nga.md
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- **Data/metadata**: CSV dumps in the GitHub repo `github.com/NationalGalleryOfArt/opendata` (data lives under `/data/*.csv`, fetch raw via `raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/<file>.csv`). No search API — thi - Network accessline 8
- **Image server (IIIF Image API 2.0, level1)**: `https://api.nga.gov/iiif/{uuid}/{region}/{size}/{rotation}/{quality}.{format}` - Network accessline 9
- `info.json` at `https://api.nga.gov/iiif/{uuid}/info.json` confirms native width/height and supported sizes/qualities. - Network accessline 39
1. Fetch `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv`. - Network accessline 51
GET https://api.nga.gov/iiif/{uuid}/full/full/0/default.jpg - Network accessline 55
GET https://api.nga.gov/iiif/{uuid}/full/max/0/default.jpg - Network accessline 61
- **exampleApiUrl** (metadata, first bytes of the open-access image catalog): `https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - Network accessline 62
`https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv` - Network accessline 63
First data row: `uuid=00007f61-4922-417b-8f27-893ea328206c, iiifurl=https://api.nga.gov/iiif/00007f61-4922-417b-8f27-893ea328206c, openaccess=1, depictstmsobjectid=17387` (join to `objects.csv` objectid 17387 for title/artist). - Network accessline 65
- **info.json check**: `https://api.nga.gov/iiif/00007f61-4922-417b-8f27-893ea328206c/info.json` → returned valid IIIF Image API 2.0 descriptor, native size 3365×4332, level1 profile, jpg format, supports `sizeAboveFull`. - Network accessline 66
`https://api.nga.gov/iiif/00007f61-4922-417b-8f27-893ea328206c/info.json` → returned valid IIIF Image API 2.0 descriptor, native size 3365×4332, level1 profile, jpg format, supports `sizeAboveFull`. - Network accessline 68
- **exampleImageUrl** (full-resolution, downloadable): `https://api.nga.gov/iiif/00007f61-4922-417b-8f27-893ea328206c/full/full/0/default.jpg` - Network accessline 69
`https://api.nga.gov/iiif/00007f61-4922-417b-8f27-893ea328206c/full/full/0/default.jpg` - Network accessline 75
- Fetched `objects.csv` raw (byte-range) — confirmed schema and that title/artist metadata lives here, joined via `objectid`. - `curl` GET `info.json` for a real uuid — valid IIIF descriptor returned. - `curl -I` (HEAD) on `full/full/0/defa - Network accessline 76
- `curl` GET `info.json` for a real uuid — valid IIIF descriptor returned. - `curl -I` (HEAD) on `full/full/0/default.jpg` and `full/max/0/default.jpg` — both HTTP 200, image/jpeg, multi-MB content-length, i.e. genuinely full resolution, no - Network accessline 77
- `curl -I` (HEAD) on `full/full/0/default.jpg` and `full/max/0/default.jpg` — both HTTP 200, image/jpeg, multi-MB content-length, i.e. genuinely full resolution, not a redirect or error page. - Network accessline 78
- `curl -I` on the `iiifthumburl` pattern (`full/!200,200/0/default.jpg`) — HTTP 200, small JPEG (9.7 KB), confirming the thumbnail convenience URL also works.
skills/museum-art/references/rijksmuseum.md
- Network accessline 9
## Base URL - Search: `https://data.rijksmuseum.nl/search/collection` - Network accessline 10
- Search: `https://data.rijksmuseum.nl/search/collection` - Network accessline 11
- Object resolver (linked data / JSON-LD): `https://id.rijksmuseum.nl/{objectId}` - Network accessline 12
- Image (IIIF, via Micrio): `https://iiif.micr.io/{imageId}/{region}/{size}/{rotation}/{quality}.{format}` - Network accessline 17
## License - **CC0 / Public Domain** for most digitized collection objects — confirmed directly on the test object's `VisualItem` record: `subject_to` → `classified_as` → `https://creativecommons.org/publicdomain/mark/1.0/` ("Public Domain" - Network accessline 18
- **CC0 / Public Domain** for most digitized collection objects — confirmed directly on the test object's `VisualItem` record: `subject_to` → `classified_as` → `https://creativecommons.org/publicdomain/mark/1.0/` ("Public Domain") and a nes - Network accessline 24
### (a) Search for public-domain artworks that have images 1. Call `GET https://data.rijksmuseum.nl/search/collection?type=painting&imageAvailable=true` - Network accessline 25
1. Call `GET https://data.rijksmuseum.nl/search/collection?type=painting&imageAvailable=true` - Network accessline 27
- Other useful params: `creator`, `creationDate` (wildcards `*`/`?`), `description`, `material`, `technique`, `title`, `type`, `objectNumber`, `memberOfSetId`, `aboutActor`. Repeat a param to OR multiple values (e.g. two `material=`). - - Network accessline 29
- Response is a Linked-Art `OrderedCollectionPage`: `orderedItems: [{id: "https://id.rijksmuseum.nl/{objectId}", type: "HumanMadeObject"}, ...]`, plus `partOf.totalItems` and a `next.id` URL (contains an opaque `pageToken`) for pagination — - Network accessline 31
2. Pick an object id from `orderedItems`, e.g. `https://id.rijksmuseum.nl/200105887`. - Network accessline 33
### (b) Get a full-resolution downloadable image URL from a result 3. `GET https://id.rijksmuseum.nl/{objectId}` (Accept: application/json) → JSON-LD `HumanMadeObject` record. Find the `shows` array → `{id: "https://id.rijksmuseum.nl/{visua - Network accessline 34
3. `GET https://id.rijksmuseum.nl/{objectId}` (Accept: application/json) → JSON-LD `HumanMadeObject` record. Find the `shows` array → `{id: "https://id.rijksmuseum.nl/{visualItemId}", type: "VisualItem"}`. - Network accessline 35
4. `GET https://id.rijksmuseum.nl/{visualItemId}` → `VisualItem` record. Check: - Network accessline 36
- `subject_to[].classified_as[].id` for the rights statement (look for `creativecommons.org/publicdomain/...`). - `digitally_shown_by` → `{id: "https://id.rijksmuseum.nl/{digitalObjectId}", type: "DigitalObject"}`. 5. `GET https://id.rij - Network accessline 37
- `digitally_shown_by` → `{id: "https://id.rijksmuseum.nl/{digitalObjectId}", type: "DigitalObject"}`. - Network accessline 38
5. `GET https://id.rijksmuseum.nl/{digitalObjectId}` → `DigitalObject` record. Its `access_point[0].id` is the **ready-to-use full-resolution IIIF image URL**, already in the form `https://iiif.micr.io/{imageId}/full/max/0/default.jpg` — no - Network accessline 40
### IIIF sizing (if you want other resolutions/crops) Template: `https://iiif.micr.io/{imageId}/{region}/{size}/{rotation}/{quality}.{format}` - Network accessline 41
Template: `https://iiif.micr.io/{imageId}/{region}/{size}/{rotation}/{quality}.{format}` - Network accessline 53
https://data.rijksmuseum.nl/search/collection?type=painting&imageAvailable=true - Network accessline 55
Verified via `curl` → HTTP 200, valid JSON, `totalItems: 4916`, first result `https://id.rijksmuseum.nl/200100988`. - Network accessline 57
**Object chain used for the image example:** - Object: `https://id.rijksmuseum.nl/200105887` → "Cat at Play" (Katjesspel), Henriëtte Ronner-Knip, c.1860-1878, object number SK-A-3089 - Network accessline 58
- Object: `https://id.rijksmuseum.nl/200105887` → "Cat at Play" (Katjesspel), Henriëtte Ronner-Knip, c.1860-1878, object number SK-A-3089 - Network accessline 59
- VisualItem: `https://id.rijksmuseum.nl/202105887` → rights = Public Domain / CC0 - Network accessline 60
- DigitalObject: `https://id.rijksmuseum.nl/5001087555671055286110` → `access_point[0].id` - Network accessline 64
https://iiif.micr.io/YAxov/full/max/0/default.jpg - Network accessline 66
Verified via `curl -I` and full download: - Network accessline 82
## Sources - https://data.rijksmuseum.nl/docs/ (API overview) - Network accessline 83
- https://data.rijksmuseum.nl/docs/ (API overview) - Network accessline 84
- https://data.rijksmuseum.nl/docs/search (Search API reference) - Network accessline 85
- https://data.rijksmuseum.nl/docs/api/collection (old Collection API — marked DEPRECATED) - Network accessline 86
- https://data.rijksmuseum.nl/docs/iiif/ , /docs/iiif/image , /docs/iiif/presentation (IIIF docs) - https://data.rijksmuseum.nl/policy/ (licensing/rights policy) - Live verified via `curl`: `data.rijksmuseum.nl/search/collection`, `id.rijks - Network accessline 87
- https://data.rijksmuseum.nl/policy/ (licensing/rights policy)
skills/museum-art/references/smithsonian.md
- Network accessline 8
https://api.si.edu/openaccess/api/v1.0/ - Access to secretsline 21
[Credential-related source omitted] - Network accessline 23
- Free signup: https://api.data.gov/signup/ (name + email, key emailed immediately — standard api.data.gov self-serve flow, no approval wait, no cost). - For quick testing without signing up, the shared `DEMO_KEY` works (used below) but is - Network accessline 42
GET https://api.si.edu/openaccess/api/v1.0/search - Access to secretsline 46
[Credential-related source omitted] - Network accessline 58
- `media.usage.access` — CC0 check (per above) - `media.content` — an IDS delivery-service URL, e.g. `https://ids.si.edu/ids/deliveryService?id=<idsId>` — **calling this with no size param returns the full-resolution original** (verified: 1 - Network accessline 59
- `media.content` — an IDS delivery-service URL, e.g. `https://ids.si.edu/ids/deliveryService?id=<idsId>` — **calling this with no size param returns the full-resolution original** (verified: 1.8MB JPEG). - Network accessline 68
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Network accessline 70
Verified response: HTTP 200, `rowCount: 12999`, returned real SAAM artwork records (e.g. "A Chiefe Herowan," object id `saam_1985.66.403_410`, record link https://americanart.si.edu/collections/search/artwork/?id=18695) each carrying `metad - Network accessline 74
https://ids.si.edu/ids/download?id=NMNH-vol.090_449776-449800.jpg - Network accessline 76
Verified: `curl -sL` → HTTP 200, `image/jpeg`, 23,659,074 bytes, resolves (307 redirect) to `https://smithsonian-open-access.s3-us-west-2.amazonaws.com/media/nmnh/NMNH-vol.090_449776-449800.jpg`. `usage.access: "CC0"` on the media object. - Network accessline 80
https://ids.si.edu/ids/deliveryService?id=SAAM-1985.66.403410_1 - Network accessline 87
- **`online_media_type:Images` also isn't a guaranteed hard filter on every unit.** Several paintings-category test queries returned zero records with a populated `online_media` object despite the filter term, while bird/NMNH and SAAM units - Network accessline 88
- **Rate limits are api.data.gov's, not Smithsonian's**: DEMO_KEY = 30 req/hr/IP (easy to exhaust in a scripting loop — get a real key for anything beyond a handful of test calls). - **IDS delivery URLs sometimes 307-redirect to S3** (`smit - Network accessline 89
- **IDS delivery URLs sometimes 307-redirect to S3** (`smithsonian-open-access.s3-us-west-2.amazonaws.com`) — follow redirects (`curl -L`) or your HTTP client's default redirect-follow. - Network accessline 90
- **Attribution not legally required** under CC0, but Smithsonian's own guidance asks for a credit line where practical (e.g. "Smithsonian American Art Museum"). - **Two rights fields to reconcile**: record-level `metadata_usage.access` and - Network accessline 92
- Full docs referenced (not independently fetchable due to JS-rendered pages, but corroborated via GitHub/Postman/search): https://www.si.edu/openaccess/devfaq, https://edan.si.edu/openaccess/docs/, https://github.com/Smithsonian/OpenAccess - Network accessline 94
## Sources - https://www.si.edu/openaccess/faq - Network accessline 95
- https://www.si.edu/openaccess/faq - Network accessline 96
- https://edan.si.edu/openaccess/docs/ - Network accessline 97
- https://github.com/Smithsonian/OpenAccess - Network accessline 98
- https://github.com/Smithsonian/smithsonian-openaccess (Python client) - https://api.data.gov/signup/ - Live API responses captured via curl, 2026-07-24 (this session) - Network accessline 99
- https://api.data.gov/signup/
skills/museum-art/references/smk.md
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Status: **VERIFIED working** (2026-07-24, live curl + WebFetch checks against the real API). - Network accessline 8
https://api.smk.dk/api/v1 - Network accessline 11
Official docs (Swagger UI, embedded OpenAPI 3.0.3 spec — page itself is JS-rendered but the spec JSON is inlined in `swagger-ui-init.js`): - Human docs: https://api.smk.dk/api/v1/docs/ - Network accessline 12
spec JSON is inlined in `swagger-ui-init.js`): - Human docs: https://api.smk.dk/api/v1/docs/ - Article: https://www.smk.dk/en/article/smk-api/ - Network accessline 13
- Human docs: https://api.smk.dk/api/v1/docs/ - Network accessline 14
- Article: https://www.smk.dk/en/article/smk-api/ - Network accessline 28
"rights": "https://creativecommons.org/publicdomain/mark/1.0/" - Network accessline 31
i.e. **Public Domain Mark 1.0** (SMK calls it "Public Domain" on their license page, https://www.smk.dk/en/license/public-domain/ — not literally the CC0 waiver, but functionally - Network accessline 32
https://www.smk.dk/en/license/public-domain/ — not literally the CC0 waiver, but functionally - Network accessline 58
GET https://api.smk.dk/api/v1/art/search - Network accessline 68
https://api.smk.dk/api/v1/art/search?keys=*&filters=%5Bpublic_domain:true%5D&filters=%5Bhas_image:true%5D&offset=0&rows=10 - Network accessline 96
"object_number": "KKS5261", "public_domain": true, "rights": "https://creativecommons.org/publicdomain/mark/1.0/", - Network accessline 97
"public_domain": true, "rights": "https://creativecommons.org/publicdomain/mark/1.0/", "image_width": 4992, - Network accessline 98
"rights": "https://creativecommons.org/publicdomain/mark/1.0/", - Network accessline 100
"image_height": 6287, "image_size": 32476791, "image_thumbnail": "https://iip-thumb.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2/full/!1024,/0/default.jpg", - Network accessline 101
"image_size": 32476791, "image_thumbnail": "https://iip-thumb.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2/full/!1024,/0/default.jpg", "image_native": "https://api.smk.dk/api/v1/download/W3siaW1nX3VybCI6Imh0dHBzOi8vaWlwLnNtay5kay9paWlmL2pw - Network accessline 102
"image_thumbnail": "https://iip-thumb.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2/full/!1024,/0/default.jpg", "image_native": "https://api.smk.dk/api/v1/download/W3siaW1nX3VybCI6Imh0dHBzOi8vaWlwLnNtay5kay9paWlmL2pwMi9xejIwc3g3NzFfa2tzNTI2MS - Network accessline 103
"image_native": "https://api.smk.dk/api/v1/download/W3siaW1nX3VybCI6Imh0dHBzOi8vaWlwLnNtay5kay9paWlmL2pwMi9xejIwc3g3NzFfa2tzNTI2MS50aWYuanAyL2Z1bGwvZnVsbC8wL25hdGl2ZS5qcGciLCJwdWJsaWNfZG9tYWluIjp0cnVlLCJvYmplY3RfbnVtYmVyIjoiS0tTNTI2MSIsIm51 - Network accessline 104
"image_iiif_id": "https://iip.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2", - Network accessline 105
"image_iiif_info": "https://iip.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2/info.json" - Network accessline 111
GET https://api.smk.dk/api/v1/art?object_number=KKS5261 - Network accessline 119
https://api.smk.dk/api/v1/art/search?keys=*&filters=%5Bpublic_domain:true%5D&filters=%5Bhas_image:true%5D&offset=0&rows=2 - Network accessline 125
https://api.smk.dk/api/v1/download/W3siaW1nX3VybCI6Imh0dHBzOi8vaWlwLnNtay5kay9paWlmL2pwMi9xejIwc3g3NzFfa2tzNTI2MS50aWYuanAyL2Z1bGwvZnVsbC8wL25hdGl2ZS5qcGciLCJwdWJsaWNfZG9tYWluIjp0cnVlLCJvYmplY3RfbnVtYmVyIjoiS0tTNTI2MSIsIm51bSI6IiJ9XQ==/KKS5 - Network accessline 130
https://iip.smk.dk/iiif/jp2/qz20sx771_kks5261.tif.jp2/full/1024,/0/default.jpg - Network accessline 135
- `curl -sI` on `image_native` URL → `HTTP/1.1 200 OK`, `Content-Type: image/jpeg`, - Network accessline 136
`Content-Length: 24043853`, `Access-Control-Allow-Origin: *`. - `curl -s` on `art/search` example URL → HTTP 200, valid JSON, `found: 39479`, `items[0].public_domain == true`, `items[0].has_image` implied by presence of `image_native`. - Network accessline 137
- `curl -s` on `art/search` example URL → HTTP 200, valid JSON, `found: 39479`, - Network accessline 138
`items[0].public_domain == true`, `items[0].has_image` implied by presence of `image_native`. - `curl -sI` on IIIF `info.json` → HTTP 200, valid IIIF Image API 2.0 manifest with `sizes` array. - Burst of 5 sequential unauthenticated request - Network accessline 139
- `curl -sI` on IIIF `info.json` → HTTP 200, valid IIIF Image API 2.0 manifest with `sizes` array.
skills/no-ai-slop/SKILL.md
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Default target for every explanation, report, spec, chat reply, and ticket the agent writes: about 80% of the way to ASD-STE100 (Simplified Technical English, the controlled language from aerospace maintenance manuals). Source: Karpathy, 20
skills/no-ai-slop/SOURCE.md
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Source: https://github.com/petergyang/no-ai-slop (Peter Yang), MIT License.
skills/product-ui-taste/references/canonical-sources.md
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**Frame / layout** - Carbon 2x grid: https://carbondesignsystem.com/elements/2x-grid/usage/ - Network accessline 10
- Carbon 2x grid: https://carbondesignsystem.com/elements/2x-grid/usage/ - Network accessline 11
- Primer PageLayout: https://primer.style/product/components/page-layout - Network accessline 12
- Material 3 canonical layouts: https://m3.material.io/foundations/adaptive-design/canonical-layouts - Network accessline 13
- Material 3 list-detail: https://m3.material.io/foundations/layout/canonical-examples/list-detail - Network accessline 14
- Material 3 supporting-pane: https://m3.material.io/foundations/layout/canonical-examples/supporting-pane - Network accessline 15
- Container queries: https://www.joshwcomeau.com/css/container-queries-unleashed/ - Network accessline 17
**Overflow / truncation** - Carbon overflow content: https://carbondesignsystem.com/patterns/overflow-content/ - Network accessline 18
- Carbon overflow content: https://carbondesignsystem.com/patterns/overflow-content/ - Network accessline 19
- Primer Truncate: https://primer.style/product/components/truncate/ - Network accessline 20
- CSS line-clamp: https://css-tricks.com/line-clampin/ - Network accessline 22
**State matrix** - Polaris EmptyState: https://polaris-react.shopify.com/components/layout-and-structure/empty-state - Network accessline 23
- Polaris EmptyState: https://polaris-react.shopify.com/components/layout-and-structure/empty-state - Network accessline 24
- Carbon empty states: https://carbondesignsystem.com/patterns/empty-states-pattern/ - Network accessline 25
- Carbon loading: https://carbondesignsystem.com/patterns/loading-pattern/ - Network accessline 26
- Primer empty states: https://primer.style/product/ui-patterns/empty-states/ - Network accessline 27
- NN/g skeleton screens: https://www.nngroup.com/articles/skeleton-screens/ - Network accessline 28
- NN/g empty-state design: https://www.nngroup.com/articles/empty-state-interface-design/ - Network accessline 29
- Pencil&Paper empty states: https://www.pencilandpaper.io/articles/empty-states - Network accessline 30
- Polaris error messages: https://polaris-react.shopify.com/content/error-messages - Network accessline 32
**Data table** - Carbon data table: https://carbondesignsystem.com/components/data-table/usage/ - Network accessline 33
- Carbon data table: https://carbondesignsystem.com/components/data-table/usage/ - Network accessline 34
- Polaris IndexTable: https://polaris-react.shopify.com/components/tables/index-table - Network accessline 35
- Ant table scroll: https://ant.design/docs/blog/table-scroll-ghost/ - Network accessline 36
- Ant virtual table: https://ant.design/docs/blog/virtual-table/ - Network accessline 37
- TanStack column sizing: https://tanstack.com/table/latest/docs/guide/column-sizing - Network accessline 38
- Mantine React Table column pinning: https://www.mantine-react-table.com/docs/guides/column-pinning - Network accessline 39
- NN/g infinite scroll: https://www.nngroup.com/articles/infinite-scrolling-tips/ - Network accessline 40
- Baymard checkout linear / pagination context: https://baymard.com/blog/checkout-process-should-be-linear - Network accessline 41
- Pagination vs infinite vs load-more: https://www.smashingmagazine.com/2016/03/pagination-infinite-scrolling-load-more-buttons/ - Network accessline 43
**Forms / flows** - LukeW web form design: https://www.lukew.com/ff/entry.asp?504 - Network accessline 44
- LukeW web form design: https://www.lukew.com/ff/entry.asp?504 - Network accessline 45
- NN/g web form design: https://www.nngroup.com/articles/web-form-design/ - Network accessline 46
- NN/g form errors: https://www.nngroup.com/articles/errors-forms-design-guidelines/ - Network accessline 47
- Polaris inline error: https://polaris-react.shopify.com/components/selection-and-input/inline-error - Network accessline 48
- Ant / 2x form pattern: https://2x.ant.design/docs/pattern/form - Network accessline 49
- Mantine form validation: https://mantine.dev/form/validation/ - Network accessline 51
**Overlays / layering / a11y** - Radix Dialog: https://www.radix-ui.com/primitives/docs/components/dialog - Network accessline 52
- Radix Dialog: https://www.radix-ui.com/primitives/docs/components/dialog - Network accessline 53
- Radix Toast: https://www.radix-ui.com/primitives/docs/components/toast - Network accessline 54
- W3C ARIA alertdialog: https://www.w3.org/WAI/ARIA/apg/patterns/alertdialog/ - Network accessline 55
- W3C ARIA dialog-modal: https://www.w3.org/WAI/ARIA/apg/patterns/dialog-modal/ - Network accessline 56
- W3C ARIA grid data-grids: https://www.w3.org/WAI/ARIA/apg/patterns/grid/examples/data-grids/ - Network accessline 57
- MDN stacking context: https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Positioned_layout/Stacking_context - Network accessline 59
**Tokens / density / i18n** - Atlaskit design tokens: https://atlassian.design/foundations/design-tokens - Network accessline 60
- Atlaskit design tokens: https://atlassian.design/foundations/design-tokens - Network accessline 61
- Atlaskit spacing: https://atlassian.design/foundations/spacing - Network accessline 62
- Crowdin UI localization: https://crowdin.com/blog/best-practices-for-ui-localization - Network accessline 63
- Material 3 accessibility (targets): https://m3.material.io/components/buttons/accessibility - Network accessline 65
**Modern minimal** - Linear method: https://linear.app/method/introduction - Network accessline 66
- Linear method: https://linear.app/method/introduction - Network accessline 67
- Vercel design guidelines: https://vercel.com/design/guidelines
skills/product-ui-taste/references/house-ui-rules.md
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- State each count once. - Render Markdown drafts exactly as the destination renders them (GitHub, Jira, Slack). Screenshot the posted result and judge readability. - Every reference a person reads carries a short title with the link on it: - Network accessline 40
- Every reference a person reads carries a short title with the link on it: [Queued checks show at once (#30)](https://github.com/your-org/your-repo/pull/30). In the UI, show a chip as described under External objects.
skills/release-comms/SKILL.md
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2. **GitHub milestone.** The milestone filtered by `TEAM_LABEL` is the release manifest. Issues may still be open, so closed does not equal shipped. 3. **Issue bodies and comments.** This is the media source. Dev-test comments ("Dev test: p - Network accessline 36
3. **Issue bodies and comments.** This is the media source. Dev-test comments ("Dev test: passed on prod") and QA comments ("Verified PASSED on staging") carry screenshots and demo videos as GitHub attachment URLs. For a private repo these - Network accessline 37
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Network accessline 38
Check the Content-Type with `curl -sI` for the right extension. GitHub videos are usually QuickTime `.mov`. Treat a comment-level "Verified PASSED" as the ship signal, because issues often stay open until a later close step.
skills/release-video/SKILL.md
- Network accessline 15
This skill makes motion scenes and composes demos from recordings. It does not cut raw footage of people talking. For narrated takes, founder voice-overs or interviews, use [browser-use/video-use](https://github.com/browser-use/video-use), - Access to secretsline 19
[Credential-related source omitted]
skills/release-video/scripts/compose_demo.py
skills/release-video/scripts/engine.js
- Executable scriptline 1
Executable file
skills/release-video/scripts/frames_from_video.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/release-video/scripts/gen_audio.sh
- Executable scriptline 1
#!/usr/bin/env bash - Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 6
[Credential-related source omitted] - Network accessline 9
out="${5:-sfx}"; mkdir -p "$out" body=$(python3 -c 'import json,sys; print(json.dumps({"text": sys.argv[1], "duration_seconds": float(sys.argv[2]), "prompt_influence": 0.6}))' "$3" "$4") curl -sf -X POST "https://api.elevenlabs.io/v - Network accessline 10
[Credential-related source omitted] - Network accessline 11
[Credential-related source omitted] - Access to secretsline 11
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Network accessline 14
[Credential-related source omitted] - Network accessline 15
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted]
skills/release-video/scripts/human_input.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/release-video/scripts/mix_final.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/release-video/scripts/mix_sfx.py
skills/release-video/scripts/music_check.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/release-video/scripts/plain_demo.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/release-video/scripts/render.py
skills/slop-gate/scripts/model_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/slop-gate/scripts/scan.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/social-media-kit/scripts/cardrender.py
skills/social-media-kit/scripts/linearcard.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 210
[Credential-related source omitted] - Access to secretsline 249
[Credential-related source omitted]
skills/social-media-kit/scripts/terminal_banner.py
skills/taste-skill/SKILL.md
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1. **Image-generation tool first.** If ANY image-gen tool is available in the environment (`generate_image`, MCP image tool, IDE-integrated gen, OpenAI image tools, etc.) you MUST use it to create section-specific assets: hero photography, - Network accessline 268
2. **Real web images second.** When no gen tool is available, use real photography sources. Acceptable defaults: * `https://picsum.photos/seed/{descriptive-seed}/{w}/{h}` for placeholder photography (seed should describe the section, e.g - Network accessline 269
* `https://picsum.photos/seed/{descriptive-seed}/{w}/{h}` for placeholder photography (seed should describe the section, e.g. `marrow-cookware-kitchen`) - Network accessline 276
**Real company logos for social proof.** When the brief calls for a "Trusted by / Used by / Customers" logo wall, do NOT default to plain text wordmarks (`<span>Acme Co</span>` styled in a row). Use real SVG logos: * **Source: Simple Icons* - Network accessline 277
* **Source: Simple Icons** (`https://cdn.simpleicons.org/{slug}/ffffff` for any color, or `simple-icons` npm package). Covers most known brands. - Network accessline 500
* **Hand-rolled decorative SVGs strongly discouraged** as default (see Section 4.8). * **NO div-based fake screenshots.** Never build a fake product UI out of `<div>` rectangles to simulate a screenshot. Use real images, generated images, o - Network accessline 501
* **NO div-based fake screenshots.** Never build a fake product UI out of `<div>` rectangles to simulate a screenshot. Use real images, generated images, or skip the preview. * **NO broken Unsplash links.** Use `https://picsum.photos/seed/{ - Network accessline 502
* **NO broken Unsplash links.** Use `https://picsum.photos/seed/{descriptive-string}/{w}/{h}`, or generated photo placeholders, or actual assets.
skills/taste-skill/references/canonical-sources.md
- Network accessline 7
### Material Web - https://github.com/material-components/material-web - Network accessline 8
- https://github.com/material-components/material-web - Network accessline 9
- https://material-web.dev/theming/material-theming/ - Network accessline 10
- https://m3.material.io/develop/web - Network accessline 12
### Fluent UI - https://fluent2.microsoft.design/get-started/develop - Network accessline 13
- https://fluent2.microsoft.design/get-started/develop - Network accessline 14
- https://fluent2.microsoft.design/components/web/react/ - Network accessline 15
- https://github.com/microsoft/fluentui - Network accessline 16
- https://learn.microsoft.com/en-us/fluent-ui/web-components/ - Network accessline 18
### Carbon - https://carbondesignsystem.com/ - Network accessline 19
- https://carbondesignsystem.com/ - Network accessline 20
- https://github.com/carbon-design-system/carbon - Network accessline 21
- https://carbondesignsystem.com/developing/react-tutorial/overview/ - Network accessline 22
- https://carbondesignsystem.com/developing/web-components-tutorial/overview/ - Network accessline 24
### Shopify Polaris - https://shopify.dev/docs/api/app-home/web-components - Network accessline 25
- https://shopify.dev/docs/api/app-home/web-components - Network accessline 26
- https://github.com/Shopify/polaris-react - Network accessline 27
- https://polaris-react.shopify.com/components - Network accessline 29
### Atlassian - https://atlassian.design/get-started/develop - Network accessline 30
- https://atlassian.design/get-started/develop - Network accessline 31
- https://atlassian.design/components/button/examples - Network accessline 32
- https://atlaskit.atlassian.com/packages/design-system/button/example/disabled - Network accessline 33
- https://atlassian.design/tokens/design-tokens - Network accessline 35
### Primer - https://primer.style/ - Network accessline 36
- https://primer.style/ - Network accessline 37
- https://github.com/primer/css - Network accessline 38
- https://github.com/primer/brand - Network accessline 40
### GOV.UK - https://design-system.service.gov.uk/components/button/ - Network accessline 41
- https://design-system.service.gov.uk/components/button/ - Network accessline 42
- https://design-system.service.gov.uk/styles/layout/ - Network accessline 43
- https://github.com/alphagov/govuk-frontend - Network accessline 45
### USWDS - https://designsystem.digital.gov/documentation/developers/ - Network accessline 46
- https://designsystem.digital.gov/documentation/developers/ - Network accessline 47
- https://designsystem.digital.gov/components/button/ - Network accessline 48
- https://designsystem.digital.gov/components/card/ - Network accessline 49
- https://github.com/uswds/uswds - Network accessline 51
### Bootstrap - https://getbootstrap.com/docs/5.3/layout/grid/ - Network accessline 52
- https://getbootstrap.com/docs/5.3/layout/grid/ - Network accessline 53
- https://getbootstrap.com/docs/5.3/components/card/ - Network accessline 55
### Tailwind - https://tailwindcss.com/docs/dark-mode - Network accessline 56
- https://tailwindcss.com/docs/dark-mode - Network accessline 57
- https://tailwindcss.com/blog/tailwindcss-v4 - Network accessline 59
### Radix - https://www.radix-ui.com/themes/docs/components/theme - Network accessline 60
- https://www.radix-ui.com/themes/docs/components/theme - Network accessline 61
- https://www.radix-ui.com/themes/docs/components/card - Network accessline 62
- https://github.com/radix-ui/themes - Network accessline 64
### shadcn/ui - https://ui.shadcn.com/docs - Network accessline 65
- https://ui.shadcn.com/docs - Network accessline 66
- https://ui.shadcn.com/docs/components/card - Network accessline 67
- https://github.com/shadcn-ui/ui - Network accessline 69
### Native CSS / W3C standards - https://developer.mozilla.org/en-US/docs/Web/CSS/Reference/Properties/backdrop-filter - Network accessline 70
- https://developer.mozilla.org/en-US/docs/Web/CSS/Reference/Properties/backdrop-filter - Network accessline 71
- https://developer.mozilla.org/en-US/docs/Web/CSS/Reference/At-rules/@media/prefers-color-scheme - Network accessline 72
- https://developer.mozilla.org/en-US/docs/Web/CSS/Reference/At-rules/@media/prefers-reduced-motion - Network accessline 73
- https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Grid_layout - Network accessline 74
- https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Scroll-driven_animations - Network accessline 75
- https://drafts.csswg.org/scroll-animations-1/ - Network accessline 77
### Apple Liquid Glass (Apple platforms only) - https://developer.apple.com/design/human-interface-guidelines/materials - Network accessline 78
- https://developer.apple.com/design/human-interface-guidelines/materials - Network accessline 79
- https://developer.apple.com/documentation/TechnologyOverviews/liquid-glass - Network accessline 80
- https://developer.apple.com/documentation/TechnologyOverviews/adopting-liquid-glass - Network accessline 81
- https://developer.apple.com/documentation/SwiftUI/Material
skills/taste-skill/references/design-systems-install.md
- Network accessline 46
# Add this to your app HTML head: # <meta name="shopify-api-key" content="%SHOPIFY_API_KEY%" /> # <script src="https://cdn.shopify.com/shopifycloud/polaris.js"></script> - Access to secretsline 47
[Credential-related source omitted] - Network accessline 48
# <script src="https://cdn.shopify.com/shopifycloud/polaris.js"></script>
skills/team-brief/SKILL.md
- Network accessline 163
2. For each matched Linear issue: - Use `mcp__claude_ai_Linear__create_attachment` to attach the PR URL to the issue (if not already linked). Use format: - url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - Network accessline 164
- Use `mcp__claude_ai_Linear__create_attachment` to attach the PR URL to the issue (if not already linked). Use format: - url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - title: "PR #XXX: [PR title]" - Network accessline 165
- url: `https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX` - Access to secretsline 227
[Credential-related source omitted] - Network accessline 229
**API Details:** - Base URL: `https://api.hackmd.io/v1` - Network accessline 230
- Base URL: `https://api.hackmd.io/v1` - Network accessline 235
1. **Create the note** via `POST https://api.hackmd.io/v1/notes` with: - Network accessline 248
2. **Update the note content** via `PATCH https://api.hackmd.io/v1/notes/<id>` with the full brief markdown content: - Network accessline 249
- Read the saved brief from `[CUSTOMIZE: path/to/briefs/]daily-brief-YYYY-MM-DD.md` - Use Python to JSON-encode the content and write to a temp file - Send via `curl -X PATCH ... -d @/tmp/hackmd-payload.json` - Network accessline 251
- Send via `curl -X PATCH ... -d @/tmp/hackmd-payload.json` - Network accessline 253
3. **Store the HackMD link** for use in the Slack message AND the local brief file: - The `publishLink` from step 1 (format: `https://hackmd.io/@[CUSTOMIZE: your-hackmd-username]/<shortId>`) - Network accessline 254
- The `publishLink` from step 1 (format: `https://hackmd.io/@[CUSTOMIZE: your-hackmd-username]/<shortId>`) - Network accessline 286
- If there's nothing urgent, keep it short: "Good day, we shipped X, nothing on fire." - Reference specific people only when it's constructive (celebrating a win or when they're the clear owner of a follow-up). Don't publicly call people - Network accessline 287
- Reference specific people only when it's constructive (celebrating a win or when they're the clear owner of a follow-up). Don't publicly call people out for missing deadlines. - **ALWAYS embed GitHub links for PR references.** Use Slac - Network accessline 288
- **ALWAYS embed GitHub links for PR references.** Use Slack link format: `<https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX>`. Never use bare `#930` — make it clickable.
skills/team-brief/references/publish-templates.md
- Network accessline 7
# Step 1: Create note curl -s -X POST 'https://api.hackmd.io/v1/notes' \ - Network accessline 8
[Credential-related source omitted] - Access to secretsline 8
[Credential-related source omitted] - Access to secretsline 9
[Credential-related source omitted] - Network accessline 19
json.dump({'content': content}, f) " curl -s -X PATCH "https://api.hackmd.io/v1/notes/<id>" \ - Network accessline 20
[Credential-related source omitted] - Network accessline 21
[Credential-related source omitted] - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted] - Network accessline 33
*What shipped:* • [Feature/PR 1 with <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> link] — [1-line description] - Network accessline 34
• [Feature/PR 1 with <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> link] — [1-line description] - Network accessline 63
:zap: *Quick wins:* • <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> and <...|#YYY> are approved — just need someone to merge. - Network accessline 64
• <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> and <...|#YYY> are approved — just need someone to merge. - Network accessline 73
*What shipped:* [X] PRs merged. :rocket: • [Feature/PR 1 with <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> link] — [1-line description] - Network accessline 74
• [Feature/PR 1 with <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> link] — [1-line description] - Network accessline 95
:zap: *Quick wins:* • <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> and <...|#YYY> are approved — just need someone to merge. - Network accessline 96
• <https://github.com/[CUSTOMIZE: your-org/your-repo]/pull/XXX|#XXX> and <...|#YYY> are approved — just need someone to merge.
skills/ultragoal/SKILL.md
- Network accessline 69
│ (always run for ultragoals — cross-phase regression is the main risk) ▼ CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return - Network accessline 70
▼ CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return │ EVIDENCE AC-n | CP-5 | PASS | <observation> | <artifact> - Network accessline 71
CP-5 ACCEPT observe the artifact (curl / screenshot / re-fetch), not the tool return
skills/update-cog/SKILL.md
- Network accessline 25
# Add the upstream remote if not already present git remote get-url cog-upstream 2>/dev/null || \ git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git - Network accessline 26
git remote get-url cog-upstream 2>/dev/null || git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git - Network accessline 27
git remote add cog-upstream https://github.com/huytieu/COG-second-brain.git
skills/url-dump/SKILL.md
- Network accessline 351
# CORRECT type: "url-bookmark" url: "https://example.com/path?query=value" - Network accessline 352
type: "url-bookmark" url: "https://example.com/path?query=value" tags: ["#bookmark", "#article", "#ai"] - Network accessline 353
url: "https://example.com/path?query=value" - Network accessline 358
# INCORRECT type: url-bookmark url: https://example.com/path?query=value - Network accessline 359
type: url-bookmark url: https://example.com/path?query=value tags: [#bookmark, #article, #ai] - Network accessline 360
url: https://example.com/path?query=value
skills/vector-cover/render.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/vector-cover/template.html
- Network accessline 21
<svg width="1200" height="480" viewBox="0 0 1200 480" xmlns="http://www.w3.org/2000/svg">
skills/voice-baseline/scripts/census.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/youtube-video-kit/SKILL.md
skills/youtube-video-kit/scripts/ytopen.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 132
[Credential-related source omitted]
skills/youtube-video-kit/scripts/ytthumb.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 22
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted]
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.
No combined signals at this commit.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/plugin.json | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Native | Path .cursorrules | .cursor/skills/<skill-name>/ |
| Gemini CLI | Native | Path .gemini/commands/braindump.toml | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/auto-research/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/auto-research/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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