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Get Started Free →majiayu000/add-ollama-tool
Add Ollama MCP server so the container agent can call local models for cheaper/faster tasks like summarization, translation, or general queries.
Passed 16 of 21 test cases with the skill, and 7 of 21 without it.
and 13 more in the full report
10 fixed8 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to configure my NanoClaw git repository remote prior to fetching the integration branch. Which exact git command adds the standard upstream repository URL?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies git remote add upstream https://github.com/qwibitai/nanoclaw.git. | Missed | Passed |
The instructions your model reads when the skill loads.
This skill adds a stdio-based MCP server that exposes local Ollama models as tools for the container agent. Claude remains the orchestrator but can offload work to local models, and can optionally manage the model library directly.
Core tools (always available):
ollama_list_models — list installed Ollama models with name, size, and familyollama_generate — send a prompt to a specified model and return the responseManagement tools (opt-in via OLLAMA_ADMIN_TOOLS=true):
ollama_pull_model — pull (download) a model from the Ollama registryollama_delete_model — delete a locally installed model to free disk spaceollama_show_model — show model details: modelfile, parameters, and architecture infoollama_list_running — list models currently loaded in memory with memory usage and processor typeCheck if container/agent-runner/src/ollama-mcp-stdio.ts exists. If it does, skip to Phase 3 (Configure).
Verify Ollama is installed and running on the host:
ollama list
If Ollama is not installed, direct the user to https://ollama.com/download.
If no models are installed, suggest pulling one:
You need at least one model. I recommend:
ollama pull gemma3:1b # Small, fast (1GB) ollama pull llama3.2 # Good general purpose (2GB) ollama pull qwen3-coder:30b # Best for code tasks (18GB)
test cases passed, vs 7 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.