Individual skill
ollama
Use when running open-weight LLMs locally with Ollama — pulling and tagging models, calling the local API, picking a quantization or GGUF, writing Modelfiles, and sizing VRAM and RAM for the machine at hand. NOT remote or managed GPU serving and autoscaling (that is `runpod`), NOT downloading raw weights or datasets (that is `huggingface`), NOT retrieval pipeline design (that is `rag`).
skills/ollama/SKILL.md
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- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 556
- Repository last updated
- Nov 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
- cursor Repository topic
- ollama Declared skill tag
- local-llm Declared skill tag
- gguf Declared skill tag
- quantization Declared skill tag
- self-hosted-inference Declared skill tag
- design Derived category
- writing Derived category
Files and excerpt
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Read excerpt
# Ollama — run open-weight LLMs on one box
Ollama serves GGUF models from a local daemon at `http://localhost:11434`, exposing both a native
HTTP API and an OpenAI-compatible layer. Your job: reach for the right command, the right endpoint,
and the right quant **for the hardware in front of you** — and recognize when the model does not fit
and the work belongs on a remote GPU instead.
This skill owns: install/serve, pull/tag, the local API (native + OpenAI-compat), Modelfiles,
quantization choice, and VRAM/RAM sizing on a single machine.
## When to use / when not
**Use when** the model runs on *this* machine: pulling/running a model, fixing an OOM, choosing
Q4 vs Q8, authoring a Modelfile, or wiring an app to `localhost:11434`.