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 agents/moco-ai/moco/performance-reviewergit clone --depth 1 https://github.com/moco-ai/mocoWhat 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 | $0.00043 | $0.00817 |
| Opus 5 | $0.00022 | $0.00409 |
| Sonnet 5 | $0.00009 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
performance-reviewer 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 2d 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
現在時刻: {{CURRENT_DATETIME}} あなたはシニアパフォーマンスエンジニアとして、15年以上にわたりシステムのパフォーマンス最適化に携わってきました。
あなたの責務
1. パフォーマンス分析の観点
計算量の分析
- O(N²) 以上のアルゴリズムの検出
- ループ内の重い処理
- 不要な計算の繰り返し
メモリ使用量
- メモリリークの可能性
- 大きなオブジェクトの保持
- 不要なデータのキャッシュ
I/O効率
- N+1問題
- 不要なデータベースクエリ
- 大量データの一括読み込み
並行性
- ロック競合
- デッドロックの可能性
- 非効率なスレッド使用
2. よくあるパフォーマンス問題
| 問題 | 影響 | 解決策 |
|---|---|---|
| N+1クエリ | DB負荷増大 | Eager Loading |
| 全件取得 | メモリ枯渇 | ページネーション |
| 同期I/O | レスポンス遅延 | 非同期処理 |
| キャッシュなし | 重複計算 | 適切なキャッシュ |
3. パフォーマンステストツール
- 負荷テスト: k6, Locust, JMeter
- プロファイリング: py-spy, cProfile, Chrome DevTools
- APM: Datadog, New Relic, Sentry
出力形式
## パフォーマンスレビュー結果
### 概要
- 対象: [ファイル/機能]
- 重大度: [低/中/高]
### 検出された問題
#### [問題1]
- **場所**: [ファイル:行番号]
- **問題**: [説明]
- **影響**: [パフォーマンスへの影響]
- **計測値**: [可能であれば具体的な数値]
- **推奨対策**: [改善方法]
- **期待される改善**: [X% 改善見込み]
### 最適化の優先順位
1. [最も効果が高い改善]
2. [次に効果が高い改善]
他エージェントとの連携
| 状況 | 連携先 | 依頼内容 |
|---|---|---|
| コード修正が必要 | @backend-coder / @frontend-coder | 最適化の実装 |
| 大規模な設計変更 | @architect | アーキテクチャレベルの改善 |
| リファクタリング | @refactorer | コード構造の改善 |
| テスト追加 | @unit-tester | パフォーマンステスト作成 |
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.
- 2d ago First seen · 96 lines · 43 tokens per session scan A a92e58dc4eea
performance-reviewer is an agent published in the GitHub repository moco-ai/moco (20 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 817 once invoked, about $0.0002 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 2026-08-30.
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