Borrowing it
Nothing to install: this file belongs to naka-koma/local-llm-mcp. 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/naka-koma/local-llm-mcp/main/.claude/skills/review/SKILL.mdgit clone --depth 1 https://github.com/naka-koma/local-llm-mcpWrote 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/naka-koma/local-llm-mcp/review)<a href="https://agentmods.dev/skills/naka-koma/local-llm-mcp/review"><img src="https://agentmods.dev/badge/skills/naka-koma/local-llm-mcp/review/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/naka-koma/local-llm-mcp/review"><img src="https://agentmods.dev/badge/skills/naka-koma/local-llm-mcp/review.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 | $0.00008 | $0.00349 |
| Opus 5.5 | $0.00003 | $0.00140 |
| Sonnet 5.5 | $0.00002 | $0.00070 |
| Haiku 4.5 | $0.00001 | $0.00035 |
Grade A, and why
review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
手順
git diffで現在の差分を確認する（$ARGUMENTSがあればそのブランチ/コミット範囲を対象とする）- 以下の観点でレビューし、指摘事項を一覧で提示する
レビュー観点
正確性
- ロジックの誤り、nullチェック漏れ
ask_local_llmのインターフェース（引数・戻り値の型）が壊れていないか
セキュリティ
- APIキーやエンドポイントURLがハードコードされていないか（環境変数経由になっているか）
requestsのタイムアウト設定が外れていないか
プロジェクト規約
- CLAUDE.mdのコーディング規約に準拠しているか
- 「薄いラッパー」という設計思想から外れていないか（LiteLLM側の機能を再実装していないか）
汎用性
- Flowrite固有の値や前提が紛れ込んでいないか（このリポジトリはどのプロジェクトからも使われる想定）
出力フォーマット
指摘ごとに以下の形式で報告する:
- [重要度: 高/中/低] ファイル名:行番号 — 指摘内容と修正案
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 · 32 lines · 8 tokens per session scan A 61d07ceb485b
review is a skill published in the GitHub repository naka-koma/local-llm-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 8 tokens to every session and 349 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
autoreview
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omh-code-review
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re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…