Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/forest6511/claude-code-recipes-v2/code-reviewnpx skills add forest6511/claude-code-recipes-v2 --skill code-reviewgit clone --depth 1 https://github.com/forest6511/claude-code-recipes-v2Wrote 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/forest6511/claude-code-recipes-v2/code-review)<a href="https://agentmods.dev/skills/forest6511/claude-code-recipes-v2/code-review"><img src="https://agentmods.dev/badge/skills/forest6511/claude-code-recipes-v2/code-review.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.00427 |
| Opus 5 | $0.00026 | $0.00214 |
| Sonnet 5 | $0.00010 | $0.00085 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
code-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 4d 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
以下の対象に対してコードレビューを実施してください。
対象: $ARGUMENTS（未指定の場合は git diff --staged の差分全体）
レビュー観点
- 正確性: ロジックエラー、エッジケース、null/undefined の扱い
- セキュリティ: 入力検証、認可、機密情報のハードコード、SQL/コマンドインジェクション
- パフォーマンス: N+1 クエリ、O(n²) 以上の探索、不要な再計算
- 可読性: 命名、関数長、責務分離、コメントの過不足
- テスト: 新規ロジックに対するテストの有無
重要度区分
- Critical: 本番で動作不能/セキュリティ事故レベル。マージ前の修正必須
- High: 動作はするが想定外動作やパフォーマンス劣化が出る
- Medium: 改善余地あり、別 PR に切り出してもよい
- Low: スタイル・命名の提案、任意
出力形式
重要度順に「ファイル:行番号 → 問題 → 推奨修正」の3点セットで列挙してください。 最後に Critical/High の合計件数を1行サマリで報告してください。
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.
- 4d ago First seen · 29 lines · 51 tokens per session scan A c136a7862153
code-review is a skill published in the GitHub repository forest6511/claude-code-recipes-v2 (5 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 427 once invoked, about $0.0003 per session on Opus 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
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brainstorming
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auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…