implementation-report

An automated report for a pull request, the code-change request used for review. It compares the planned work with the actual Git changes and records quality-check results and review responses.

In plain words
What is it for?
Preparing pull-request descriptions after quality checks, using the quality report, implementation plan, code diff, and review feedback.
Why use it?
It gives reviewers one summary of what changed, whether it followed the plan, and whether required quality checks passed.

Skill for Claude CodeCodex

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 skills/crearize/ai-dev-helm/implementation-report
Any agent
npx skills add Crearize/ai-dev-helm --skill implementation-report
Clone the repo
git clone --depth 1 https://github.com/Crearize/ai-dev-helm

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,575 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 $0.00038 $0.01575
Opus 5 $0.00019 $0.00788
Sonnet 5 $0.00008 $0.00315
Haiku 4.5 $0.00004 $0.00158

Measured 2d ago against content hash 07fe294d5664, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

implementation-report 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.

skills/project/implementation-report/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Implementation Report Skill - 実装レポート生成

概要

PR作成時に実装レポートを生成するスキル。quality-check スキル通過後、push → PR作成時に実行する(feature ブランチへの push はゲートされない。品質ゲートはマージ時および main への直接 push 時に .quality-check-passed を検証する)。

実装計画と実際の変更差分を比較し、品質チェック結果・レビュー指摘への対応をまとめたレポートを生成する。


前提条件

  • quality-check スキルが完了し .quality-check-report.json が存在すること
  • .quality-check-report.json が見つからない場合はエラーとし、先に quality-check スキルを実行するよう促す

実行手順

Step 1: .quality-check-report.json を読み取る
  ↓
Step 2: 実装計画ドキュメントを検索・参照
  ↓
Step 3: 計画とGit変更差分を比較し判定
  ↓
Step 4: レポートを生成
  ↓
Step 5: PR descriptionに実装レポートを含めてPR作成

Step 1: .quality-check-report.json を読み取る

プロジェクトルートの .quality-check-report.json を読み取る。

フォーマットの詳細は _schemas/quality-check-report.schema.md を参照。

ファイルが存在しない場合: エラーを出力し、先に quality-check スキルを実行するよう促して処理を中断する。


Step 2: 実装計画ドキュメントを検索・参照

docs/superpowers/plans/ 配下の計画ドキュメントを検索し、現在のブランチ・Issue に関連する計画を特定する。

  • 計画ドキュメントが見つかった場合: その内容を参照する
  • 見つからない場合: 会話コンテキスト内の計画情報を使用し、レポートに「計画ドキュメント参照不可(会話コンテキストから生成)」と注記する

Step 3: 計画とGit変更差分を比較・判定

git diff origin/main...HEAD

各Phaseについて以下を判定する:

判定 条件
計画通り 計画に記載された変更内容がdiffに反映されている
差分あり 計画と実際の変更に差異がある(追加・省略・変更)

Step 4: レポートを生成

「品質チェック結果サマリ」「ゲート上書き・承認」の各項目は、該当なしの場合も「なし」と明記する(記載の省略と該当なしを区別できるようにする)。レポートに該当フィールドが存在しない場合は「未記録」と明記する。

レポートテンプレート

## 実装レポート

### 計画との対応
| Phase | 計画内容 | 状態 | 備考 |
|-------|---------|------|------|
| Phase N | [計画内容] | 計画通り / 差分あり | [備考] |

### 計画からの差分
- **Phase N**: [差分の説明]

### 品質チェック結果サマリ
- リスクレベル: high / medium / low(`risk_level`)
- 静的チェック AI 修正パス: N回(打ち切り事由: なし / oscillation)(`lint_cycles` / `lint_abort_reason`)
- テスト設計メモ: verified / retroactive / out_of_scope / not_required(メモ: [パス] または なし)(`test_design.status` / `test_design.memo_path`)
- ミューテーションテスト: モード gate / advisory、実行 N回、調整後スコア N%(生 N% / 閾値 N%)、生存 N 件(killed n / equivalent n / accepted n / unresolved n / untriaged n)/ 未実行(理由: not_configured / low_risk / out_of_scope / mode_off / empty_scope / scope_error)(`mutation`)
- 品質チェックサイクル数: N回(N回目で高/中指摘ゼロ達成 / 打ち切り事由: なし / cycle_limit / stagnation / 追加サイクル: N回)(`total_cycles` / `cycle_abort_reason` / `cycle_extensions`)
- E2Eテスト: 全件パス / N件失敗 / 対象外
- ドキュメント更新: updated / not_required(`documentation.status`、updated の場合は対象ファイル)
- self-improvement: applied / skipped / not_required(`self_improvement.status`)

### ゲート上書き・承認
- ゲートパラメータ上書き: なし / [キー: 値](理由: [理由])(`gate_parameter_overrides`)
- 打ち切り承認: なし / [対象工程](理由: [ユーザーの判断根拠])(`gate_override`)
- リスクレベル引き下げ: なし / [メモ自己判定 → 採用レベル](理由: [根拠])(`risk_level_downgrade`)

### レビュー指摘への対応
| サイクル | 指摘内容 | ペルソナ | 対応 |
|---------|---------|---------|------|
| N回目 | [指摘内容] | [ペルソナ名] | 対応済: [詳細] / 対応不要(理由後述) |

### 対応不要と判断したもの
- **[指摘内容]**([ペルソナ名]指摘): [判断理由]

Read the full file on GitHub · 124 lines

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 · 124 lines · 38 tokens per session scan A 07fe294d5664

Subscribe to this mod's changes

implementation-report is a skill published in the GitHub repository Crearize/ai-dev-helm (4 stars, last pushed 7d ago), licensed MIT. It adds 38 tokens to every session and 1,575 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 2026-08-31.

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