Borrowing it
Nothing to install: this file belongs to ShinyStar307/nano---Openclaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ShinyStar307/nano---Openclaw/main/.claude/skills/add-ollama-tool/SKILL.mdgit clone --depth 1 https://github.com/ShinyStar307/nano---OpenclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shinystar307/nano---openclaw/add-ollama-tool)<a href="https://agentmods.dev/skills/shinystar307/nano---openclaw/add-ollama-tool"><img src="https://agentmods.dev/badge/skills/shinystar307/nano---openclaw/add-ollama-tool/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shinystar307/nano---openclaw/add-ollama-tool"><img src="https://agentmods.dev/badge/skills/shinystar307/nano---openclaw/add-ollama-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01117 |
| Opus 5 | $0.00017 | $0.00558 |
| Sonnet 5 | $0.00007 | $0.00223 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
add-ollama-tool scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. Check Docker can reach the host: `docker run --rm curlimages/curl curl -s http://host.docker.internal:11434/api/tags` This is a copy
89% identical to add-ollama-tool — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Ollama Integration
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.
Tools added:
ollama_list_models— lists installed Ollama modelsollama_generate— sends a prompt to a specified model and returns the response
Phase 1: Pre-flight
Check if already applied
Check if container/agent-runner/src/ollama-mcp-stdio.ts exists. If it does, skip to Phase 3 (Configure).
Check prerequisites
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)
Phase 2: Apply Code Changes
Ensure upstream remote
git remote -v
If upstream is missing, add it:
git remote add upstream https://github.com/qwibitai/nanoclaw.git
Merge the skill branch
git fetch upstream skill/ollama-tool
git merge upstream/skill/ollama-tool
This merges in:
container/agent-runner/src/ollama-mcp-stdio.ts(Ollama MCP server)scripts/ollama-watch.sh(macOS notification watcher)- Ollama MCP config in
container/agent-runner/src/index.ts(allowedTools + mcpServers) [OLLAMA]log surfacing insrc/container-runner.tsOLLAMA_HOSTin.env.example
If the merge reports conflicts, resolve them by reading the conflicted files and understanding the intent of both sides.
Copy to per-group agent-runner
Existing groups have a cached copy of the agent-runner source. Copy the new files:
for dir in data/sessions/*/agent-runner-src; do
cp container/agent-runner/src/ollama-mcp-stdio.ts "$dir/"
cp container/agent-runner/src/index.ts "$dir/"
done
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 154 lines · 34 tokens per session scan A 6363b00da928
add-ollama-tool is a skill published in the GitHub repository ShinyStar307/nano---Openclaw (5 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,117 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 89% identical to add-ollama-tool, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.