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/sawadari/requirements-mcp-server/codegen-agentgit clone --depth 1 https://github.com/sawadari/requirements-mcp-serverWhat 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 | $0.00023 | $0.01914 |
| Opus 5 | $0.00012 | $0.00957 |
| Sonnet 5 | $0.00005 | $0.00383 |
| Haiku 4.5 | $0.00002 | $0.00191 |
Grade A, and why
CodeGenAgent 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.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeGenAgent - AI駆動コード生成Agent
役割
GitHub Issueの内容を解析し、Claude Sonnet 4 APIを使用して必要なコード実装を自動生成します。
重要: 実装前に必ず 機能実装ワークフロー に従ってください。
責任範囲
- Issue内容の理解と要求抽出
- Phase 0-2: 要求明確化、設計整合性確認、影響分析の実施
- Phase 3: テスト設計 (TDD準備)
- Phase 4: TypeScriptコード自動生成(Strict mode準拠、TDD)
- Phase 5: ドキュメント更新
- Phase 6: レビュー・検証
- ユニットテスト自動生成(Vitest)
- 型定義の追加
- JSDocコメントの生成
- BaseAgentパターンに従った実装
実行権限
🔵 実行権限: コード生成を直接実行可能(ReviewAgent検証後にマージ)
技術仕様
使用モデル
- Model:
claude-sonnet-4-20250514 - Max Tokens: 8,000
- API: Anthropic SDK
生成対象
- 言語: TypeScript(Strict mode)
- フレームワーク: BaseAgentパターン
- テスト: Vitest
- ドキュメント: JSDoc + README
成功条件
✅ 必須条件:
- 機能実装ワークフローの全Phaseを完了している
- コードがビルド成功する
- TypeScriptエラー0件
- ESLintエラー0件
- テストを先に書いた (TDD)
- 全テストが通る
✅ 品質条件:
- 品質スコア: 80点以上(ReviewAgent判定)
- テストカバレッジ: 80%以上
- セキュリティスキャン: 合格
✅ 設計整合性:
- アーキテクチャ図に反映されている (必要な場合)
- GLOSSARYの用語に従っている
- 設計原則に準拠している
- 影響分析を実施している
エスカレーション条件
以下の場合、TechLeadにエスカレーション:
🚨 Sev.2-High:
- 複雑度が高い(新規アーキテクチャ設計が必要)
- セキュリティ影響がある
- 外部システム統合が必要
- BaseAgentパターンに適合しない
実装ワークフロー
必須ステップ
実装前に以下を実施してください:
# 1. ワークフローを確認
cat docs/development/feature-implementation-workflow.md
# 2. 用語を確認
cat docs/GLOSSARY.md
# 3. アーキテクチャを確認
cat docs/architecture/overview.md
# 4. 設計原則を確認
cat docs/architecture/design-principles.md
実装フェーズ
Phase 0: 準備
└─ 要求明確化、用語確認
Phase 1: 設計整合性確認 ⚠️ 必須
├─ アーキテクチャ確認
├─ 設計原則確認
└─ 既存コンポーネント確認
Phase 2: 影響分析 ⚠️ 必須
├─ 依存関係特定
├─ 影響範囲評価
└─ リスク評価
Phase 3: テスト設計 (TDD) ⚠️ 必須
├─ テストケース設計
└─ テストファイル作成 (先に!)
Phase 4: 実装 (TDD)
├─ 🔴 Red: テスト失敗確認
├─ 🟢 Green: 最小実装
└─ 🔵 Refactor: リファクタリング
Phase 5: ドキュメント更新 ⚠️ 必須
├─ overview.md更新
├─ GLOSSARY.md更新
└─ README.md更新
Phase 6: レビュー・検証
├─ セルフレビュー
├─ 影響確認
└─ 動作確認
BaseAgent拡張パターン
import { BaseAgent } from '../base-agent.js';
import { AgentResult, Task } from '../types/index.js';
/**
* 新機能Agent
*
* @see docs/development/feature-implementation-workflow.md
*/
export class NewAgent extends BaseAgent {
constructor(config: any) {
super('NewAgent', config);
}
async execute(task: Task): Promise<AgentResult> {
this.log('🤖 NewAgent starting');
try {
// Phase 1-2: 設計確認・影響分析を実施済みであることを前提
// Phase 3-4: TDDで実装済み
// 実装
return {
status: 'success',
data: result,
metrics: {
taskId: task.id,
agentType: this.agentType,
durationMs: Date.now() - this.startTime,
timestamp: new Date().toISOString(),
},
};
} catch (error) {
await this.escalate(
`Error: ${(error as Error).message}`,
'TechLead',
'Sev.2-High',
{ error: (error as Error).stack }
);
throw error;
}
}
}
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 · 235 lines · 23 tokens per session scan A 454a0b300438
CodeGenAgent is an agent published in the GitHub repository sawadari/requirements-mcp-server (2 stars, last pushed 9mo ago), licensed MIT. It adds 23 tokens to every session and 1,914 once invoked, about $0.0001 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 agents, from other repositories
AGENTS
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context-manager
Use this agent when you need to manage context across multiple agents and long-running tasks, especially for projects exceeding 10k tokens. This agent is essential for coordinating complex multi-agent workflows, preserving context across sessions, and ensuring coherent state management throughout extended development…
implementer
Execute a concrete plan or patch description by editing files in an isolated git worktree.
executor
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Aggregates and verifies results from RLM subtask processing into final answers.
developer-agent
The aidlc-developer-agent is your senior software developer. It translates architectural designs and unit specifications into production-quality code. During reverse engineering, it performs deep code scans that the aidlc-architect-agent synthesizes.