performance-reviewer

A performance-review workflow that looks for code and system choices that make software slower or use more memory or database resources than necessary.

In plain words
What is it for?
It helps analyze algorithms, memory use, input/output, concurrency, and performance-test results, then prioritize possible optimizations.
Why use it?
It helps identify common causes of slow applications, such as repeated calculations, unnecessary database queries, memory leaks, and lock contention.

Agent

Install

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.

agentmods
npx agentmods add agents/moco-ai/moco/performance-reviewer
Clone the repo
git clone --depth 1 https://github.com/moco-ai/moco
Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 817 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash a92e58dc4eea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

src/moco/profiles/development/agents/performance-reviewer.md · 96 lines

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
Changes

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.

  1. 2d ago First seen · 96 lines · 43 tokens per session scan A a92e58dc4eea

Subscribe to this mod's changes

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.

Related

Other agents, from other repositories

codemap

Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…

alvinunreal/oh-my-opencode-slim · 0 tokens

merge-conflict-resolve

Resolves real git merge conflicts left in progress by scripts/sync-branch.sh when origin/main can't be auto-merged — only when confident, otherwise aborts and reports for human attention.

cloudposse/atmos · 45 tokens

plan-verifier

Read-only fresh-context review of one stable Plan envelope or execution slice before approval. Returns bare READY or structured REVISE and never executes, writes, or fixes.

Nanako0129/pilotfish · 37 tokens

foreman-codex-wrapper

Codex transport wrapper for fable-foreman (v0.3). Runs the skill's fixed-argv launcher (scripts/codex-dispatch.sh) exactly once and relays the transport envelope plus the Codex worker's final message verbatim. Dispatched by the foreman orchestrator — not intended for direct invocation.

olsenbrands/fable-foreman · 74 tokens

data-engineer

ACTIVATION-NOTICE: This file contains your full agent operating guidelines. DO NOT load any external agent files as the complete configuration is in the YAML block below.

SynkraAI/aiox-core · 0 tokens

developer

Implement Idea and scoreidea in src/backlog.py, and verify them with tests/testbacklog.py. Use the formula impact 5 + strategicfit 3 - effort 2.

bonigarcia/context-engineering · 0 tokens