Borrowing it
Nothing to install: this file belongs to receptron/mulmoclaude. 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/receptron/mulmoclaude/main/.claude/skills/setup-ollama-local/SKILL.mdgit clone --depth 1 https://github.com/receptron/mulmoclaudeWrote 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/receptron/mulmoclaude/setup-ollama-local)<a href="https://agentmods.dev/skills/receptron/mulmoclaude/setup-ollama-local"><img src="https://agentmods.dev/badge/skills/receptron/mulmoclaude/setup-ollama-local/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/receptron/mulmoclaude/setup-ollama-local"><img src="https://agentmods.dev/badge/skills/receptron/mulmoclaude/setup-ollama-local.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Supply Chain · line 58 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00076 | $0.02202 |
| Opus 5.5 | $0.00030 | $0.00881 |
| Sonnet 5.5 | $0.00015 | $0.00440 |
| Haiku 4.5 | $0.00008 | $0.00220 |
Grade C, and why
setup-ollama-local scanned grade C with 2 findings 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 1mo 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
- `curl -fsSL https://claude.ai/install.sh | sh` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:11434/api/tags | head -c 200 How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Claude Code × Ollama (local LLM)
Scope / 適用範囲
This skill sets up the standalone
claudeCLI to talk to a local Ollama server. It is independent of MulmoClaude; MulmoClaude itself does not currently support Ollama (seeplans/feat-mulmoclaude-ollama-support.mdfor a tentative plan).このスキルは
claudeCLI 単体をローカルの Ollama サーバに接続するセットアップです。MulmoClaude とは独立しており、MulmoClaude 本体は現在 Ollama 接続をサポートしていません（実装案はplans/feat-mulmoclaude-ollama-support.mdを参照）。
For detailed findings and pitfalls, see docs/tips/claude-code-ollama.md (Japanese) / docs/tips/claude-code-ollama.en.md (English).
Prerequisites / 前提知識
- Ollama v0.14.0 or later is required (Anthropic Messages API compatibility was added in that version).
- Claude Code sends roughly 50,000–57,000 tokens per request, so the model needs at least a 64k context window.
- 3B-class small models effectively cannot drive Claude Code (no tool calling, broken templates).
- Even on supported models, the first turn often takes 10+ minutes on a MacBook Air; subsequent turns benefit from KV cache and drop to 1–3 minutes.
Step 1: Verify / install Ollama
1-1. Check existing install
which ollama && ollama --version
- Installed and v0.14.0+: proceed to 1-2.
- Older version:
brew upgrade ollamaand thenbrew services restart ollama(Homebrew installs). - Not installed: suggest one of:
- Official installer (recommended): https://ollama.com/download/mac
- Homebrew:
brew install ollama
1-2. Verify the server is running
curl -s http://localhost:11434/api/tags | head -c 200
- Got JSON back: server is up, go to Step 2.
- Empty / connection refused: start it.
- Official app: click the Ollama menu-bar icon.
- Homebrew:
brew services start ollamaorollama serve.
Step 2: Verify Claude Code
which claude && claude --version
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.
- 1mo ago First seen · 176 lines · 76 tokens per session scan C 683c717726b3
setup-ollama-local is a skill published in the GitHub repository receptron/mulmoclaude (386 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 2,202 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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