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 skills add s977043/river-review --skill ai-agent-review-readinessgit clone --depth 1 https://github.com/s977043/river-reviewWrote 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/s977043/river-review/ai-agent-review-readiness)<a href="https://agentmods.dev/skills/s977043/river-review/ai-agent-review-readiness"><img src="https://agentmods.dev/badge/skills/s977043/river-review/ai-agent-review-readiness/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/s977043/river-review/ai-agent-review-readiness"><img src="https://agentmods.dev/badge/skills/s977043/river-review/ai-agent-review-readiness.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 | $0.00036 | $0.01934 |
| Opus 5.5 | $0.00014 | $0.00774 |
| Sonnet 5.5 | $0.00007 | $0.00387 |
| Haiku 4.5 | $0.00004 | $0.00193 |
Grade A, and why
AI Agent Review Readiness 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 2mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pattern declaration
Primary pattern: Reviewer Secondary patterns: Gate Why: AI エージェント委譲の文脈があるドキュメントを対象に、委譲前の readiness を統合的にチェックする。Pre-execution Gate で適用対象を絞り込む。
Goal / 目的
- AI エージェントに作業を委譲する前に、チームが「何を作るか」「どう判定するか」「どこに人間が介在するか」を明示しているかをチェックする。
- 委譲前の設計不足を早期に検出し、AI が推測で実装することを防ぐ。
Non-goals / 扱わないこと
- 受入条件の Given-When-Then 形式チェック →
requirements-acceptanceの責務。 - 実装計画の危険操作・Stop-work トリガー →
plan-review-gateの責務。 - plan.md / pbi-input.md 間の整合性 →
plangate-plan-integrityの責務。 - PlanGate ファイル（plan.md / pbi-input.md）への依存は持たない。
Pre-execution Gate / 実行前ゲート
以下の条件がすべて満たされない限り NO_REVIEW を返す。
- diff に AI エージェントへの作業委譲・タスク委任の文脈が含まれている （キーワードは大文字小文字を区別しない。例: "agent", "delegate", "AI にやらせる", "Cursor", "Claude Code", "Aider", "agentic", "ai-assisted", "LLM", "copilot", "GPT", "Gemini", "自動実行", "委任", "委譲" 等が 委譲・自動実行の意図 を伴って 使われている。一般的な自動化（CI/CD 説明等）には適用しない）
- inputContext に diff が含まれている
ゲート不成立時の出力: NO_REVIEW: ai-agent-review-readiness — AI 委譲の文脈が見当たらない
False-positive guards / 抑制条件
- AI ツールの紹介・説明文書（「AI を使うと便利」という一般論のみ）では起動しない。
- 変更ログ・リリースノート（CHANGELOG.md, RELEASES.md 等）では起動しない。
- 既に 5 つの readiness 条件（基準・コンテキスト・ループ・境界・フィードバック）がすべて明示されている文書では findings を出さない。
Evidence / 根拠の取り方
- 推測ではなく、差分のテキストから直接引用して根拠にする。
- 欠落指摘（セクションが存在しない）は文書全体を確認してから行う。
- キーワード一致だけでなく、委譲意図の文脈を確認してから Gate を通過させる。
Rule / ルール
Check 1 — Review criteria before work / 作業前レビュー基準の定義
AI 委譲タスクの文書に以下が定義されていない場合に finding を出す:
- 成功基準（何ができれば完了か）
- 受入条件・非ゴール
- レビュー観点・期待する review perspective
Check 2 — Required knowledge access / 必要なコンテキストへのアクセス
エージェントが参照すべき設計コンテキストへの参照が明示されていない場合:
- アーキテクチャ規約・ADR
- API コントラクト・スキーマ
- セキュリティ要件・運用制約
判定閾値: セクション名（例: "## 前提条件"）やドキュメント種別名による参照で足りる。 ファイルパスの完全指定は必須ではない。
Check 3 — Explicit review loop / 明示的なレビューループ
実装ステップの後に自己レビュー・外部レビュー・修正のステップが明示されていない場合:
- 期待ループ: plan → execute → self-review → external review → revise
Check 4 — Human judgment boundary / 人間判断境界の明示
以下の高リスク領域で AI 出力を final として扱う場合（人間承認ステップが明示されていない）:
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 138 lines · 36 tokens per session scan A d2d97d1ede53
AI Agent Review Readiness is a skill published in the GitHub repository s977043/river-review (7 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,934 once invoked, about $0.0001 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 2026-09-03.
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