Borrowing it
Nothing to install: this file belongs to Kewton/CommandMate. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Kewton/CommandMate/main/.claude/commands/apply-review.mdgit clone --depth 1 https://github.com/Kewton/CommandMateWrote 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/commands/kewton/commandmate/apply-review)<a href="https://agentmods.dev/commands/kewton/commandmate/apply-review"><img src="https://agentmods.dev/badge/commands/kewton/commandmate/apply-review/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/commands/kewton/commandmate/apply-review"><img src="https://agentmods.dev/badge/commands/kewton/commandmate/apply-review.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 | $0.00010 | $0.01374 |
| Opus 5.5 | $0.00004 | $0.00550 |
| Sonnet 5.5 | $0.00002 | $0.00275 |
| Haiku 4.5 | $0.00001 | $0.00137 |
Grade A, and why
apply-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 1mo 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
レビュー指摘反映コマンド
概要
アーキテクチャレビューの指摘事項(Must Fix, Should Fix)を設計方針書に反映するコマンドです。
重要: このコマンドは設計方針書のみを更新します。 ソースコードの実装は行いません。レビュー結果を設計方針書に反映し、 実装時の指針として活用します。
使用方法
/apply-review [Issue番号]
/apply-review [Issue番号] --scope=must_fix
/apply-review [Issue番号] --skip="項目1,項目2"
例:
/apply-review 76 # 全指摘事項を反映
/apply-review 76 --scope=must_fix # 必須項目のみ
/apply-review 76 --skip="パフォーマンス最適化" # 特定項目をスキップ
実行内容
あなたはレビュー指摘反映の統括者です。サブエージェントを使用して指摘事項を実装に反映します。
パラメータ
- issue_number: 対象Issue番号(必須)
- scope: 反映範囲(all|must_fix|should_fix)デフォルト: all
- skip: スキップする項目(カンマ区切り)
実行フェーズ
Phase 1: レビュー結果の確認
1-1. レビュー結果ファイルの検索
ls dev-reports/issue/{issue_number}/review/review-result.json
# または
ls dev-reports/review/*-issue{issue_number}-*.md
1-2. レビュー結果の読み込み
レビュー結果JSONから以下を抽出:
- Must Fix: 必須改善項目
- Should Fix: 推奨改善項目
- Consider: 検討事項(今回は対象外)
Phase 2: コンテキストファイル作成
ファイルパス: dev-reports/issue/{issue_number}/review/apply-review-context.json
{
"issue_number": {issue_number},
"review_result_path": "dev-reports/issue/{issue_number}/review/review-result.json",
"design_doc_path": "dev-reports/design/issue-{issue_number}-xxx-design-policy.md",
"scope": "all",
"skip_items": [],
"additional_instructions": ""
}
Phase 3: サブエージェント呼び出し
Use apply-review-agent to update design policy for Issue #{issue_number}.
Target: dev-reports/design/issue-{issue_number}-*-design-policy.md
Context file: dev-reports/issue/{issue_number}/review/apply-review-context.json
Output file: dev-reports/issue/{issue_number}/review/apply-review-result.json
IMPORTANT: Only update design policy documents. Do NOT modify source code.
Phase 4: 結果確認と報告
4-1. 結果ファイルの確認
apply-review-result.json を読み込み、以下を確認:
- 設計方針書に反映した項目数
- スキップした項目
- 追加された実装チェックリスト
4-2. 設計方針書の更新確認
設計方針書の内容を確認:
- レビュー指摘事項が適切に反映されているか
- 実装チェックリストが網羅的か
- 次のアクション(実装コマンド)の提案
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.
- 1mo ago First seen · 173 lines · 10 tokens per session scan A 5e950fa83b41
apply-review is a command published in the GitHub repository Kewton/CommandMate (44 stars, last pushed today), licensed MIT. It adds 10 tokens to every session and 1,374 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
mach12:issue-review
Read a GitHub issue and all comments, review the implementation plan, and present findings.
convene
Run the Convener on a spec — maps the roles and groups it affects, drafts the concrete question worth asking each one, and writes the consultation record at docs/superpowers/consultations/ .md; never contacts anyone; use at plan approval alongside /choice-cartograph.
model-card
Research and author Mitchell-extended model cards. /model-card create for one-card hybrid review-before-commit flow; /model-card seed for bulk-populate from the shipped frontier seed list.
code-review
Code review a pull request (local only — never posts to GitHub).
tests
Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
staff
You MUST start your FIRST response with this exact agent activation line.