diff --git a/README.md b/README.md index 97a6176d..2635dab4 100644 --- a/README.md +++ b/README.md @@ -8,15 +8,15 @@ Claude Code / Codex / Kiro CLI向けのスキル・MCP設定を共有するた **NDFプラグイン v4.20.1** は、同じ `ndf@ai-plugins` という名前で Claude Code / Codex / Kiro CLI へ配布されるランタイム別プラグインです。共通ソースは `plugins/ndf-shared/` に集約し、利用者が install する配布物は `plugins/ndf-claude/` / `plugins/ndf-codex/` / `plugins/ndf-kiro/` に分かれています。 -- **公開Skills**: Claude Code向け core 29個、Kiro向け core 28個、Codex向け core 30個に分離。 -- **元Skills（49個）**: +- **公開Skills**: Claude Code向け core 27個、Kiro向け core 26個、Codex向け core 28個に分離。 +- **元Skills（41個）**: - PR/レビューワークフロー (13): pr, pr-tests, fix, review, review-branch, review-pr-comments, resolve-pr-comments, cherry-pick-pr, deploy, sync-main, merged, clean, browser-test - 原則・ガイドライン (10): ndf-policies, branch-fix-strategy, implementation-plan, plan-to-spec, investigation-rules, problem-solving, logging-guidelines, markdown-writing, issue-plan-strategy, ml-model-structure - - データ分析・品質・環境 (12): data-analyst-sql-optimization, data-analyst-export, qa-security-scan, python-execution, docker-container-access, git-gh-operations, google-auth, codex, deepwiki-transfer, knowledge-reorg, mcp-builder, official-skills-autoloader - - E2Eテスト/Playwright (6): playwright-test-planning, playwright-script-creation, playwright-execution, playwright-report, playwright-kit-ops, playwright-scenario-test - - 外部サービス連携 (2): google-drive, google-chat + - データ分析・品質・環境 (5): qa-security-scan, docker-container-access, google-auth, codex, official-skills-autoloader + - E2Eテスト/Playwright (8): playwright-test-planning, playwright-script-creation, playwright-execution, playwright-report, playwright-kit-ops, playwright-scenario-test, playwright-browser-connect, playwright-evidence-drive + - 外部サービス連携 (1): google-drive - AIクロスレビュー (2): cross-review, gemini - - 運用 (1): skill-stats + - 運用 (2): skill-stats, statusline - **8つの専門エージェント**: director, data-analyst, corder, researcher, qa, debugger, devops-engineer, code-reviewer - **自動フック**: SessionStart (transcript保持期間を最低90日に保つ) + Stop (AI要約生成+Slack通知) - **外部AI委譲**: `/ndf:codex` skill + `corder` エージェント経由で Codex CLI をバックグラウンド実行 (v4.0.0 で Codex MCP サーバは廃止) diff --git a/docs/ndf-plugin-reference.md b/docs/ndf-plugin-reference.md index 34773d4f..ae45ae91 100644 --- a/docs/ndf-plugin-reference.md +++ b/docs/ndf-plugin-reference.md @@ -67,8 +67,8 @@ NDF の Skill 実装は `plugins/ndf-shared/skills/` が編集元です。公開 - PR / review workflow: `pr`, `pr-tests`, `fix`, `review`, `cross-review`, `resolve-pr-comments` - branch / release workflow: `deploy`, `cherry-pick-pr`, `sync-main`, `merged`, `clean` - planning / documentation: `implementation-plan`, `issue-plan-strategy`, `plan-to-spec`, `markdown-writing` -- quality / execution: `playwright-*`, `python-execution`, `docker-container-access`, `git-gh-operations` -- external services: `google-drive`, `google-chat`, `data-analyst-*` +- quality / execution: `playwright-*`, `docker-container-access` +- external services: `google-drive` - policy: `ndf-policies`, `problem-solving`, `logging-guidelines` ## MCP Plugins diff --git a/docs/official-skills-installation.md b/docs/official-skills-installation.md index d012dda6..b5814f0f 100644 --- a/docs/official-skills-installation.md +++ b/docs/official-skills-installation.md @@ -105,9 +105,9 @@ cp -r ~/work/anthropic-skills/skills/docx .claude/skills/ ## NDFプラグインとの関係 -### NDFが同梱している公式Skill（Apache-2.0のみ） +### NDFが同梱している公式Skill -- `mcp-builder` → `plugins/ndf-shared/skills/mcp-builder/`（LICENSE.txt同梱） +現在はありません。以前同梱していた `mcp-builder`（Apache-2.0）は利用実績がないため削除しました。必要な場合は下記インストーラで各自の環境に配置してください。 ### NDFが提供するインストーラ diff --git a/plugins/ndf-claude/.claude-plugin/plugin.json b/plugins/ndf-claude/.claude-plugin/plugin.json index 1dfb737a..812bf78c 100644 --- a/plugins/ndf-claude/.claude-plugin/plugin.json +++ b/plugins/ndf-claude/.claude-plugin/plugin.json @@ -37,9 +37,7 @@ "./skills/clean", "./skills/ndf-policies", "./skills/markdown-writing", - "./skills/python-execution", "./skills/docker-container-access", - "./skills/git-gh-operations", "./skills/branch-fix-strategy", "./skills/implementation-plan", "./skills/investigation-rules", diff --git a/plugins/ndf-claude/agents/data-analyst.md b/plugins/ndf-claude/agents/data-analyst.md index 802ca89d..05b23460 100644 --- a/plugins/ndf-claude/agents/data-analyst.md +++ b/plugins/ndf-claude/agents/data-analyst.md @@ -26,11 +26,46 @@ description: | - ビジネスインサイトの抽出 ### 3. データ出力 -- CSV、JSON、Excel形式でのデータエクスポート +- CSV、JSON、Excel、Markdownテーブル形式でのデータエクスポート - 結果データのファイル保存 - レポート生成とデータ可視化の準備 - データサマリーの作成 +## SQL最適化パターン + +遅いクエリは EXPLAIN で実行計画を確認し、該当パターンを適用してから再度 EXPLAIN で改善を確認する。 + +| パターン | 問題 | 解決策 | +|---------|------|--------| +| N+1削減 | ループ内SQL | JOINで1回に統合 | +| インデックス | フルスキャン | WHERE/JOIN列にインデックス | +| JOIN最適化 | 不要な大規模JOIN | 必要な列のみ取得 | +| ウィンドウ関数 | 複雑なサブクエリ | ROW_NUMBER(), RANK()使用 | +| EXISTS vs IN | 遅いIN句 | EXISTSに変更 | +| LIMIT活用 | 全件取得 | SQLでページング | + +| DO | DON'T | +|----|-------| +| EXPLAINで実行計画を確認 | 不要なDISTINCT | +| インデックスは選択的に作成 | 関数をWHERE句の列に適用 | +| 必要な列のみSELECT | 過剰なJOIN | +| 早期フィルタリング | サブクエリの多用 | + +## エクスポート形式の選択 + +| 形式 | 用途 | 注意点 | +|------|------|--------| +| CSV | 単純なデータ、他システム連携 | Excelで開くならUTF-8 BOMを付与 | +| JSON | API連携、構造化データ | 日付は ISO 8601 に統一 | +| Excel | 複雑なレポート、書式設定 | 1シート1,048,576行の上限を超えたら分割 | +| Markdown | ドキュメント埋め込み | パイプ文字のエスケープ | + +| DO | DON'T | +|----|-------| +| ヘッダー行を含める | 全データをメモリに展開 | +| 大きなデータはストリーミングで書き出す | 日付フォーマットの不統一 | +| 文字コードは UTF-8 に統一 | 特殊文字のエスケープ忘れ | + ## 使用可能なMCPツール ### BigQuery MCP diff --git a/plugins/ndf-claude/skills/deploy/SKILL.md b/plugins/ndf-claude/skills/deploy/SKILL.md index 769919d7..b338c84c 100644 --- a/plugins/ndf-claude/skills/deploy/SKILL.md +++ b/plugins/ndf-claude/skills/deploy/SKILL.md @@ -1,6 +1,6 @@ --- name: deploy -description: "Create deploy PRs from feature to environment branches." +description: "Create a deploy PR from the current feature branch to an environment branch such as qa/staging or release/v2. 破壊的操作のため、利用者が /ndf:deploy を明示的に指示したときのみ実行する（環境ブランチへデプロイ / qaに上げる / stagingに反映 / リリースブランチへPR）。deployブランチを作成し origin/main を取り込んでからPRを出す。" argument-hint: " (例: qa/staging, release/v2)" disable-model-invocation: true allowed-tools: diff --git a/plugins/ndf-claude/skills/docker-container-access/SKILL.md b/plugins/ndf-claude/skills/docker-container-access/SKILL.md index eadd1ffe..444a993f 100644 --- a/plugins/ndf-claude/skills/docker-container-access/SKILL.md +++ b/plugins/ndf-claude/skills/docker-container-access/SKILL.md @@ -67,7 +67,6 @@ ls -la /var/run/docker.sock 2>/dev/null && echo "DooD環境" || echo "DinDまた ## 関連Skill -- **python-execution**: Python実行環境の判定 - **corder-code-templates**: Dockerfileテンプレート ## 関連リソース diff --git a/plugins/ndf-claude/skills/git-gh-operations/01-common-errors.md b/plugins/ndf-claude/skills/git-gh-operations/01-common-errors.md deleted file mode 100644 index a7afa445..00000000 --- a/plugins/ndf-claude/skills/git-gh-operations/01-common-errors.md +++ /dev/null @@ -1,145 +0,0 @@ -# Git / gh 共通エラー事例集 - -## 1. git add pathspec エラー - -### 事象 -``` -fatal: pathspec 'lambda-batch/CarImageProcessingPipeline/src/foo.py' did not match any files -``` - -### 原因 -CWD が `/work/repo/lambda-batch/CarImageProcessingPipeline/` なのに、 -リポジトリルートからの相対パスで `git add` した。 - -`git status` はリポジトリルートからの相対パスで表示するが、 -`git add` は CWD からの相対パスで解決する。 - -### 予防策 -```bash -# Step 1: CWD確認 -pwd -# => /work/repo/lambda-batch/CarImageProcessingPipeline/ - -# Step 2: git status の出力を確認 -git status -# modified: lambda-batch/CarImageProcessingPipeline/src/foo.py -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -# これはリポジトリルートからの相対パス - -# Step 3: CWD からの相対パスに変換 -git add src/foo.py -# または -git add . # CWD以下のすべての変更 -``` - -## 2. gh api 404 エラー - -### 事象 -``` -gh api repos/owner/repo/pulls/comments/123/replies -f body='message' -# => 404 Not Found -``` - -### 原因 -POST メソッドが必要な API エンドポイントに GET でアクセスした。 -`gh api` はデフォルトで GET を使用する。 - -### 修正 -```bash -gh api -X POST repos/owner/repo/pulls/comments/123/replies -f body='message' -``` - -## 3. GitHub 自己 Approve エラー - -### 事象 -``` -Could not approve for pull request review. Can not approve your own pull request -``` - -### 原因 -GitHub はセキュリティ上、自分で作成した PR を APPROVE できない。 - -### 対策 -```bash -# pending review を削除してから COMMENT として再送信 -# method: "delete_pending" → method: "create" + event: "COMMENT" -``` - -## 4. AWS CLI [$LATEST] パースエラー - -### 事象 -``` -Unknown options: , , , -``` - -### 原因 -CloudWatch ログストリーム名に含まれる `[$LATEST]` が -`--query` JMESPath パーサーや shell の glob として解釈される。 - -### 対策 -```bash -# シングルクォートで囲んでも --query との組み合わせで問題が出る -# --output json + python パースが最も安全 -aws logs get-log-events \ - --log-group-name "/aws/lambda/func-name" \ - --log-stream-name '2026/02/18/[$LATEST]abc123' \ - --output json | python3 -c " -import sys, json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## 5. git commit メッセージの特殊文字 - -### 事象 -コミットメッセージに日本語や改行が含まれるとエスケープ問題が発生。 - -### 対策 -常に HEREDOC 形式を使用: -```bash -git commit -m "$(cat <<'EOF' -日本語メッセージ - -詳細説明 - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -注意: `<<'EOF'` （シングルクォート付き）で変数展開を抑制する。 - -## 6. gh pr checks が exit code 1 で止まる - -### 事象 -``` -gh pr checks 11765 2>&1 -# => チェック結果は表示されるが、1つでもfailがあると exit code 1 で終了 -# => Claude Code が「コマンド失敗」と判定して処理を中断 -``` - -### 原因 -`gh pr checks` は CI チェックに失敗があると非0の exit code を返す仕様。 -Claude Code の Bash ツールはコマンドの exit code が 0 以外だとエラーとして扱う。 - -### 対策 -常に `|| true` を付けて exit code を 0 にする: -```bash -# チェック一覧を取得（failがあっても止まらない） -gh pr checks 11765 2>&1 || true - -# --watch で完了待ちする場合も同様 -gh pr checks 11765 --watch 2>&1 || true - -# 失敗のみフィルタする場合 -gh pr checks 11765 2>&1 | grep -i fail || true -``` - -### 補足 -同様の問題が発生する gh コマンド: -- `gh run view RUN_ID --log-failed` （失敗ログ取得時） -- `gh pr diff` （差分が大きい場合にパイプ破損） - -いずれも `2>&1 || true` を付けることで安全に実行できる。 diff --git a/plugins/ndf-claude/skills/git-gh-operations/SKILL.md b/plugins/ndf-claude/skills/git-gh-operations/SKILL.md deleted file mode 100644 index 68b8a7a0..00000000 --- a/plugins/ndf-claude/skills/git-gh-operations/SKILL.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -name: git-gh-operations -description: "Resolve git and GitHub CLI operation errors." -when_to_use: "git / gh コマンドでエラーが出た or 操作方法に迷うとき。Triggers: 'git add', 'git commit', 'git push', 'gh pr', 'gh api', 'GitHub操作', 'gitエラー', 'fatal:', 'pathspec'" -allowed-tools: - - Bash - - Read ---- - -# Git / gh 操作スキル - -## 最重要ルール: CWD とパスの整合性 - -git コマンドはすべて **CWD からの相対パス** で解決される。 -操作前に必ず `pwd` で CWD を確認すること。 - -### パターン1: CWDがサブディレクトリの場合 - -``` -# CWD: /work/repo/lambda-batch/MyProject/ -# リポジトリルート: /work/repo/ - -# NG: リポジトリルートからのパスを指定 -git add lambda-batch/MyProject/src/foo.py -# => fatal: pathspec did not match any files - -# OK: CWDからの相対パスを指定 -git add src/foo.py - -# OK: 絶対パスを指定 -git add /work/repo/lambda-batch/MyProject/src/foo.py -``` - -### パターン2: 安全な方法 - -```bash -# 方法A: git -C でリポジトリルートを指定 -git -C /work/repo add lambda-batch/MyProject/src/foo.py - -# 方法B: CWD を変更せずに絶対パスを使用 -git add "$(git rev-parse --show-toplevel)/lambda-batch/MyProject/src/foo.py" - -# 方法C（推奨）: CWDからの相対パスを使用 -# まず pwd で確認してからパスを組み立てる -``` - -## git 操作チェックリスト - -### git add の前に - -1. `pwd` で CWD を確認 -2. `git status` で変更ファイルのパスを確認（表示されるパスはリポジトリルートからの相対パス） -3. `git status` の出力パスと CWD の関係を計算してから `git add` する - -### git commit の前に - -1. `git diff --cached` でステージング内容を確認 -2. HEREDOC形式でメッセージを渡す（改行・特殊文字の問題回避） - -```bash -git commit -m "$(cat <<'EOF' -コミットメッセージ - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -## gh CLI / GitHub API の注意点 - -### パラメータ: `-f` vs `-F` - -```bash -# -f: 文字列パラメータ -gh api repos/OWNER/REPO/pulls/PR/comments -f body="テキスト" - -# -F: 非文字列パラメータ（数値、boolean、null、ファイル） -gh api repos/OWNER/REPO/pulls/PR/comments -F in_reply_to=2826074026 - -# 混在OK -gh api repos/OWNER/REPO/pulls/PR/comments -f body="返信テキスト" -F in_reply_to=2826074026 -``` - -### PRレビューコメントの取得 - -```bash -# コメント一覧を取得（id, path, body の先頭を表示） -gh api repos/OWNER/REPO/pulls/PR/comments \ - --jq '.[] | {id: .id, path: .path, body: (.body | split("\n")[0][:80])}' -``` - -### PRレビューコメントへの返信 - -```bash -# NG: /replies エンドポイントは存在しない（404になる） -gh api repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# NG: -X POST を付けても同じ（エンドポイント自体が存在しない） -gh api -X POST repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# OK: in_reply_to パラメータを使って新規コメントとして投稿 -gh api repos/OWNER/REPO/pulls/PR/comments \ - -f body="返信テキスト" \ - -F in_reply_to=COMMENT_ID -``` - -### レビュースレッドの Resolve（GraphQL） - -```bash -# 1. 未解決スレッドのID一覧を取得 -gh api graphql -f query=' -query { - repository(owner: "OWNER", name: "REPO") { - pullRequest(number: PR) { - reviewThreads(first: 50) { - nodes { - id - isResolved - comments(first: 1) { - nodes { path body } - } - } - } - } - } -}' --jq '.data.repository.pullRequest.reviewThreads.nodes[] | select(.isResolved == false) | {id, path: .comments.nodes[0].path}' - -# 2. スレッドを Resolve -gh api graphql -f query=' -mutation { - resolveReviewThread(input: {threadId: "PRRT_xxx"}) { - thread { isResolved } - } -}' -``` - -### PR の CI チェック結果 - -`gh pr checks` は1つでもfailがあると **exit code 1** で終了する。 -Claude Codeではコマンド失敗と判定されて処理が止まるため、必ず `|| true` を付ける。 - -```bash -# NG: failがあるとexit code 1で止まる -gh pr checks PR --repo OWNER/REPO - -# OK: exit codeを常に0にして出力を取得 -gh pr checks PR --repo OWNER/REPO 2>&1 || true - -# OK: 失敗のみフィルタ -gh pr checks PR --repo OWNER/REPO 2>&1 | grep -i fail || true -``` - -#### 重要: CIの完了を待ってはいけない - -- `--watch` や完了までのポーリングは **禁止**。現在のステータスを一度スナップショットするだけでよい。 -- チェックが `in_progress` / `queued` / `pending` の場合は **完了を待たず次のステップへ進む**。 -- 対応対象は **コード修正で直せるfailのみ**。以下のような「ステータス確認系」チェックは無視する: - - `check_pr_requirements` 等、PR要件・メタ情報のみ検証するもの - - Lint/テストに非依存なラベル/タイトル/説明チェック - - 外部サービス起因で自己修復するトランジェントなfail（再実行で直るもの） -- 対応する: ビルド失敗・テスト失敗・型エラー・lint違反など、**リポジトリ内コードの修正で解消可能なもの**。 - -```bash -# 失敗ジョブのログ（エラー行のみ抽出） -gh run view RUN_ID --repo OWNER/REPO --log-failed 2>&1 \ - | grep -E '(FAIL|Error|Tests:)' | head -20 || true -``` - -### 自分のPRは Approve できない - -``` -# GitHub の制約: 自分で作成した PR に APPROVE レビューは不可 -# => "Can not approve your own pull request" -# 対策: event を "COMMENT" に変更して送信 -``` - -### PR作成時の body は HEREDOC - -```bash -# NG: \n がリテラルで混入する可能性 -gh pr create --title "タイトル" --body "行1\n行2" - -# OK: HEREDOC形式 -gh pr create --title "タイトル" --body "$(cat <<'EOF' -## Summary -- 変更内容 - -## Test plan -- [ ] テスト項目 -EOF -)" -``` - -## AWS CLI の注意点 - -### CloudWatch ログストリーム名の [$LATEST] - -```bash -# NG: --query で [$LATEST] を含む文字列がパースエラー -aws logs get-log-events --query 'events[*].message' --output text - -# OK: --output json にして python でパース -aws logs get-log-events --output json | python3 -c " -import sys,json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## エラー事例集 - -| エラーメッセージ | 原因 | 対策 | -|----------------|------|------| -| `fatal: pathspec '...' did not match any files` | CWD とパスの不一致 | `pwd` 確認後、CWD相対パスで指定 | -| `404 Not Found` (gh api replies) | `/comments/{id}/replies` は存在しない | `in_reply_to` パラメータで投稿 | -| `422 Unprocessable` (gh api) | `-f` で数値を渡した | 数値は `-F` を使う | -| `Can not approve your own pull request` | 自己 Approve 不可 | `COMMENT` イベントに変更 | -| `gh pr checks` が exit code 1 | 1つでもfailがあると非0終了 | `gh pr checks ... 2>&1 \|\| true` | -| `Unknown options: , , ,` (aws cli) | `[$LATEST]` のシェルエスケープ | `--output json` + python パース | - -## 詳細ガイド - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-common-errors.md` | 詳細なエラー事例と再現手順 | エラー発生時 | diff --git a/plugins/ndf-claude/skills/logging-guidelines/SKILL.md b/plugins/ndf-claude/skills/logging-guidelines/SKILL.md index 007b9691..3ad64b34 100644 --- a/plugins/ndf-claude/skills/logging-guidelines/SKILL.md +++ b/plugins/ndf-claude/skills/logging-guidelines/SKILL.md @@ -1,7 +1,19 @@ --- name: logging-guidelines -description: "Design safe and useful application logging." -when_to_use: "コードにログを追加・修正・整理するとき。Triggers: 'ログ追加', 'log追加', 'logger', 'logging', 'ログレベル', 'log level', 'デバッグログ', 'エラーログ', 'logger.info', 'logger.error', 'print文をログに'" +description: "Choose log levels and write safe, useful application logs when adding or reworking logging in code（ログ追加 / logger / ログレベル / デバッグログ / エラーログ / print文をログに）. Use when editing source code that emits logs, to pick the level and keep secrets and personal data out of the output." +paths: + - "**/*.py" + - "**/*.ts" + - "**/*.tsx" + - "**/*.js" + - "**/*.jsx" + - "**/*.go" + - "**/*.rb" + - "**/*.java" + - "**/*.kt" + - "**/*.php" + - "**/*.rs" + - "**/*.sh" --- # ログ運用ガイドライン diff --git a/plugins/ndf-claude/skills/python-execution/01-uv-setup.md b/plugins/ndf-claude/skills/python-execution/01-uv-setup.md deleted file mode 100644 index aafd4f1c..00000000 --- a/plugins/ndf-claude/skills/python-execution/01-uv-setup.md +++ /dev/null @@ -1,85 +0,0 @@ -# uv詳細セットアップガイド - -> **Note**: 基本的な`uv sync`と`uv run python`はSKILL.mdを参照。このファイルは初回セットアップや詳細設定が必要な場合のみ参照。 - -## uvインストール - -```bash -# Linux/macOS -curl -LsSf https://astral.sh/uv/install.sh | sh - -# pip経由（代替） -pip install uv - -# 確認 -uv --version -``` - -## 依存関係管理 - -```bash -# uv.lockがある場合（推奨） -uv sync - -# uv.lockがない場合 -uv lock && uv sync - -# 開発用依存関係も含める -uv sync --dev - -# 特定のextraを含める -uv sync --extra test -``` - -## Pythonバージョン管理 - -```bash -# 特定バージョンをインストール -uv python install 3.12 -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.12 - -# インストール済みバージョン一覧 -uv python list -``` - -## 実行オプション - -```bash -# スクリプト実行 -uv run python script.py - -# モジュール実行 -uv run python -m pytest -uv run python -m mypy . - -# 引数付き -uv run python script.py --arg value - -# インタラクティブシェル -uv run python -``` - -## プロジェクト初期化（新規作成時） - -```bash -# 新規プロジェクト作成 -uv init my-project -cd my-project - -# 依存関係追加 -uv add requests -uv add --dev pytest - -# ロックファイル生成 -uv lock -``` - -## uv環境の利点 - -- **高速**: Rustで実装、pip比10-100倍速 -- **再現性**: uv.lockで完全な依存関係固定 -- **Pythonバージョン管理**: pyenvなしでバージョン切り替え -- **グローバル環境を汚染しない**: プロジェクト単位で隔離 diff --git a/plugins/ndf-claude/skills/python-execution/02-troubleshooting.md b/plugins/ndf-claude/skills/python-execution/02-troubleshooting.md deleted file mode 100644 index 3475b7ec..00000000 --- a/plugins/ndf-claude/skills/python-execution/02-troubleshooting.md +++ /dev/null @@ -1,112 +0,0 @@ -# Python実行 トラブルシューティング - -## よくある問題と解決策 - -### Q: `uv: command not found` - -**原因**: uvがインストールされていない - -**解決策**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -# シェルを再起動するか、パスを通す -source ~/.bashrc # または ~/.zshrc -``` - -### Q: `ModuleNotFoundError` - -**原因**: 依存関係がインストールされていない - -**解決策**: -```bash -# uv環境の場合 -uv sync - -# venv環境の場合 -.venv/bin/pip install -r requirements.txt - -# pyproject.tomlがある場合 -.venv/bin/pip install -e . -``` - -### Q: `python: command not found` - -**原因**: Pythonがインストールされていない、またはパスが通っていない - -**解決策**: -```bash -# python3を試す -python3 --version - -# uvでPythonをインストール -uv python install 3.12 -``` - -### Q: 異なるPythonバージョンが必要 - -**解決策（uv環境）**: -```bash -# 特定バージョンをインストール -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.11 - -# そのバージョンで実行 -uv run python script.py -``` - -### Q: `pyproject.toml`はあるが`uv.lock`がない - -**解決策**: -```bash -# ロックファイルを生成 -uv lock - -# 依存関係をインストール -uv sync -``` - -### Q: 仮想環境が壊れている - -**解決策**: -```bash -# 仮想環境を削除して再作成 -rm -rf .venv - -# uv環境の場合 -uv sync - -# 手動で作成する場合 -python3 -m venv .venv -.venv/bin/pip install -r requirements.txt -``` - -### Q: パーミッションエラー - -**解決策**: -```bash -# 仮想環境を使用（推奨） -uv sync -uv run python script.py - -# どうしてもグローバルにインストールする場合（非推奨） -pip install --user package_name -``` - -### Q: SSL証明書エラー - -**解決策**: -```bash -# macOSの場合 -/Applications/Python\ 3.x/Install\ Certificates.command - -# または環境変数で一時的に無効化（非推奨） -export PYTHONHTTPSVERIFY=0 -``` - -## 関連リソース - -- [uv公式ドキュメント](https://docs.astral.sh/uv/) -- [Python venv](https://docs.python.org/3/library/venv.html) -- [pyproject.toml仕様](https://packaging.python.org/en/latest/specifications/pyproject-toml/) diff --git a/plugins/ndf-claude/skills/python-execution/SKILL.md b/plugins/ndf-claude/skills/python-execution/SKILL.md deleted file mode 100644 index a07705aa..00000000 --- a/plugins/ndf-claude/skills/python-execution/SKILL.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -name: python-execution -description: "Detect and run the right Python environment." -when_to_use: "Python スクリプトを実行 / セットアップするとき。Triggers: 'python', 'uv', 'スクリプト', 'python環境'" -allowed-tools: - - Read - - Bash - - Glob ---- - -# Python Execution Skill - -## 概要 - -Pythonコードを実行する前に、プロジェクトの実行環境を調査し、適切な方法で実行するためのガイドラインです。 - -## Step 1: 環境検出 - -```bash -ls -la pyproject.toml uv.lock .venv/ venv/ requirements.txt 2>/dev/null -``` - -## Step 2: 実行コマンド選択 - -| 検出ファイル | 実行方法 | 優先度 | -|-------------|---------|-------| -| `pyproject.toml` | `uv run python` | 最高 | -| `.venv/` | `.venv/bin/python` | 中 | -| `venv/` | `venv/bin/python` | 中 | -| 何もなし | `python3` | 最低 | - -## Step 3: 実行 - -### uv環境（pyproject.tomlあり） - -```bash -# 依存関係インストール（初回のみ） -uv sync - -# 実行 -uv run python script.py -uv run python -m module_name -``` - -**uvがない場合のインストール**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -source ~/.bashrc # パスを反映 -``` - -### venv環境（.venv/あり） - -```bash -# 依存関係インストール（初回のみ） -.venv/bin/pip install -r requirements.txt - -# 実行 -.venv/bin/python script.py -``` - -### システムPython - -```bash -python3 script.py -``` - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| 実行前に環境を調査 | 環境を確認せずに実行 | -| README.md/CLAUDE.mdの指示を優先 | グローバル環境に依存関係をインストール | -| pyproject.tomlがあればuv使用 | source activateに依存 | -| 仮想環境のPythonをパス指定で実行 | python2を使用 | - -## 詳細ガイド（必要時のみ参照） - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-uv-setup.md` | uv詳細セットアップ、Pythonバージョン管理 | 初回セットアップ時 | -| `02-troubleshooting.md` | エラー解決策 | 問題発生時 | - -## 関連Skill - -- **corder-code-templates**: Pythonコードテンプレート -- **corder-test-generation**: Pythonテスト生成 diff --git a/plugins/ndf-codex/.codex-plugin/plugin.json b/plugins/ndf-codex/.codex-plugin/plugin.json index 806a9c3d..127ba714 100644 --- a/plugins/ndf-codex/.codex-plugin/plugin.json +++ b/plugins/ndf-codex/.codex-plugin/plugin.json @@ -1,7 +1,7 @@ { "name": "ndf", "version": "4.20.1", - "description": "Codex plugin with focused NDF skills for PR/review workflows, cross-review, implementation planning, Playwright testing, Docker container access, GitHub operations, and optional Slack completion notifications.", + "description": "Codex plugin with focused NDF skills for PR/review workflows, cross-review, implementation planning, Playwright testing, Docker container access, and optional Slack completion notifications.", "skills": "./skills/", "hooks": "./hooks/hooks.json" } diff --git a/plugins/ndf-codex/skills/deploy/SKILL.md b/plugins/ndf-codex/skills/deploy/SKILL.md index 769919d7..b338c84c 100644 --- a/plugins/ndf-codex/skills/deploy/SKILL.md +++ b/plugins/ndf-codex/skills/deploy/SKILL.md @@ -1,6 +1,6 @@ --- name: deploy -description: "Create deploy PRs from feature to environment branches." +description: "Create a deploy PR from the current feature branch to an environment branch such as qa/staging or release/v2. 破壊的操作のため、利用者が /ndf:deploy を明示的に指示したときのみ実行する（環境ブランチへデプロイ / qaに上げる / stagingに反映 / リリースブランチへPR）。deployブランチを作成し origin/main を取り込んでからPRを出す。" argument-hint: " (例: qa/staging, release/v2)" disable-model-invocation: true allowed-tools: diff --git a/plugins/ndf-codex/skills/docker-container-access/SKILL.md b/plugins/ndf-codex/skills/docker-container-access/SKILL.md index eadd1ffe..444a993f 100644 --- a/plugins/ndf-codex/skills/docker-container-access/SKILL.md +++ b/plugins/ndf-codex/skills/docker-container-access/SKILL.md @@ -67,7 +67,6 @@ ls -la /var/run/docker.sock 2>/dev/null && echo "DooD環境" || echo "DinDまた ## 関連Skill -- **python-execution**: Python実行環境の判定 - **corder-code-templates**: Dockerfileテンプレート ## 関連リソース diff --git a/plugins/ndf-codex/skills/git-gh-operations/01-common-errors.md b/plugins/ndf-codex/skills/git-gh-operations/01-common-errors.md deleted file mode 100644 index a7afa445..00000000 --- a/plugins/ndf-codex/skills/git-gh-operations/01-common-errors.md +++ /dev/null @@ -1,145 +0,0 @@ -# Git / gh 共通エラー事例集 - -## 1. git add pathspec エラー - -### 事象 -``` -fatal: pathspec 'lambda-batch/CarImageProcessingPipeline/src/foo.py' did not match any files -``` - -### 原因 -CWD が `/work/repo/lambda-batch/CarImageProcessingPipeline/` なのに、 -リポジトリルートからの相対パスで `git add` した。 - -`git status` はリポジトリルートからの相対パスで表示するが、 -`git add` は CWD からの相対パスで解決する。 - -### 予防策 -```bash -# Step 1: CWD確認 -pwd -# => /work/repo/lambda-batch/CarImageProcessingPipeline/ - -# Step 2: git status の出力を確認 -git status -# modified: lambda-batch/CarImageProcessingPipeline/src/foo.py -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -# これはリポジトリルートからの相対パス - -# Step 3: CWD からの相対パスに変換 -git add src/foo.py -# または -git add . # CWD以下のすべての変更 -``` - -## 2. gh api 404 エラー - -### 事象 -``` -gh api repos/owner/repo/pulls/comments/123/replies -f body='message' -# => 404 Not Found -``` - -### 原因 -POST メソッドが必要な API エンドポイントに GET でアクセスした。 -`gh api` はデフォルトで GET を使用する。 - -### 修正 -```bash -gh api -X POST repos/owner/repo/pulls/comments/123/replies -f body='message' -``` - -## 3. GitHub 自己 Approve エラー - -### 事象 -``` -Could not approve for pull request review. Can not approve your own pull request -``` - -### 原因 -GitHub はセキュリティ上、自分で作成した PR を APPROVE できない。 - -### 対策 -```bash -# pending review を削除してから COMMENT として再送信 -# method: "delete_pending" → method: "create" + event: "COMMENT" -``` - -## 4. AWS CLI [$LATEST] パースエラー - -### 事象 -``` -Unknown options: , , , -``` - -### 原因 -CloudWatch ログストリーム名に含まれる `[$LATEST]` が -`--query` JMESPath パーサーや shell の glob として解釈される。 - -### 対策 -```bash -# シングルクォートで囲んでも --query との組み合わせで問題が出る -# --output json + python パースが最も安全 -aws logs get-log-events \ - --log-group-name "/aws/lambda/func-name" \ - --log-stream-name '2026/02/18/[$LATEST]abc123' \ - --output json | python3 -c " -import sys, json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## 5. git commit メッセージの特殊文字 - -### 事象 -コミットメッセージに日本語や改行が含まれるとエスケープ問題が発生。 - -### 対策 -常に HEREDOC 形式を使用: -```bash -git commit -m "$(cat <<'EOF' -日本語メッセージ - -詳細説明 - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -注意: `<<'EOF'` （シングルクォート付き）で変数展開を抑制する。 - -## 6. gh pr checks が exit code 1 で止まる - -### 事象 -``` -gh pr checks 11765 2>&1 -# => チェック結果は表示されるが、1つでもfailがあると exit code 1 で終了 -# => Claude Code が「コマンド失敗」と判定して処理を中断 -``` - -### 原因 -`gh pr checks` は CI チェックに失敗があると非0の exit code を返す仕様。 -Claude Code の Bash ツールはコマンドの exit code が 0 以外だとエラーとして扱う。 - -### 対策 -常に `|| true` を付けて exit code を 0 にする: -```bash -# チェック一覧を取得（failがあっても止まらない） -gh pr checks 11765 2>&1 || true - -# --watch で完了待ちする場合も同様 -gh pr checks 11765 --watch 2>&1 || true - -# 失敗のみフィルタする場合 -gh pr checks 11765 2>&1 | grep -i fail || true -``` - -### 補足 -同様の問題が発生する gh コマンド: -- `gh run view RUN_ID --log-failed` （失敗ログ取得時） -- `gh pr diff` （差分が大きい場合にパイプ破損） - -いずれも `2>&1 || true` を付けることで安全に実行できる。 diff --git a/plugins/ndf-codex/skills/git-gh-operations/SKILL.md b/plugins/ndf-codex/skills/git-gh-operations/SKILL.md deleted file mode 100644 index 68b8a7a0..00000000 --- a/plugins/ndf-codex/skills/git-gh-operations/SKILL.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -name: git-gh-operations -description: "Resolve git and GitHub CLI operation errors." -when_to_use: "git / gh コマンドでエラーが出た or 操作方法に迷うとき。Triggers: 'git add', 'git commit', 'git push', 'gh pr', 'gh api', 'GitHub操作', 'gitエラー', 'fatal:', 'pathspec'" -allowed-tools: - - Bash - - Read ---- - -# Git / gh 操作スキル - -## 最重要ルール: CWD とパスの整合性 - -git コマンドはすべて **CWD からの相対パス** で解決される。 -操作前に必ず `pwd` で CWD を確認すること。 - -### パターン1: CWDがサブディレクトリの場合 - -``` -# CWD: /work/repo/lambda-batch/MyProject/ -# リポジトリルート: /work/repo/ - -# NG: リポジトリルートからのパスを指定 -git add lambda-batch/MyProject/src/foo.py -# => fatal: pathspec did not match any files - -# OK: CWDからの相対パスを指定 -git add src/foo.py - -# OK: 絶対パスを指定 -git add /work/repo/lambda-batch/MyProject/src/foo.py -``` - -### パターン2: 安全な方法 - -```bash -# 方法A: git -C でリポジトリルートを指定 -git -C /work/repo add lambda-batch/MyProject/src/foo.py - -# 方法B: CWD を変更せずに絶対パスを使用 -git add "$(git rev-parse --show-toplevel)/lambda-batch/MyProject/src/foo.py" - -# 方法C（推奨）: CWDからの相対パスを使用 -# まず pwd で確認してからパスを組み立てる -``` - -## git 操作チェックリスト - -### git add の前に - -1. `pwd` で CWD を確認 -2. `git status` で変更ファイルのパスを確認（表示されるパスはリポジトリルートからの相対パス） -3. `git status` の出力パスと CWD の関係を計算してから `git add` する - -### git commit の前に - -1. `git diff --cached` でステージング内容を確認 -2. HEREDOC形式でメッセージを渡す（改行・特殊文字の問題回避） - -```bash -git commit -m "$(cat <<'EOF' -コミットメッセージ - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -## gh CLI / GitHub API の注意点 - -### パラメータ: `-f` vs `-F` - -```bash -# -f: 文字列パラメータ -gh api repos/OWNER/REPO/pulls/PR/comments -f body="テキスト" - -# -F: 非文字列パラメータ（数値、boolean、null、ファイル） -gh api repos/OWNER/REPO/pulls/PR/comments -F in_reply_to=2826074026 - -# 混在OK -gh api repos/OWNER/REPO/pulls/PR/comments -f body="返信テキスト" -F in_reply_to=2826074026 -``` - -### PRレビューコメントの取得 - -```bash -# コメント一覧を取得（id, path, body の先頭を表示） -gh api repos/OWNER/REPO/pulls/PR/comments \ - --jq '.[] | {id: .id, path: .path, body: (.body | split("\n")[0][:80])}' -``` - -### PRレビューコメントへの返信 - -```bash -# NG: /replies エンドポイントは存在しない（404になる） -gh api repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# NG: -X POST を付けても同じ（エンドポイント自体が存在しない） -gh api -X POST repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# OK: in_reply_to パラメータを使って新規コメントとして投稿 -gh api repos/OWNER/REPO/pulls/PR/comments \ - -f body="返信テキスト" \ - -F in_reply_to=COMMENT_ID -``` - -### レビュースレッドの Resolve（GraphQL） - -```bash -# 1. 未解決スレッドのID一覧を取得 -gh api graphql -f query=' -query { - repository(owner: "OWNER", name: "REPO") { - pullRequest(number: PR) { - reviewThreads(first: 50) { - nodes { - id - isResolved - comments(first: 1) { - nodes { path body } - } - } - } - } - } -}' --jq '.data.repository.pullRequest.reviewThreads.nodes[] | select(.isResolved == false) | {id, path: .comments.nodes[0].path}' - -# 2. スレッドを Resolve -gh api graphql -f query=' -mutation { - resolveReviewThread(input: {threadId: "PRRT_xxx"}) { - thread { isResolved } - } -}' -``` - -### PR の CI チェック結果 - -`gh pr checks` は1つでもfailがあると **exit code 1** で終了する。 -Claude Codeではコマンド失敗と判定されて処理が止まるため、必ず `|| true` を付ける。 - -```bash -# NG: failがあるとexit code 1で止まる -gh pr checks PR --repo OWNER/REPO - -# OK: exit codeを常に0にして出力を取得 -gh pr checks PR --repo OWNER/REPO 2>&1 || true - -# OK: 失敗のみフィルタ -gh pr checks PR --repo OWNER/REPO 2>&1 | grep -i fail || true -``` - -#### 重要: CIの完了を待ってはいけない - -- `--watch` や完了までのポーリングは **禁止**。現在のステータスを一度スナップショットするだけでよい。 -- チェックが `in_progress` / `queued` / `pending` の場合は **完了を待たず次のステップへ進む**。 -- 対応対象は **コード修正で直せるfailのみ**。以下のような「ステータス確認系」チェックは無視する: - - `check_pr_requirements` 等、PR要件・メタ情報のみ検証するもの - - Lint/テストに非依存なラベル/タイトル/説明チェック - - 外部サービス起因で自己修復するトランジェントなfail（再実行で直るもの） -- 対応する: ビルド失敗・テスト失敗・型エラー・lint違反など、**リポジトリ内コードの修正で解消可能なもの**。 - -```bash -# 失敗ジョブのログ（エラー行のみ抽出） -gh run view RUN_ID --repo OWNER/REPO --log-failed 2>&1 \ - | grep -E '(FAIL|Error|Tests:)' | head -20 || true -``` - -### 自分のPRは Approve できない - -``` -# GitHub の制約: 自分で作成した PR に APPROVE レビューは不可 -# => "Can not approve your own pull request" -# 対策: event を "COMMENT" に変更して送信 -``` - -### PR作成時の body は HEREDOC - -```bash -# NG: \n がリテラルで混入する可能性 -gh pr create --title "タイトル" --body "行1\n行2" - -# OK: HEREDOC形式 -gh pr create --title "タイトル" --body "$(cat <<'EOF' -## Summary -- 変更内容 - -## Test plan -- [ ] テスト項目 -EOF -)" -``` - -## AWS CLI の注意点 - -### CloudWatch ログストリーム名の [$LATEST] - -```bash -# NG: --query で [$LATEST] を含む文字列がパースエラー -aws logs get-log-events --query 'events[*].message' --output text - -# OK: --output json にして python でパース -aws logs get-log-events --output json | python3 -c " -import sys,json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## エラー事例集 - -| エラーメッセージ | 原因 | 対策 | -|----------------|------|------| -| `fatal: pathspec '...' did not match any files` | CWD とパスの不一致 | `pwd` 確認後、CWD相対パスで指定 | -| `404 Not Found` (gh api replies) | `/comments/{id}/replies` は存在しない | `in_reply_to` パラメータで投稿 | -| `422 Unprocessable` (gh api) | `-f` で数値を渡した | 数値は `-F` を使う | -| `Can not approve your own pull request` | 自己 Approve 不可 | `COMMENT` イベントに変更 | -| `gh pr checks` が exit code 1 | 1つでもfailがあると非0終了 | `gh pr checks ... 2>&1 \|\| true` | -| `Unknown options: , , ,` (aws cli) | `[$LATEST]` のシェルエスケープ | `--output json` + python パース | - -## 詳細ガイド - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-common-errors.md` | 詳細なエラー事例と再現手順 | エラー発生時 | diff --git a/plugins/ndf-codex/skills/logging-guidelines/SKILL.md b/plugins/ndf-codex/skills/logging-guidelines/SKILL.md index 007b9691..3ad64b34 100644 --- a/plugins/ndf-codex/skills/logging-guidelines/SKILL.md +++ b/plugins/ndf-codex/skills/logging-guidelines/SKILL.md @@ -1,7 +1,19 @@ --- name: logging-guidelines -description: "Design safe and useful application logging." -when_to_use: "コードにログを追加・修正・整理するとき。Triggers: 'ログ追加', 'log追加', 'logger', 'logging', 'ログレベル', 'log level', 'デバッグログ', 'エラーログ', 'logger.info', 'logger.error', 'print文をログに'" +description: "Choose log levels and write safe, useful application logs when adding or reworking logging in code（ログ追加 / logger / ログレベル / デバッグログ / エラーログ / print文をログに）. Use when editing source code that emits logs, to pick the level and keep secrets and personal data out of the output." +paths: + - "**/*.py" + - "**/*.ts" + - "**/*.tsx" + - "**/*.js" + - "**/*.jsx" + - "**/*.go" + - "**/*.rb" + - "**/*.java" + - "**/*.kt" + - "**/*.php" + - "**/*.rs" + - "**/*.sh" --- # ログ運用ガイドライン diff --git a/plugins/ndf-codex/skills/python-execution/01-uv-setup.md b/plugins/ndf-codex/skills/python-execution/01-uv-setup.md deleted file mode 100644 index aafd4f1c..00000000 --- a/plugins/ndf-codex/skills/python-execution/01-uv-setup.md +++ /dev/null @@ -1,85 +0,0 @@ -# uv詳細セットアップガイド - -> **Note**: 基本的な`uv sync`と`uv run python`はSKILL.mdを参照。このファイルは初回セットアップや詳細設定が必要な場合のみ参照。 - -## uvインストール - -```bash -# Linux/macOS -curl -LsSf https://astral.sh/uv/install.sh | sh - -# pip経由（代替） -pip install uv - -# 確認 -uv --version -``` - -## 依存関係管理 - -```bash -# uv.lockがある場合（推奨） -uv sync - -# uv.lockがない場合 -uv lock && uv sync - -# 開発用依存関係も含める -uv sync --dev - -# 特定のextraを含める -uv sync --extra test -``` - -## Pythonバージョン管理 - -```bash -# 特定バージョンをインストール -uv python install 3.12 -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.12 - -# インストール済みバージョン一覧 -uv python list -``` - -## 実行オプション - -```bash -# スクリプト実行 -uv run python script.py - -# モジュール実行 -uv run python -m pytest -uv run python -m mypy . - -# 引数付き -uv run python script.py --arg value - -# インタラクティブシェル -uv run python -``` - -## プロジェクト初期化（新規作成時） - -```bash -# 新規プロジェクト作成 -uv init my-project -cd my-project - -# 依存関係追加 -uv add requests -uv add --dev pytest - -# ロックファイル生成 -uv lock -``` - -## uv環境の利点 - -- **高速**: Rustで実装、pip比10-100倍速 -- **再現性**: uv.lockで完全な依存関係固定 -- **Pythonバージョン管理**: pyenvなしでバージョン切り替え -- **グローバル環境を汚染しない**: プロジェクト単位で隔離 diff --git a/plugins/ndf-codex/skills/python-execution/02-troubleshooting.md b/plugins/ndf-codex/skills/python-execution/02-troubleshooting.md deleted file mode 100644 index 3475b7ec..00000000 --- a/plugins/ndf-codex/skills/python-execution/02-troubleshooting.md +++ /dev/null @@ -1,112 +0,0 @@ -# Python実行 トラブルシューティング - -## よくある問題と解決策 - -### Q: `uv: command not found` - -**原因**: uvがインストールされていない - -**解決策**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -# シェルを再起動するか、パスを通す -source ~/.bashrc # または ~/.zshrc -``` - -### Q: `ModuleNotFoundError` - -**原因**: 依存関係がインストールされていない - -**解決策**: -```bash -# uv環境の場合 -uv sync - -# venv環境の場合 -.venv/bin/pip install -r requirements.txt - -# pyproject.tomlがある場合 -.venv/bin/pip install -e . -``` - -### Q: `python: command not found` - -**原因**: Pythonがインストールされていない、またはパスが通っていない - -**解決策**: -```bash -# python3を試す -python3 --version - -# uvでPythonをインストール -uv python install 3.12 -``` - -### Q: 異なるPythonバージョンが必要 - -**解決策（uv環境）**: -```bash -# 特定バージョンをインストール -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.11 - -# そのバージョンで実行 -uv run python script.py -``` - -### Q: `pyproject.toml`はあるが`uv.lock`がない - -**解決策**: -```bash -# ロックファイルを生成 -uv lock - -# 依存関係をインストール -uv sync -``` - -### Q: 仮想環境が壊れている - -**解決策**: -```bash -# 仮想環境を削除して再作成 -rm -rf .venv - -# uv環境の場合 -uv sync - -# 手動で作成する場合 -python3 -m venv .venv -.venv/bin/pip install -r requirements.txt -``` - -### Q: パーミッションエラー - -**解決策**: -```bash -# 仮想環境を使用（推奨） -uv sync -uv run python script.py - -# どうしてもグローバルにインストールする場合（非推奨） -pip install --user package_name -``` - -### Q: SSL証明書エラー - -**解決策**: -```bash -# macOSの場合 -/Applications/Python\ 3.x/Install\ Certificates.command - -# または環境変数で一時的に無効化（非推奨） -export PYTHONHTTPSVERIFY=0 -``` - -## 関連リソース - -- [uv公式ドキュメント](https://docs.astral.sh/uv/) -- [Python venv](https://docs.python.org/3/library/venv.html) -- [pyproject.toml仕様](https://packaging.python.org/en/latest/specifications/pyproject-toml/) diff --git a/plugins/ndf-codex/skills/python-execution/SKILL.md b/plugins/ndf-codex/skills/python-execution/SKILL.md deleted file mode 100644 index a07705aa..00000000 --- a/plugins/ndf-codex/skills/python-execution/SKILL.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -name: python-execution -description: "Detect and run the right Python environment." -when_to_use: "Python スクリプトを実行 / セットアップするとき。Triggers: 'python', 'uv', 'スクリプト', 'python環境'" -allowed-tools: - - Read - - Bash - - Glob ---- - -# Python Execution Skill - -## 概要 - -Pythonコードを実行する前に、プロジェクトの実行環境を調査し、適切な方法で実行するためのガイドラインです。 - -## Step 1: 環境検出 - -```bash -ls -la pyproject.toml uv.lock .venv/ venv/ requirements.txt 2>/dev/null -``` - -## Step 2: 実行コマンド選択 - -| 検出ファイル | 実行方法 | 優先度 | -|-------------|---------|-------| -| `pyproject.toml` | `uv run python` | 最高 | -| `.venv/` | `.venv/bin/python` | 中 | -| `venv/` | `venv/bin/python` | 中 | -| 何もなし | `python3` | 最低 | - -## Step 3: 実行 - -### uv環境（pyproject.tomlあり） - -```bash -# 依存関係インストール（初回のみ） -uv sync - -# 実行 -uv run python script.py -uv run python -m module_name -``` - -**uvがない場合のインストール**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -source ~/.bashrc # パスを反映 -``` - -### venv環境（.venv/あり） - -```bash -# 依存関係インストール（初回のみ） -.venv/bin/pip install -r requirements.txt - -# 実行 -.venv/bin/python script.py -``` - -### システムPython - -```bash -python3 script.py -``` - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| 実行前に環境を調査 | 環境を確認せずに実行 | -| README.md/CLAUDE.mdの指示を優先 | グローバル環境に依存関係をインストール | -| pyproject.tomlがあればuv使用 | source activateに依存 | -| 仮想環境のPythonをパス指定で実行 | python2を使用 | - -## 詳細ガイド（必要時のみ参照） - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-uv-setup.md` | uv詳細セットアップ、Pythonバージョン管理 | 初回セットアップ時 | -| `02-troubleshooting.md` | エラー解決策 | 問題発生時 | - -## 関連Skill - -- **corder-code-templates**: Pythonコードテンプレート -- **corder-test-generation**: Pythonテスト生成 diff --git a/plugins/ndf-kiro/skills/deploy/SKILL.md b/plugins/ndf-kiro/skills/deploy/SKILL.md index 769919d7..b338c84c 100644 --- a/plugins/ndf-kiro/skills/deploy/SKILL.md +++ b/plugins/ndf-kiro/skills/deploy/SKILL.md @@ -1,6 +1,6 @@ --- name: deploy -description: "Create deploy PRs from feature to environment branches." +description: "Create a deploy PR from the current feature branch to an environment branch such as qa/staging or release/v2. 破壊的操作のため、利用者が /ndf:deploy を明示的に指示したときのみ実行する（環境ブランチへデプロイ / qaに上げる / stagingに反映 / リリースブランチへPR）。deployブランチを作成し origin/main を取り込んでからPRを出す。" argument-hint: " (例: qa/staging, release/v2)" disable-model-invocation: true allowed-tools: diff --git a/plugins/ndf-kiro/skills/docker-container-access/SKILL.md b/plugins/ndf-kiro/skills/docker-container-access/SKILL.md index eadd1ffe..444a993f 100644 --- a/plugins/ndf-kiro/skills/docker-container-access/SKILL.md +++ b/plugins/ndf-kiro/skills/docker-container-access/SKILL.md @@ -67,7 +67,6 @@ ls -la /var/run/docker.sock 2>/dev/null && echo "DooD環境" || echo "DinDまた ## 関連Skill -- **python-execution**: Python実行環境の判定 - **corder-code-templates**: Dockerfileテンプレート ## 関連リソース diff --git a/plugins/ndf-kiro/skills/git-gh-operations/01-common-errors.md b/plugins/ndf-kiro/skills/git-gh-operations/01-common-errors.md deleted file mode 100644 index a7afa445..00000000 --- a/plugins/ndf-kiro/skills/git-gh-operations/01-common-errors.md +++ /dev/null @@ -1,145 +0,0 @@ -# Git / gh 共通エラー事例集 - -## 1. git add pathspec エラー - -### 事象 -``` -fatal: pathspec 'lambda-batch/CarImageProcessingPipeline/src/foo.py' did not match any files -``` - -### 原因 -CWD が `/work/repo/lambda-batch/CarImageProcessingPipeline/` なのに、 -リポジトリルートからの相対パスで `git add` した。 - -`git status` はリポジトリルートからの相対パスで表示するが、 -`git add` は CWD からの相対パスで解決する。 - -### 予防策 -```bash -# Step 1: CWD確認 -pwd -# => /work/repo/lambda-batch/CarImageProcessingPipeline/ - -# Step 2: git status の出力を確認 -git status -# modified: lambda-batch/CarImageProcessingPipeline/src/foo.py -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -# これはリポジトリルートからの相対パス - -# Step 3: CWD からの相対パスに変換 -git add src/foo.py -# または -git add . # CWD以下のすべての変更 -``` - -## 2. gh api 404 エラー - -### 事象 -``` -gh api repos/owner/repo/pulls/comments/123/replies -f body='message' -# => 404 Not Found -``` - -### 原因 -POST メソッドが必要な API エンドポイントに GET でアクセスした。 -`gh api` はデフォルトで GET を使用する。 - -### 修正 -```bash -gh api -X POST repos/owner/repo/pulls/comments/123/replies -f body='message' -``` - -## 3. GitHub 自己 Approve エラー - -### 事象 -``` -Could not approve for pull request review. Can not approve your own pull request -``` - -### 原因 -GitHub はセキュリティ上、自分で作成した PR を APPROVE できない。 - -### 対策 -```bash -# pending review を削除してから COMMENT として再送信 -# method: "delete_pending" → method: "create" + event: "COMMENT" -``` - -## 4. AWS CLI [$LATEST] パースエラー - -### 事象 -``` -Unknown options: , , , -``` - -### 原因 -CloudWatch ログストリーム名に含まれる `[$LATEST]` が -`--query` JMESPath パーサーや shell の glob として解釈される。 - -### 対策 -```bash -# シングルクォートで囲んでも --query との組み合わせで問題が出る -# --output json + python パースが最も安全 -aws logs get-log-events \ - --log-group-name "/aws/lambda/func-name" \ - --log-stream-name '2026/02/18/[$LATEST]abc123' \ - --output json | python3 -c " -import sys, json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## 5. git commit メッセージの特殊文字 - -### 事象 -コミットメッセージに日本語や改行が含まれるとエスケープ問題が発生。 - -### 対策 -常に HEREDOC 形式を使用: -```bash -git commit -m "$(cat <<'EOF' -日本語メッセージ - -詳細説明 - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -注意: `<<'EOF'` （シングルクォート付き）で変数展開を抑制する。 - -## 6. gh pr checks が exit code 1 で止まる - -### 事象 -``` -gh pr checks 11765 2>&1 -# => チェック結果は表示されるが、1つでもfailがあると exit code 1 で終了 -# => Claude Code が「コマンド失敗」と判定して処理を中断 -``` - -### 原因 -`gh pr checks` は CI チェックに失敗があると非0の exit code を返す仕様。 -Claude Code の Bash ツールはコマンドの exit code が 0 以外だとエラーとして扱う。 - -### 対策 -常に `|| true` を付けて exit code を 0 にする: -```bash -# チェック一覧を取得（failがあっても止まらない） -gh pr checks 11765 2>&1 || true - -# --watch で完了待ちする場合も同様 -gh pr checks 11765 --watch 2>&1 || true - -# 失敗のみフィルタする場合 -gh pr checks 11765 2>&1 | grep -i fail || true -``` - -### 補足 -同様の問題が発生する gh コマンド: -- `gh run view RUN_ID --log-failed` （失敗ログ取得時） -- `gh pr diff` （差分が大きい場合にパイプ破損） - -いずれも `2>&1 || true` を付けることで安全に実行できる。 diff --git a/plugins/ndf-kiro/skills/git-gh-operations/SKILL.md b/plugins/ndf-kiro/skills/git-gh-operations/SKILL.md deleted file mode 100644 index 68b8a7a0..00000000 --- a/plugins/ndf-kiro/skills/git-gh-operations/SKILL.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -name: git-gh-operations -description: "Resolve git and GitHub CLI operation errors." -when_to_use: "git / gh コマンドでエラーが出た or 操作方法に迷うとき。Triggers: 'git add', 'git commit', 'git push', 'gh pr', 'gh api', 'GitHub操作', 'gitエラー', 'fatal:', 'pathspec'" -allowed-tools: - - Bash - - Read ---- - -# Git / gh 操作スキル - -## 最重要ルール: CWD とパスの整合性 - -git コマンドはすべて **CWD からの相対パス** で解決される。 -操作前に必ず `pwd` で CWD を確認すること。 - -### パターン1: CWDがサブディレクトリの場合 - -``` -# CWD: /work/repo/lambda-batch/MyProject/ -# リポジトリルート: /work/repo/ - -# NG: リポジトリルートからのパスを指定 -git add lambda-batch/MyProject/src/foo.py -# => fatal: pathspec did not match any files - -# OK: CWDからの相対パスを指定 -git add src/foo.py - -# OK: 絶対パスを指定 -git add /work/repo/lambda-batch/MyProject/src/foo.py -``` - -### パターン2: 安全な方法 - -```bash -# 方法A: git -C でリポジトリルートを指定 -git -C /work/repo add lambda-batch/MyProject/src/foo.py - -# 方法B: CWD を変更せずに絶対パスを使用 -git add "$(git rev-parse --show-toplevel)/lambda-batch/MyProject/src/foo.py" - -# 方法C（推奨）: CWDからの相対パスを使用 -# まず pwd で確認してからパスを組み立てる -``` - -## git 操作チェックリスト - -### git add の前に - -1. `pwd` で CWD を確認 -2. `git status` で変更ファイルのパスを確認（表示されるパスはリポジトリルートからの相対パス） -3. `git status` の出力パスと CWD の関係を計算してから `git add` する - -### git commit の前に - -1. `git diff --cached` でステージング内容を確認 -2. HEREDOC形式でメッセージを渡す（改行・特殊文字の問題回避） - -```bash -git commit -m "$(cat <<'EOF' -コミットメッセージ - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -## gh CLI / GitHub API の注意点 - -### パラメータ: `-f` vs `-F` - -```bash -# -f: 文字列パラメータ -gh api repos/OWNER/REPO/pulls/PR/comments -f body="テキスト" - -# -F: 非文字列パラメータ（数値、boolean、null、ファイル） -gh api repos/OWNER/REPO/pulls/PR/comments -F in_reply_to=2826074026 - -# 混在OK -gh api repos/OWNER/REPO/pulls/PR/comments -f body="返信テキスト" -F in_reply_to=2826074026 -``` - -### PRレビューコメントの取得 - -```bash -# コメント一覧を取得（id, path, body の先頭を表示） -gh api repos/OWNER/REPO/pulls/PR/comments \ - --jq '.[] | {id: .id, path: .path, body: (.body | split("\n")[0][:80])}' -``` - -### PRレビューコメントへの返信 - -```bash -# NG: /replies エンドポイントは存在しない（404になる） -gh api repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# NG: -X POST を付けても同じ（エンドポイント自体が存在しない） -gh api -X POST repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# OK: in_reply_to パラメータを使って新規コメントとして投稿 -gh api repos/OWNER/REPO/pulls/PR/comments \ - -f body="返信テキスト" \ - -F in_reply_to=COMMENT_ID -``` - -### レビュースレッドの Resolve（GraphQL） - -```bash -# 1. 未解決スレッドのID一覧を取得 -gh api graphql -f query=' -query { - repository(owner: "OWNER", name: "REPO") { - pullRequest(number: PR) { - reviewThreads(first: 50) { - nodes { - id - isResolved - comments(first: 1) { - nodes { path body } - } - } - } - } - } -}' --jq '.data.repository.pullRequest.reviewThreads.nodes[] | select(.isResolved == false) | {id, path: .comments.nodes[0].path}' - -# 2. スレッドを Resolve -gh api graphql -f query=' -mutation { - resolveReviewThread(input: {threadId: "PRRT_xxx"}) { - thread { isResolved } - } -}' -``` - -### PR の CI チェック結果 - -`gh pr checks` は1つでもfailがあると **exit code 1** で終了する。 -Claude Codeではコマンド失敗と判定されて処理が止まるため、必ず `|| true` を付ける。 - -```bash -# NG: failがあるとexit code 1で止まる -gh pr checks PR --repo OWNER/REPO - -# OK: exit codeを常に0にして出力を取得 -gh pr checks PR --repo OWNER/REPO 2>&1 || true - -# OK: 失敗のみフィルタ -gh pr checks PR --repo OWNER/REPO 2>&1 | grep -i fail || true -``` - -#### 重要: CIの完了を待ってはいけない - -- `--watch` や完了までのポーリングは **禁止**。現在のステータスを一度スナップショットするだけでよい。 -- チェックが `in_progress` / `queued` / `pending` の場合は **完了を待たず次のステップへ進む**。 -- 対応対象は **コード修正で直せるfailのみ**。以下のような「ステータス確認系」チェックは無視する: - - `check_pr_requirements` 等、PR要件・メタ情報のみ検証するもの - - Lint/テストに非依存なラベル/タイトル/説明チェック - - 外部サービス起因で自己修復するトランジェントなfail（再実行で直るもの） -- 対応する: ビルド失敗・テスト失敗・型エラー・lint違反など、**リポジトリ内コードの修正で解消可能なもの**。 - -```bash -# 失敗ジョブのログ（エラー行のみ抽出） -gh run view RUN_ID --repo OWNER/REPO --log-failed 2>&1 \ - | grep -E '(FAIL|Error|Tests:)' | head -20 || true -``` - -### 自分のPRは Approve できない - -``` -# GitHub の制約: 自分で作成した PR に APPROVE レビューは不可 -# => "Can not approve your own pull request" -# 対策: event を "COMMENT" に変更して送信 -``` - -### PR作成時の body は HEREDOC - -```bash -# NG: \n がリテラルで混入する可能性 -gh pr create --title "タイトル" --body "行1\n行2" - -# OK: HEREDOC形式 -gh pr create --title "タイトル" --body "$(cat <<'EOF' -## Summary -- 変更内容 - -## Test plan -- [ ] テスト項目 -EOF -)" -``` - -## AWS CLI の注意点 - -### CloudWatch ログストリーム名の [$LATEST] - -```bash -# NG: --query で [$LATEST] を含む文字列がパースエラー -aws logs get-log-events --query 'events[*].message' --output text - -# OK: --output json にして python でパース -aws logs get-log-events --output json | python3 -c " -import sys,json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## エラー事例集 - -| エラーメッセージ | 原因 | 対策 | -|----------------|------|------| -| `fatal: pathspec '...' did not match any files` | CWD とパスの不一致 | `pwd` 確認後、CWD相対パスで指定 | -| `404 Not Found` (gh api replies) | `/comments/{id}/replies` は存在しない | `in_reply_to` パラメータで投稿 | -| `422 Unprocessable` (gh api) | `-f` で数値を渡した | 数値は `-F` を使う | -| `Can not approve your own pull request` | 自己 Approve 不可 | `COMMENT` イベントに変更 | -| `gh pr checks` が exit code 1 | 1つでもfailがあると非0終了 | `gh pr checks ... 2>&1 \|\| true` | -| `Unknown options: , , ,` (aws cli) | `[$LATEST]` のシェルエスケープ | `--output json` + python パース | - -## 詳細ガイド - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-common-errors.md` | 詳細なエラー事例と再現手順 | エラー発生時 | diff --git a/plugins/ndf-kiro/skills/logging-guidelines/SKILL.md b/plugins/ndf-kiro/skills/logging-guidelines/SKILL.md index 007b9691..3ad64b34 100644 --- a/plugins/ndf-kiro/skills/logging-guidelines/SKILL.md +++ b/plugins/ndf-kiro/skills/logging-guidelines/SKILL.md @@ -1,7 +1,19 @@ --- name: logging-guidelines -description: "Design safe and useful application logging." -when_to_use: "コードにログを追加・修正・整理するとき。Triggers: 'ログ追加', 'log追加', 'logger', 'logging', 'ログレベル', 'log level', 'デバッグログ', 'エラーログ', 'logger.info', 'logger.error', 'print文をログに'" +description: "Choose log levels and write safe, useful application logs when adding or reworking logging in code（ログ追加 / logger / ログレベル / デバッグログ / エラーログ / print文をログに）. Use when editing source code that emits logs, to pick the level and keep secrets and personal data out of the output." +paths: + - "**/*.py" + - "**/*.ts" + - "**/*.tsx" + - "**/*.js" + - "**/*.jsx" + - "**/*.go" + - "**/*.rb" + - "**/*.java" + - "**/*.kt" + - "**/*.php" + - "**/*.rs" + - "**/*.sh" --- # ログ運用ガイドライン diff --git a/plugins/ndf-kiro/skills/python-execution/01-uv-setup.md b/plugins/ndf-kiro/skills/python-execution/01-uv-setup.md deleted file mode 100644 index aafd4f1c..00000000 --- a/plugins/ndf-kiro/skills/python-execution/01-uv-setup.md +++ /dev/null @@ -1,85 +0,0 @@ -# uv詳細セットアップガイド - -> **Note**: 基本的な`uv sync`と`uv run python`はSKILL.mdを参照。このファイルは初回セットアップや詳細設定が必要な場合のみ参照。 - -## uvインストール - -```bash -# Linux/macOS -curl -LsSf https://astral.sh/uv/install.sh | sh - -# pip経由（代替） -pip install uv - -# 確認 -uv --version -``` - -## 依存関係管理 - -```bash -# uv.lockがある場合（推奨） -uv sync - -# uv.lockがない場合 -uv lock && uv sync - -# 開発用依存関係も含める -uv sync --dev - -# 特定のextraを含める -uv sync --extra test -``` - -## Pythonバージョン管理 - -```bash -# 特定バージョンをインストール -uv python install 3.12 -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.12 - -# インストール済みバージョン一覧 -uv python list -``` - -## 実行オプション - -```bash -# スクリプト実行 -uv run python script.py - -# モジュール実行 -uv run python -m pytest -uv run python -m mypy . - -# 引数付き -uv run python script.py --arg value - -# インタラクティブシェル -uv run python -``` - -## プロジェクト初期化（新規作成時） - -```bash -# 新規プロジェクト作成 -uv init my-project -cd my-project - -# 依存関係追加 -uv add requests -uv add --dev pytest - -# ロックファイル生成 -uv lock -``` - -## uv環境の利点 - -- **高速**: Rustで実装、pip比10-100倍速 -- **再現性**: uv.lockで完全な依存関係固定 -- **Pythonバージョン管理**: pyenvなしでバージョン切り替え -- **グローバル環境を汚染しない**: プロジェクト単位で隔離 diff --git a/plugins/ndf-kiro/skills/python-execution/02-troubleshooting.md b/plugins/ndf-kiro/skills/python-execution/02-troubleshooting.md deleted file mode 100644 index 3475b7ec..00000000 --- a/plugins/ndf-kiro/skills/python-execution/02-troubleshooting.md +++ /dev/null @@ -1,112 +0,0 @@ -# Python実行 トラブルシューティング - -## よくある問題と解決策 - -### Q: `uv: command not found` - -**原因**: uvがインストールされていない - -**解決策**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -# シェルを再起動するか、パスを通す -source ~/.bashrc # または ~/.zshrc -``` - -### Q: `ModuleNotFoundError` - -**原因**: 依存関係がインストールされていない - -**解決策**: -```bash -# uv環境の場合 -uv sync - -# venv環境の場合 -.venv/bin/pip install -r requirements.txt - -# pyproject.tomlがある場合 -.venv/bin/pip install -e . -``` - -### Q: `python: command not found` - -**原因**: Pythonがインストールされていない、またはパスが通っていない - -**解決策**: -```bash -# python3を試す -python3 --version - -# uvでPythonをインストール -uv python install 3.12 -``` - -### Q: 異なるPythonバージョンが必要 - -**解決策（uv環境）**: -```bash -# 特定バージョンをインストール -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.11 - -# そのバージョンで実行 -uv run python script.py -``` - -### Q: `pyproject.toml`はあるが`uv.lock`がない - -**解決策**: -```bash -# ロックファイルを生成 -uv lock - -# 依存関係をインストール -uv sync -``` - -### Q: 仮想環境が壊れている - -**解決策**: -```bash -# 仮想環境を削除して再作成 -rm -rf .venv - -# uv環境の場合 -uv sync - -# 手動で作成する場合 -python3 -m venv .venv -.venv/bin/pip install -r requirements.txt -``` - -### Q: パーミッションエラー - -**解決策**: -```bash -# 仮想環境を使用（推奨） -uv sync -uv run python script.py - -# どうしてもグローバルにインストールする場合（非推奨） -pip install --user package_name -``` - -### Q: SSL証明書エラー - -**解決策**: -```bash -# macOSの場合 -/Applications/Python\ 3.x/Install\ Certificates.command - -# または環境変数で一時的に無効化（非推奨） -export PYTHONHTTPSVERIFY=0 -``` - -## 関連リソース - -- [uv公式ドキュメント](https://docs.astral.sh/uv/) -- [Python venv](https://docs.python.org/3/library/venv.html) -- [pyproject.toml仕様](https://packaging.python.org/en/latest/specifications/pyproject-toml/) diff --git a/plugins/ndf-kiro/skills/python-execution/SKILL.md b/plugins/ndf-kiro/skills/python-execution/SKILL.md deleted file mode 100644 index a07705aa..00000000 --- a/plugins/ndf-kiro/skills/python-execution/SKILL.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -name: python-execution -description: "Detect and run the right Python environment." -when_to_use: "Python スクリプトを実行 / セットアップするとき。Triggers: 'python', 'uv', 'スクリプト', 'python環境'" -allowed-tools: - - Read - - Bash - - Glob ---- - -# Python Execution Skill - -## 概要 - -Pythonコードを実行する前に、プロジェクトの実行環境を調査し、適切な方法で実行するためのガイドラインです。 - -## Step 1: 環境検出 - -```bash -ls -la pyproject.toml uv.lock .venv/ venv/ requirements.txt 2>/dev/null -``` - -## Step 2: 実行コマンド選択 - -| 検出ファイル | 実行方法 | 優先度 | -|-------------|---------|-------| -| `pyproject.toml` | `uv run python` | 最高 | -| `.venv/` | `.venv/bin/python` | 中 | -| `venv/` | `venv/bin/python` | 中 | -| 何もなし | `python3` | 最低 | - -## Step 3: 実行 - -### uv環境（pyproject.tomlあり） - -```bash -# 依存関係インストール（初回のみ） -uv sync - -# 実行 -uv run python script.py -uv run python -m module_name -``` - -**uvがない場合のインストール**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -source ~/.bashrc # パスを反映 -``` - -### venv環境（.venv/あり） - -```bash -# 依存関係インストール（初回のみ） -.venv/bin/pip install -r requirements.txt - -# 実行 -.venv/bin/python script.py -``` - -### システムPython - -```bash -python3 script.py -``` - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| 実行前に環境を調査 | 環境を確認せずに実行 | -| README.md/CLAUDE.mdの指示を優先 | グローバル環境に依存関係をインストール | -| pyproject.tomlがあればuv使用 | source activateに依存 | -| 仮想環境のPythonをパス指定で実行 | python2を使用 | - -## 詳細ガイド（必要時のみ参照） - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-uv-setup.md` | uv詳細セットアップ、Pythonバージョン管理 | 初回セットアップ時 | -| `02-troubleshooting.md` | エラー解決策 | 問題発生時 | - -## 関連Skill - -- **corder-code-templates**: Pythonコードテンプレート -- **corder-test-generation**: Pythonテスト生成 diff --git a/plugins/ndf-shared/manifests/claude-skills.txt b/plugins/ndf-shared/manifests/claude-skills.txt index bb74879b..068c05d0 100644 --- a/plugins/ndf-shared/manifests/claude-skills.txt +++ b/plugins/ndf-shared/manifests/claude-skills.txt @@ -7,9 +7,7 @@ merged clean ndf-policies markdown-writing -python-execution docker-container-access -git-gh-operations branch-fix-strategy implementation-plan investigation-rules diff --git a/plugins/ndf-shared/manifests/codex-skills.txt b/plugins/ndf-shared/manifests/codex-skills.txt index 403632f8..dea561e8 100644 --- a/plugins/ndf-shared/manifests/codex-skills.txt +++ b/plugins/ndf-shared/manifests/codex-skills.txt @@ -5,7 +5,6 @@ cross-review deploy docker-container-access fix -git-gh-operations implementation-plan investigation-rules issue-plan-strategy @@ -22,7 +21,6 @@ playwright-test-planning pr pr-tests problem-solving -python-execution resolve-pr-comments review review-branch diff --git a/plugins/ndf-shared/manifests/kiro-skills.txt b/plugins/ndf-shared/manifests/kiro-skills.txt index b58dc6bd..14216801 100644 --- a/plugins/ndf-shared/manifests/kiro-skills.txt +++ b/plugins/ndf-shared/manifests/kiro-skills.txt @@ -7,9 +7,7 @@ merged clean ndf-policies markdown-writing -python-execution docker-container-access -git-gh-operations branch-fix-strategy implementation-plan investigation-rules diff --git a/plugins/ndf-shared/skills/data-analyst-export/01-formats.md b/plugins/ndf-shared/skills/data-analyst-export/01-formats.md deleted file mode 100644 index b30fc41f..00000000 --- a/plugins/ndf-shared/skills/data-analyst-export/01-formats.md +++ /dev/null @@ -1,143 +0,0 @@ -# エクスポート形式詳細 - -## CSV出力 - -### export-csv.js - -JSON配列をCSV形式に変換します。 - -**機能**: -- 自動ヘッダー生成 -- カスタムデリミタ（カンマ、タブ、セミコロン） -- 引用符エスケープ -- UTF-8 BOM対応（Excel互換） - -**使用例**: -```bash -# 基本的な使用 -node export-csv.js data.json output.csv - -# タブ区切り -node export-csv.js data.json output.tsv --delimiter="\t" - -# Excel互換（BOM付き） -node export-csv.js data.json output.csv --bom -``` - -**入力データ形式** (input.json): -```json -[ - {"id": 1, "name": "Product A", "price": 1000}, - {"id": 2, "name": "Product B", "price": 2000} -] -``` - -**出力** (output.csv): -```csv -id,name,price -1,Product A,1000 -2,Product B,2000 -``` - -## JSON出力 - -### export-json.js - -CSV/配列データをJSON形式に変換します。 - -**機能**: -- Pretty-print（整形出力） -- 圧縮出力 -- 配列またはオブジェクト形式 - -**使用例**: -```bash -# Pretty-print -node export-json.js data.csv output.json --pretty - -# 圧縮 -node export-json.js data.csv output.json --compact -``` - -## Excel出力 - -### export-excel.js - -JSON配列をExcelファイル(.xlsx)に変換します。 - -**機能**: -- 複数シート作成 -- ヘッダー書式設定 -- セルの書式設定（数値、通貨、日付） -- 列幅自動調整 - -**使用例**: -```bash -# 単一シート -node export-excel.js data.json output.xlsx - -# 複数シート（各シートのデータはdata.jsonに含む） -node export-excel.js multi-sheet-data.json output.xlsx -``` - -**multi-sheet-data.json の形式**: -```json -{ - "Sheet1": [ - {"id": 1, "name": "Item 1"} - ], - "Sheet2": [ - {"id": 2, "name": "Item 2"} - ] -} -``` - -## Markdownテーブル出力 - -### export-markdown.js - -JSON配列をMarkdownテーブルに変換します。 - -**機能**: -- GitHub Flavored Markdown形式 -- 列の自動整列 -- 見出し行の区切り - -**使用例**: -```bash -node export-markdown.js data.json output.md -``` - -**出力例**: -```markdown -| id | name | price | -|----|------|-------| -| 1 | Product A | 1000 | -| 2 | Product B | 2000 | -``` - -## トラブルシューティング - -### Q: Excelで日本語が文字化けする - -A: UTF-8 BOMを付与してください: -```bash -node export-csv.js data.json output.csv --bom -``` - -### Q: 大きなデータでメモリ不足 - -A: ストリーミング処理に変更: -```javascript -// ストリーミング版のスクリプトを使用 -node export-csv-stream.js large-data.json output.csv -``` - -### Q: Excelの行数制限（1048576行）を超える - -A: 複数ファイルに分割: -```javascript -// 100万行ごとに分割 -node export-excel.js data.json output --split 1000000 -// output-1.xlsx, output-2.xlsx, ... が生成される -``` diff --git a/plugins/ndf-shared/skills/data-analyst-export/02-examples.md b/plugins/ndf-shared/skills/data-analyst-export/02-examples.md deleted file mode 100644 index 3ec07abe..00000000 --- a/plugins/ndf-shared/skills/data-analyst-export/02-examples.md +++ /dev/null @@ -1,116 +0,0 @@ -# エクスポート実装例 - -## 例1: BigQueryクエリ結果をCSVにエクスポート - -```javascript -// クエリ実行（BigQuery MCP使用） -const results = await bigquery.query("SELECT * FROM dataset.table LIMIT 1000"); - -// CSVに変換 -const fs = require('fs'); -const exportCSV = require('./scripts/export-csv.js'); - -exportCSV(results, 'query-results.csv'); -console.log('✅ CSVにエクスポート完了: query-results.csv'); -``` - -## 例2: 分析結果をExcelレポートに出力 - -```javascript -// 複数の分析結果を取得 -const salesData = await bigquery.query("SELECT * FROM sales"); -const productData = await bigquery.query("SELECT * FROM products"); - -// 複数シートでExcel出力 -const data = { - "Sales": salesData, - "Products": productData -}; - -exportExcel(data, 'monthly-report.xlsx'); -console.log('✅ Excelレポート作成完了: monthly-report.xlsx'); -``` - -## 例3: ドキュメントにMarkdownテーブル挿入 - -```javascript -// クエリ結果を取得 -const topProducts = await bigquery.query( - "SELECT name, sales FROM products ORDER BY sales DESC LIMIT 10" -); - -// Markdownテーブルに変換 -const markdown = exportMarkdown(topProducts); - -// README.mdに挿入 -const fs = require('fs'); -const readme = fs.readFileSync('README.md', 'utf-8'); -const updatedReadme = readme.replace('', markdown); -fs.writeFileSync('README.md', updatedReadme); - -console.log('✅ README.mdにテーブルを挿入しました'); -``` - -## 例4: 日付付きファイル名でエクスポート - -```javascript -const today = new Date().toISOString().split('T')[0]; - -// 日付付きファイル名 -exportCSV(data, `sales-${today}.csv`); -exportExcel(data, `report-${today}.xlsx`); -``` - -## 例5: 大容量データの分割エクスポート - -```javascript -const CHUNK_SIZE = 100000; - -async function exportLargeData(query, outputPrefix) { - let offset = 0; - let fileIndex = 1; - let hasMore = true; - - while (hasMore) { - const results = await bigquery.query( - `${query} LIMIT ${CHUNK_SIZE} OFFSET ${offset}` - ); - - if (results.length === 0) { - hasMore = false; - break; - } - - const filename = `${outputPrefix}-${fileIndex}.csv`; - exportCSV(results, filename); - console.log(`✅ ${filename} 作成完了 (${results.length}行)`); - - offset += CHUNK_SIZE; - fileIndex++; - } - - console.log(`✅ 全${fileIndex - 1}ファイル作成完了`); -} - -// 使用 -await exportLargeData( - "SELECT * FROM large_table", - "output/large-data" -); -``` - -## 例6: フィルタ付きエクスポート - -```javascript -// 特定の条件でフィルタしてエクスポート -const results = await bigquery.query(` - SELECT * - FROM sales - WHERE date >= '2024-01-01' - AND region = 'APAC' -`); - -// ファイル名に条件を含める -exportCSV(results, 'sales-2024-apac.csv'); -exportJSON(results, 'sales-2024-apac.json', { pretty: true }); -``` diff --git a/plugins/ndf-shared/skills/data-analyst-export/SKILL.md b/plugins/ndf-shared/skills/data-analyst-export/SKILL.md deleted file mode 100644 index 59e6a2d8..00000000 --- a/plugins/ndf-shared/skills/data-analyst-export/SKILL.md +++ /dev/null @@ -1,64 +0,0 @@ ---- -name: data-analyst-export -description: "Export analysis results to CSV, JSON, Excel, or Markdown." -when_to_use: "Use when saving analysis results to files. Triggers: 'export data', 'save results', 'output CSV', 'output JSON', 'output Excel', 'データ出力', '結果保存', 'エクスポート'" -allowed-tools: - - Write - - Bash ---- - -# Data Analyst Export Skill - -## 概要 - -data-analystエージェントがクエリ結果を様々な形式でエクスポートする際に使用します。CSV、JSON、Excel、Markdownテーブルなど、用途に応じた最適な形式で出力できます。 - -## クイックリファレンス - -### 形式別コマンド - -```bash -# CSV出力 -node scripts/export-csv.js input.json output.csv - -# JSON出力 -node scripts/export-json.js input.csv output.json --pretty - -# Excel出力 -node scripts/export-excel.js input.json output.xlsx - -# Markdownテーブル出力 -node scripts/export-markdown.js input.json output.md -``` - -### 形式選択ガイド - -| 形式 | 用途 | -|------|------| -| CSV | 単純なデータ、他システム連携 | -| JSON | API連携、構造化データ | -| Excel | 複雑なレポート、書式設定 | -| Markdown | ドキュメント埋め込み | - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| 適切な形式を選択 | 全データをメモリに展開 | -| ヘッダーを含める | Excel形式で巨大データ（100万行制限） | -| 大きなデータはストリーミング | 日付フォーマットの不統一 | -| UTF-8 BOMを付与（Excel用CSV） | 特殊文字のエスケープ忘れ | - -## 詳細ガイド - -| ファイル | 内容 | -|---------|------| -| `01-formats.md` | 各形式のスクリプト詳細、オプション | -| `02-examples.md` | BigQuery結果のCSVエクスポート、Excelレポート作成の実例 | - -## 関連リソース - -- **scripts/export-csv.js**: CSV出力スクリプト -- **scripts/export-json.js**: JSON出力スクリプト -- **scripts/export-excel.js**: Excel出力スクリプト -- **scripts/export-markdown.js**: Markdownテーブル出力スクリプト diff --git a/plugins/ndf-shared/skills/data-analyst-export/scripts/export-csv.js b/plugins/ndf-shared/skills/data-analyst-export/scripts/export-csv.js deleted file mode 100755 index 2a284bc1..00000000 --- a/plugins/ndf-shared/skills/data-analyst-export/scripts/export-csv.js +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env node - -/** - * JSON配列をCSV形式に変換するスクリプト - * - * 使用方法: - * node export-csv.js input.json output.csv [options] - * - * オプション: - * --delimiter="," : デリミタ（デフォルト: カンマ） - * --bom : UTF-8 BOMを付与（Excel互換） - */ - -const fs = require('fs'); - -/** - * JSON配列をCSV文字列に変換 - */ -function jsonToCSV(data, options = {}) { - if (!Array.isArray(data) || data.length === 0) { - throw new Error('データは空でない配列である必要があります'); - } - - const delimiter = options.delimiter || ','; - const includeHeaders = options.includeHeaders !== false; - - // ヘッダー行を生成 - const headers = Object.keys(data[0]); - const headerLine = headers.map(h => escapeCSVValue(h, delimiter)).join(delimiter); - - // データ行を生成 - const dataLines = data.map(row => { - return headers.map(header => { - const value = row[header]; - return escapeCSVValue(value, delimiter); - }).join(delimiter); - }); - - // ヘッダーとデータを結合 - const lines = includeHeaders ? [headerLine, ...dataLines] : dataLines; - return lines.join('\n'); -} - -/** - * CSV値をエスケープ - */ -function escapeCSVValue(value, delimiter) { - if (value === null || value === undefined) { - return ''; - } - - const stringValue = String(value); - - // デリミタ、改行、ダブルクォートを含む場合はクォートで囲む - const needsQuoting = stringValue.includes(delimiter) || - stringValue.includes('\n') || - stringValue.includes('\r') || - stringValue.includes('"'); - - if (needsQuoting) { - // ダブルクォートをエスケープ（""に変換） - const escapedValue = stringValue.replace(/"/g, '""'); - return `"${escapedValue}"`; - } - - return stringValue; -} - -/** - * CSVファイルに書き込み - */ -function writeCSV(csv, outputPath, options = {}) { - let content = csv; - - // UTF-8 BOMを付与（Excel互換） - if (options.bom) { - content = '\uFEFF' + content; - } - - fs.writeFileSync(outputPath, content, 'utf-8'); -} - -/** - * メイン処理 - */ -function main() { - const args = process.argv.slice(2); - - if (args.length < 2) { - console.error('使用方法: node export-csv.js [options]'); - console.error(''); - console.error('オプション:'); - console.error(' --delimiter="," : デリミタ（デフォルト: カンマ）'); - console.error(' --bom : UTF-8 BOMを付与（Excel互換）'); - console.error(''); - console.error('例:'); - console.error(' node export-csv.js data.json output.csv'); - console.error(' node export-csv.js data.json output.tsv --delimiter="\\t"'); - console.error(' node export-csv.js data.json output.csv --bom'); - process.exit(1); - } - - const inputPath = args[0]; - const outputPath = args[1]; - - // オプションを解析 - const options = {}; - for (let i = 2; i < args.length; i++) { - const arg = args[i]; - if (arg.startsWith('--delimiter=')) { - options.delimiter = arg.split('=')[1]; - // エスケープシーケンスを処理 - options.delimiter = options.delimiter.replace(/\\t/g, '\t'); - } else if (arg === '--bom') { - options.bom = true; - } - } - - try { - // JSONファイルを読み込み - console.log(`📖 JSONファイルを読み込み中: ${inputPath}`); - const jsonData = fs.readFileSync(inputPath, 'utf-8'); - const data = JSON.parse(jsonData); - - // CSVに変換 - console.log('🔄 CSVに変換中...'); - const csv = jsonToCSV(data, options); - - // ファイルに書き込み - console.log(`💾 CSVファイルを保存中: ${outputPath}`); - writeCSV(csv, outputPath, options); - - console.log(''); - console.log('✅ CSVエクスポート完了'); - console.log(` 入力: ${inputPath}`); - console.log(` 出力: ${outputPath}`); - console.log(` 行数: ${data.length}行`); - console.log(` 列数: ${Object.keys(data[0] || {}).length}列`); - - } catch (error) { - console.error('❌ エラーが発生しました:', error.message); - process.exit(1); - } -} - -// モジュールとしてエクスポート（他のスクリプトから使用可能） -if (require.main === module) { - main(); -} else { - module.exports = { jsonToCSV, escapeCSVValue, writeCSV }; -} diff --git a/plugins/ndf-shared/skills/data-analyst-sql-optimization/01-patterns.md b/plugins/ndf-shared/skills/data-analyst-sql-optimization/01-patterns.md deleted file mode 100644 index f179b8be..00000000 --- a/plugins/ndf-shared/skills/data-analyst-sql-optimization/01-patterns.md +++ /dev/null @@ -1,145 +0,0 @@ -# SQL最適化パターン詳細 - -## 1. N+1クエリ削減 - -**問題**: ループ内で繰り返しSELECT文を実行 - -**解決**: JOINまたはサブクエリで1回のクエリに統合 - -```sql --- ❌ Bad: ループで実行（N+1クエリ） -SELECT * FROM users WHERE id = ?; -- N回実行 - --- ✅ Good: 1回のクエリで取得 -SELECT u.*, o.order_count -FROM users u -LEFT JOIN ( - SELECT user_id, COUNT(*) as order_count - FROM orders - GROUP BY user_id -) o ON u.id = o.user_id; -``` - -## 2. インデックス活用 - -**問題**: WHERE句の列にインデックスがない - -**解決**: 適切なインデックスを作成 - -```sql --- インデックス作成 -CREATE INDEX idx_orders_status_created ON orders(status, created_at); - --- ✅ インデックス効率化のためWHERE句の順序を調整 -SELECT * FROM orders -WHERE status = 'completed' -AND created_at > '2023-01-01'; -``` - -**インデックス作成の指針**: -- WHERE句で頻繁に使用される列 -- JOIN条件の列 -- ORDER BY句の列 -- カーディナリティ（値の多様性）が高い列 - -## 3. JOIN最適化 - -**問題**: 不要な大規模テーブルのJOIN - -**解決**: 必要な列のみ取得、結合順序の最適化 - -```sql --- ❌ Bad: SELECT * -SELECT * FROM orders o -JOIN products p ON o.product_id = p.id -JOIN users u ON o.user_id = u.id; - --- ✅ Good: 必要な列のみ -SELECT o.id, o.total, p.name, u.email -FROM orders o -JOIN products p ON o.product_id = p.id -JOIN users u ON o.user_id = u.id; -``` - -## 4. ウィンドウ関数活用 - -**問題**: 複雑なサブクエリの入れ子 - -**解決**: ROW_NUMBER(), RANK()等のウィンドウ関数を使用 - -```sql --- ❌ Bad: サブクエリの入れ子 -SELECT u.name, - (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) as order_count, - (SELECT SUM(total) FROM orders o WHERE o.user_id = u.id) as order_total -FROM users u; - --- ✅ Good: ウィンドウ関数で1回のスキャン -SELECT DISTINCT u.name, - COUNT(o.id) OVER (PARTITION BY u.id) as order_count, - SUM(o.total) OVER (PARTITION BY u.id) as order_total -FROM users u -LEFT JOIN orders o ON u.id = o.user_id; -``` - -## 5. EXISTS vs IN - -**問題**: サブクエリでINを使用 - -**解決**: EXISTSに変更（多くの場合高速） - -```sql --- ❌ 遅い場合がある -SELECT * FROM users -WHERE id IN (SELECT user_id FROM orders WHERE total > 1000); - --- ✅ 通常より高速 -SELECT * FROM users u -WHERE EXISTS ( - SELECT 1 FROM orders o - WHERE o.user_id = u.id AND o.total > 1000 -); -``` - -## 6. LIMIT活用 - -**問題**: 全件取得後にアプリ側でフィルタ - -**解決**: SQLでLIMIT/OFFSETを使用 - -```sql --- ✅ ページング -SELECT * FROM orders -ORDER BY created_at DESC -LIMIT 20 OFFSET 0; -- 1ページ目 -``` - -## 7. 計算列のインデックス - -**問題**: WHERE句で関数を列に適用 - -**解決**: 計算済み列を作成してインデックス - -```sql --- ❌ Bad: 関数適用でインデックス無効 -SELECT * FROM users WHERE YEAR(created_at) = 2024; - --- ✅ Good: 範囲検索でインデックス有効 -SELECT * FROM users -WHERE created_at >= '2024-01-01' -AND created_at < '2025-01-01'; -``` - -## パフォーマンス測定 - -### 改善前後の比較 - -1. **実行時間測定** -2. **スキャンバイト数確認**（BigQuery） -3. **実行計画比較**: `EXPLAIN SELECT ...;` - -### 目標指標 - -- **実行時間**: 50%以上削減 -- **スキャンバイト数**: 70%以上削減 -- **インデックス使用**: EXPLAINでtype=ref以上 diff --git a/plugins/ndf-shared/skills/data-analyst-sql-optimization/02-examples.md b/plugins/ndf-shared/skills/data-analyst-sql-optimization/02-examples.md deleted file mode 100644 index ddfc6364..00000000 --- a/plugins/ndf-shared/skills/data-analyst-sql-optimization/02-examples.md +++ /dev/null @@ -1,136 +0,0 @@ -# SQL最適化 Before/After実例集 - -## 例1: N+1クエリの削減 - -**Before**: -```sql --- ループで実行（N+1クエリ） -SELECT * FROM users WHERE id = ?; -- N回実行 -``` - -**After**: -```sql --- 1回のクエリで取得 -SELECT u.*, o.order_count -FROM users u -LEFT JOIN ( - SELECT user_id, COUNT(*) as order_count - FROM orders - GROUP BY user_id -) o ON u.id = o.user_id; -``` - -**改善**: N+1回 → 1回のクエリ、大幅な高速化 - -## 例2: インデックス活用 - -**Before**: -```sql -SELECT * FROM orders -WHERE created_at > '2023-01-01' -AND status = 'completed'; --- インデックスなし、フルスキャン -``` - -**After**: -```sql --- インデックス作成 -CREATE INDEX idx_orders_status_created ON orders(status, created_at); - --- 同じクエリがインデックスを使用 -SELECT * FROM orders -WHERE status = 'completed' -AND created_at > '2023-01-01'; --- ORDER BY の順序を逆にしてインデックス効率化 -``` - -**改善**: フルスキャン → インデックススキャン、10倍以上高速化 - -## 例3: ウィンドウ関数活用 - -**Before**: -```sql --- サブクエリの入れ子 -SELECT u.name, - (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) as order_count, - (SELECT SUM(total) FROM orders o WHERE o.user_id = u.id) as order_total -FROM users u; --- usersの各行でordersを2回スキャン -``` - -**After**: -```sql --- ウィンドウ関数で1回のスキャン -SELECT u.name, - COUNT(o.id) OVER (PARTITION BY u.id) as order_count, - SUM(o.total) OVER (PARTITION BY u.id) as order_total -FROM users u -LEFT JOIN orders o ON u.id = o.user_id; --- 1回のJOINで完結 -``` - -**改善**: 2N回スキャン → 1回のJOIN、大幅な高速化 - -## 例4: 不要なDISTINCTの削除 - -**Before**: -```sql -SELECT DISTINCT u.id, u.name -FROM users u -JOIN orders o ON u.id = o.user_id; --- DISTINCTで重複排除（コスト高） -``` - -**After**: -```sql -SELECT u.id, u.name -FROM users u -WHERE EXISTS ( - SELECT 1 FROM orders o WHERE o.user_id = u.id -); --- DISTINCTなしで同じ結果 -``` - -## 例5: BigQuery向け最適化 - -**Before**: -```sql -SELECT * -FROM `project.dataset.large_table` -WHERE DATE(timestamp) = '2024-01-15'; --- フルスキャン（高コスト） -``` - -**After**: -```sql -SELECT * -FROM `project.dataset.large_table` -WHERE timestamp >= '2024-01-15' -AND timestamp < '2024-01-16'; --- パーティション利用（低コスト） -``` - -**改善**: パーティションプルーニングで90%以上のコスト削減 - -## トラブルシューティング - -### Q: 最適化したのに遅い - -A: 以下を確認: -- インデックスが実際に使用されているか（EXPLAIN確認） -- 統計情報が最新か（ANALYZE TABLE実行） -- データ量が想定通りか - -### Q: どのパターンを適用すべきか分からない - -A: 以下の順で確認: -1. EXPLAINで実行計画を確認 -2. フルスキャンがあればインデックス作成 -3. N+1パターンがあればJOINに統合 -4. サブクエリが複雑ならウィンドウ関数検討 - -### Q: インデックスを作成したら書き込みが遅くなった - -A: インデックスの見直しが必要: -- 使用頻度の低いインデックスを削除 -- 複合インデックスで統合できないか検討 diff --git a/plugins/ndf-shared/skills/data-analyst-sql-optimization/SKILL.md b/plugins/ndf-shared/skills/data-analyst-sql-optimization/SKILL.md deleted file mode 100644 index 740130c4..00000000 --- a/plugins/ndf-shared/skills/data-analyst-sql-optimization/SKILL.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -name: data-analyst-sql-optimization -description: "Optimize SQL queries and slow database workloads." -when_to_use: "Use when improving query performance or analyzing slow queries. Triggers: 'optimize SQL', 'slow query', 'improve performance', 'SQL最適化', 'クエリ改善', 'パフォーマンス向上'" ---- - -# Data Analyst SQL Optimization Skill - -## 概要 - -data-analystエージェントがSQLクエリのパフォーマンスを改善する際に使用します。実績のある最適化パターンとベストプラクティスを提供します。 - -## クイックリファレンス - -### 最適化パターン一覧 - -| # | パターン | 問題 | 解決策 | -|---|---------|------|--------| -| 1 | N+1削減 | ループ内SQL | JOINで1回に統合 | -| 2 | インデックス | フルスキャン | WHERE/JOIN列にインデックス | -| 3 | JOIN最適化 | 不要な大規模JOIN | 必要な列のみ取得 | -| 4 | ウィンドウ関数 | 複雑なサブクエリ | ROW_NUMBER(), RANK()使用 | -| 5 | EXISTS vs IN | 遅いIN句 | EXISTSに変更 | -| 6 | LIMIT活用 | 全件取得 | SQLでページング | - -### 基本的な使い方 - -1. 遅いクエリを特定 -2. EXPLAINで実行計画を確認 -3. 該当する最適化パターンを適用 -4. 再度EXPLAINで改善を確認 - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| EXPLAINで実行計画を確認 | 不要なDISTINCT | -| インデックスは選択的に作成 | 関数をWHERE句の列に適用 | -| 必要な列のみSELECT | 過剰なJOIN | -| 早期フィルタリング | サブクエリの多用 | -| 統計情報を更新 | インデックスの作り過ぎ | - -## 詳細ガイド - -| ファイル | 内容 | -|---------|------| -| `01-patterns.md` | 各最適化パターンの詳細説明 | -| `02-examples.md` | Before/After実例集 | diff --git a/plugins/ndf-shared/skills/deepwiki-transfer/SKILL.md b/plugins/ndf-shared/skills/deepwiki-transfer/SKILL.md deleted file mode 100644 index c2933b7c..00000000 --- a/plugins/ndf-shared/skills/deepwiki-transfer/SKILL.md +++ /dev/null @@ -1,144 +0,0 @@ ---- -name: deepwiki-transfer -description: "Transfer DeepWiki content into Markdown docs." -when_to_use: "DeepWiki から Markdown としてコンテンツを取得・転載したいとき。Triggers: 'deepwiki transfer', 'deepwiki転載', 'wiki転載', 'リポジトリドキュメント取得', 'DeepWikiからMarkdown', 'transfer wiki contents'" -disable-model-invocation: true -allowed-tools: - - Bash - - Read - - Write - - Edit - - Task ---- - -# DeepWiki コンテンツ転載スキル - -DeepWikiのドキュメントをリポジトリ内のディレクトリに1ページ1ファイルのMarkdownとして転載し、PRを作成する。 - -## 必要な入力 - -- **対象リポジトリ**: `owner/repo` 形式（例: `facebook/react`） -- **出力先ディレクトリ**: リポジトリ内のパス（例: `deepWiki/`） -- **出力言語**: ファイル名および内容の出力言語（デフォルト: 日本語） -- **ベースブランチ**: PRのマージ先（デフォルト: `main`） - -## スクリプトのパス - -本スキルのスクリプトは `$CLAUDE_PLUGIN_ROOT/skills/deepwiki-transfer/scripts/` に配置されている。 - -```bash -# スクリプトディレクトリの確認 -SCRIPT_DIR="$CLAUDE_PLUGIN_ROOT/skills/deepwiki-transfer/scripts" -ls "$SCRIPT_DIR" -``` - -## 処理手順 - -### Phase A: DeepWikiコンテンツの取得とファイル化 - -`scripts/fetch_wiki.py` を使って MCP サーバーに直接HTTPリクエストを送信し、全コンテンツを一時ファイルに保存する。LLMコンテキストを経由しないため、130万文字超のレスポンスも完全に保存できる。 - -```bash -# 公開リポジトリ（認証不要） -python "$SCRIPT_DIR/fetch_wiki.py" \ - --repo owner/repo \ - --output /tmp/deepwiki_raw.md \ - --public - -# プライベートリポジトリ（Devin APIキー必要） -python "$SCRIPT_DIR/fetch_wiki.py" \ - --repo owner/repo \ - --output /tmp/deepwiki_raw.md - -# Wiki構造も取得（セクション順序の決定に使用） -python "$SCRIPT_DIR/fetch_wiki.py" \ - --repo owner/repo \ - --output /tmp/deepwiki_structure.md \ - --tool read_wiki_structure \ - --public -``` - -プライベートリポジトリの場合、環境変数 `DEVIN_API_KEY` にDevin APIキーを設定しておくこと。 - -### Phase B: 機械的ファイル分割＆リネーム - -`scripts/split_pages.py` を使い、一時ファイルを `# Page:` マーカーで分割し、セクション番号prefix付きファイル名で出力する。 - -```bash -# まず dry-run で確認 -python "$SCRIPT_DIR/split_pages.py" \ - /tmp/deepwiki_raw.md ./deepWiki/ \ - --structure /tmp/deepwiki_structure.md \ - --dry-run - -# 問題なければ実行 -python "$SCRIPT_DIR/split_pages.py" \ - /tmp/deepwiki_raw.md ./deepWiki/ \ - --structure /tmp/deepwiki_structure.md -``` - -ファイル名の命名規則: -- トップレベル: `XX_Title.md`（例: `01_Overview.md`） -- サブセクション: `XX_Y_Title.md`（例: `01_1_System_Architecture.md`） -- ソート順がセクション順と一致すること - -**この時点では内容を一切変更しない。** - -### Phase C: 1ページずつGFM形式変換 - -`scripts/validate_gfm.py` で自動修正した後、各ファイルを1ページずつ確認・修正する。 - -```bash -# 自動修正 -python "$SCRIPT_DIR/validate_gfm.py" ./deepWiki/ --verbose - -# 検証のみ -python "$SCRIPT_DIR/validate_gfm.py" ./deepWiki/ --check-only -``` - -自動修正の範囲: -- コードブロックの言語指定（``` → ```mermaid, ```php, ```sql 等） -- 見出し前後の空行挿入 -- 末尾改行の統一 - -自動修正後、各ファイルを1ページずつ開いて以下を確認: -- コードブロックの言語指定が正しいか（自動推定が誤っている場合は手動修正） -- テーブルのパイプ区切りやアライメント行が正しいか -- リンクや画像参照の構文が正しいか -- 見出しの階層が適切か - -**内容の意味を変えず、GFM上の記法修正のみ行う。言語変換はこのフェーズでは行わない。** - -### Phase D: 1ページずつ言語変換（出力言語が原文と異なる場合のみ） - -ユーザー指定の出力言語が原文の言語と異なる場合、各ファイルを1ページずつ処理する: - -1. **ファイル名**: タイトル部分を指定言語に翻訳してリネーム（prefixの `XX_` / `XX_Y_` は変更しない） -2. **ファイル内容**: 本文を指定言語に翻訳する。コードブロック内のコード、コマンド、変数名等は翻訳しない - -### Phase E: バリデーションとPR作成 - -1. 出力ディレクトリの `ls` でファイル一覧を表示し、ファイル数とセクション番号の整合性を確認 -2. 数件をランダムに選び、DeepWiki原文と照合して内容が一致していることを確認 -3. 変更をコミットしてプッシュ -4. PRを作成する。`.github/pull_request_template.md` がある場合はそのテンプレートに沿ってPR説明を作成 - -## 前提条件 - -- `requests` ライブラリ（`pip install requests`） -- 公開リポジトリ: 認証不要 -- プライベートリポジトリ: 環境変数 `DEVIN_API_KEY` にDevin APIキーを設定 - -## 禁止事項 - -- **Phase B完了時点では原文を一字一句そのまま保持すること。要約・省略・改変してはならない** -- Phase B・Cの時点では翻訳してはならない。言語変換はPhase Dでのみ行う -- 原文の内容を「理解して書き直す」のではなく、スクリプトで機械的に処理すること -- 出力先ディレクトリ以外のファイルを変更してはならない - -## 注意事項 - -- DeepWikiのコンテンツは通常**英語**で生成される。日本語出力が指定されている場合、Phase Dで翻訳する -- セクション番号が10以上でも0埋め2桁にすることでソート順を維持（`01_`, `02_`, ..., `10_`） -- `fetch_wiki.py` は MCP サーバーに直接HTTPリクエストを送信するため、`requests` ライブラリが必要 -- 既存ファイルがある場合は内容を比較し、差分がある部分のみ更新する diff --git a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/fetch_wiki.py b/plugins/ndf-shared/skills/deepwiki-transfer/scripts/fetch_wiki.py deleted file mode 100644 index 17c2a83a..00000000 --- a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/fetch_wiki.py +++ /dev/null @@ -1,229 +0,0 @@ -#!/usr/bin/env python3 -""" -DeepWiki MCP から read_wiki_contents を直接HTTP呼び出しし、 -レスポンス全体をファイルに保存するスクリプト。 - -LLMコンテキストを経由せず、MCPサーバーに直接HTTPリクエストを送信して -全コンテンツをファイル化する。 - -使用方法: - # プライベートリポジトリ（Devin MCP、APIキー必要） - python fetch_wiki.py --repo owner/repo --output /tmp/deepwiki_raw.md --api-key YOUR_KEY - - # 環境変数 DEVIN_API_KEY 利用 - DEVIN_API_KEY=xxx python fetch_wiki.py --repo owner/repo --output /tmp/deepwiki_raw.md - - # 公開リポジトリ（DeepWiki MCP、認証不要） - python fetch_wiki.py --repo facebook/react --output /tmp/deepwiki_raw.md --public - - # Wiki構造のみ取得 - python fetch_wiki.py --repo owner/repo --output /tmp/deepwiki_structure.md --tool read_wiki_structure -""" - -import argparse -import json -import os -import sys -import uuid - -try: - import requests -except ImportError: - print("エラー: requests ライブラリが必要です。pip install requests を実行してください。", file=sys.stderr) - sys.exit(1) - -# MCPサーバーのエンドポイント -DEVIN_MCP_URL = "https://mcp.devin.ai/mcp" -DEEPWIKI_MCP_URL = "https://mcp.deepwiki.com/mcp" - - -def create_jsonrpc_request(method: str, params: dict, request_id: int) -> dict: - """JSON-RPC 2.0 リクエストを生成する""" - return { - "jsonrpc": "2.0", - "id": request_id, - "method": method, - "params": params, - } - - -def parse_sse_json(resp: requests.Response) -> dict: - """SSE（Server-Sent Events）形式のレスポンスからJSON-RPCメッセージを抽出する""" - content_type = resp.headers.get("Content-Type", "") - if "text/event-stream" in content_type: - # SSE形式: "event: message\ndata: {...}\n\n" をパースする - for line in resp.text.split("\n"): - line = line.strip() - if line.startswith("data: "): - data_str = line[6:] - return json.loads(data_str) - raise RuntimeError("SSEレスポンスにdataフィールドが見つかりません") - else: - return resp.json() - - -def mcp_initialize(session: requests.Session, url: str) -> str | None: - """MCPサーバーとの初期化ハンドシェイクを行い、セッションIDを取得する""" - init_request = create_jsonrpc_request( - "initialize", - { - "protocolVersion": "2024-11-05", - "capabilities": {}, - "clientInfo": {"name": "deepwiki-fetch", "version": "1.0.0"}, - }, - 1, - ) - resp = session.post(url, json=init_request) - resp.raise_for_status() - - # SSE形式のレスポンスをパース - parse_sse_json(resp) - - # セッションIDをヘッダーから取得 - session_id = resp.headers.get("Mcp-Session-Id") - - # initialized 通知を送信 - notification = { - "jsonrpc": "2.0", - "method": "notifications/initialized", - "params": {}, - } - headers = {} - if session_id: - headers["Mcp-Session-Id"] = session_id - session.post(url, json=notification, headers=headers) - - return session_id - - -def mcp_call_tool( - session: requests.Session, - url: str, - tool_name: str, - arguments: dict, - session_id: str | None, -) -> str: - """MCPツールを呼び出し、テキスト結果を返す""" - call_request = create_jsonrpc_request( - "tools/call", - {"name": tool_name, "arguments": arguments}, - 2, - ) - headers = {} - if session_id: - headers["Mcp-Session-Id"] = session_id - - resp = session.post(url, json=call_request, headers=headers, timeout=300) - resp.raise_for_status() - - result = parse_sse_json(resp) - - if "error" in result: - error = result["error"] - raise RuntimeError(f"MCPエラー: [{error.get('code')}] {error.get('message')}") - - # レスポンスからテキストコンテンツを抽出 - content_parts = result.get("result", {}).get("content", []) - texts = [] - for part in content_parts: - if part.get("type") == "text": - texts.append(part["text"]) - - if not texts: - raise RuntimeError("MCPレスポンスにテキストコンテンツが含まれていません") - - return "\n".join(texts) - - -def main(): - parser = argparse.ArgumentParser( - description="DeepWiki MCPからWikiコンテンツを取得してファイルに保存する" - ) - parser.add_argument( - "--repo", - required=True, - help="対象リポジトリ（owner/repo形式、例: volareinc/carmo-system-console）", - ) - parser.add_argument( - "--output", - required=True, - help="出力先ファイルパス（例: /tmp/deepwiki_raw.md）", - ) - parser.add_argument( - "--api-key", - default=None, - help="Devin APIキー（未指定時は環境変数 DEVIN_API_KEY を使用）", - ) - parser.add_argument( - "--public", - action="store_true", - help="公開リポジトリ用のDeepWiki MCPを使用（認証不要）", - ) - parser.add_argument( - "--tool", - default="read_wiki_contents", - choices=["read_wiki_contents", "read_wiki_structure"], - help="使用するMCPツール（デフォルト: read_wiki_contents）", - ) - args = parser.parse_args() - - # エンドポイントと認証の設定 - if args.public: - url = DEEPWIKI_MCP_URL - api_key = None - print(f"DeepWiki MCP（公開）を使用: {url}") - else: - url = DEVIN_MCP_URL - api_key = args.api_key or os.environ.get("DEVIN_API_KEY") - if not api_key: - print( - "エラー: --api-key または環境変数 DEVIN_API_KEY が必要です（公開リポジトリは --public を指定）", - file=sys.stderr, - ) - sys.exit(1) - print(f"Devin MCP（プライベート対応）を使用: {url}") - - # HTTPセッション作成 - session = requests.Session() - if api_key: - session.headers["Authorization"] = f"Bearer {api_key}" - session.headers["Content-Type"] = "application/json" - session.headers["Accept"] = "application/json, text/event-stream" - - try: - # 1. MCP初期化 - print("MCPサーバーに接続中...") - session_id = mcp_initialize(session, url) - print(f"接続完了（セッションID: {session_id or 'なし'}）") - - # 2. ツール呼び出し - print(f"{args.tool} を呼び出し中（リポジトリ: {args.repo}）...") - content = mcp_call_tool( - session, url, args.tool, {"repoName": args.repo}, session_id - ) - - # 3. ファイルに保存 - with open(args.output, "w", encoding="utf-8") as f: - f.write(content) - - file_size = os.path.getsize(args.output) - line_count = content.count("\n") + 1 - print(f"保存完了: {args.output}") - print(f" サイズ: {file_size:,} bytes") - print(f" 行数: {line_count:,}") - - # ページ数をカウント（read_wiki_contents の場合） - if args.tool == "read_wiki_contents": - page_count = content.count("# Page: ") - print(f" ページ数: {page_count}") - - except requests.exceptions.RequestException as e: - print(f"HTTP通信エラー: {e}", file=sys.stderr) - sys.exit(1) - except RuntimeError as e: - print(f"エラー: {e}", file=sys.stderr) - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/pyproject.toml b/plugins/ndf-shared/skills/deepwiki-transfer/scripts/pyproject.toml deleted file mode 100644 index 9213b749..00000000 --- a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/pyproject.toml +++ /dev/null @@ -1,13 +0,0 @@ -[project] -name = "deepwiki-transfer-scripts" -version = "1.0.0" -description = "DeepWiki MCP コンテンツ取得・分割・GFM検証スクリプト" -requires-python = ">=3.10" -dependencies = [ - "requests>=2.28.0", -] - -[project.scripts] -fetch-wiki = "fetch_wiki:main" -split-pages = "split_pages:main" -validate-gfm = "validate_gfm:main" diff --git a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/split_pages.py b/plugins/ndf-shared/skills/deepwiki-transfer/scripts/split_pages.py deleted file mode 100644 index a2ad498f..00000000 --- a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/split_pages.py +++ /dev/null @@ -1,266 +0,0 @@ -#!/usr/bin/env python3 -""" -DeepWiki生データファイルを `# Page:` マーカーで機械的に分割し、 -セクション番号prefix付きのファイル名でリネームして出力するスクリプト。 - -分割時に内容は一切変更しない（`# Page: {タイトル}` → `# {タイトル}` の置換のみ）。 - -使用方法: - python split_pages.py /tmp/deepwiki_raw.md ./deepWiki/ - - # 構造確認のみ（ファイル出力なし） - python split_pages.py /tmp/deepwiki_raw.md ./deepWiki/ --dry-run - - # Wiki構造ファイルからセクション順序を指定 - python split_pages.py /tmp/deepwiki_raw.md ./deepWiki/ --structure /tmp/deepwiki_structure.md -""" - -import argparse -import os -import re -import sys - - -def sanitize_filename(title: str) -> str: - """タイトルをファイル名に変換する（スペース→アンダースコア、特殊文字除去）""" - name = title.strip() - name = name.replace(" ", "_") - name = name.replace("/", "_") - name = name.replace("\\", "_") - # ファイル名として安全な文字のみ残す（英数字、日本語、アンダースコア、ハイフン、ドット） - name = re.sub(r"[^\w\-.]", "", name, flags=re.UNICODE) - return name - - -def parse_raw_file(filepath: str) -> list[tuple[str, str]]: - """ - 生データファイルを読み込み、`# Page:` マーカーで分割する。 - - 戻り値: [(タイトル, 内容), ...] のリスト - 内容の先頭行は `# Page: {タイトル}` → `# {タイトル}` に置換済み - """ - with open(filepath, "r", encoding="utf-8") as f: - raw = f.read() - - # `# Page: ` で始まる行を境界として分割 - pages = [] - current_title = None - current_lines = [] - - for line in raw.splitlines(keepends=True): - if line.startswith("# Page: "): - # 前のページを保存 - if current_title is not None: - pages.append((current_title, "".join(current_lines))) - # 新しいページ開始 - current_title = line[len("# Page: "):].strip() - # `# Page: {タイトル}` → `# {タイトル}` に置換 - current_lines = [f"# {current_title}\n"] - else: - if current_title is not None: - current_lines.append(line) - - # 最後のページを保存 - if current_title is not None: - pages.append((current_title, "".join(current_lines))) - - return pages - - -def parse_structure_file(filepath: str) -> list[str]: - """ - Wiki構造ファイルからページタイトルの順序リストを取得する。 - read_wiki_structure の出力を解析する。 - """ - with open(filepath, "r", encoding="utf-8") as f: - content = f.read() - - titles = [] - for line in content.splitlines(): - line = line.strip() - if not line: - continue - # 番号付きリスト、ハイフンリスト、または単純なタイトル行を検出 - # 例: "1. Overview", "- Overview", " 1.1 System Architecture" - match = re.match(r"^[\d.\-\s]*\s*(.+)$", line) - if match: - title = match.group(1).strip() - if title and not title.startswith("#") and not title.startswith("```"): - titles.append(title) - - return titles - - -def strip_section_number(title: str) -> str: - """タイトルからセクション番号部分を除去する（ファイル名生成用）""" - # "1.1 System Architecture" → "System Architecture" - # "2. API Reference" → "API Reference" - # "Overview" → "Overview"（変更なし） - stripped = re.sub(r"^\d+\.\d+\.?\s+", "", title) - stripped = re.sub(r"^\d+\.?\s+", "", stripped) - return stripped if stripped else title - - -def assign_prefixes(pages: list[tuple[str, str]], structure_titles: list[str] | None = None) -> list[tuple[str, str, str]]: - """ - 各ページにセクション番号prefixを割り当てる。 - - structure_titles が指定されている場合、その順序に従う。 - 未指定の場合、ページの出現順に連番を振る。 - - 戻り値: [(prefix, タイトル, 内容), ...] のリスト - タイトルはセクション番号を除去済み（ファイル名生成用） - """ - # ページタイトルからセクション構造を推定 - # DeepWikiの構造: トップレベルページ → サブセクションページの順 - result = [] - section_num = 0 - subsection_counts = {} - - if structure_titles: - # 構造ファイルからの順序に基づいてprefix割り当て - title_to_content = {title: content for title, content in pages} - ordered_titles = [] - - # 構造ファイルのタイトルとページタイトルをマッチング - for struct_title in structure_titles: - for page_title, _ in pages: - if _titles_match(struct_title, page_title): - ordered_titles.append(page_title) - break - - # マッチしなかったページを末尾に追加 - matched = set(ordered_titles) - for page_title, _ in pages: - if page_title not in matched: - ordered_titles.append(page_title) - - pages_ordered = [(t, title_to_content.get(t, "")) for t in ordered_titles if t in title_to_content] - else: - pages_ordered = pages - - # セクション構造の推定とprefix割り当て - # DeepWikiの典型的な構造: - # - 最初のページはOverview（トップレベル） - # - その後はセクション番号付きのページが続く - # - サブセクションはメインセクションの直後に配置される - prev_section = 0 - section_map = {} # タイトル → (section, subsection) - - for title, content in pages_ordered: - # タイトルからセクション番号を推定 - # パターン: "1.1 Title", "1.1. Title", "Section 1: Title" など - subsection_match = re.match(r"^(\d+)\.(\d+)\.?\s", title) - section_match = re.match(r"^(\d+)\.?\s", title) - - if subsection_match: - sec = int(subsection_match.group(1)) - sub = int(subsection_match.group(2)) - prefix = f"{sec:02d}_{sub}_" - prev_section = sec - elif section_match: - sec = int(section_match.group(1)) - prefix = f"{sec:02d}_" - prev_section = sec - else: - # 番号なしのページ（Overviewなど） - section_num += 1 - # 既に番号付きセクションが出現している場合、連番を続ける - if prev_section > 0 and section_num <= prev_section: - section_num = prev_section + 1 - prefix = f"{section_num:02d}_" - prev_section = section_num - - # ファイル名用にセクション番号を除去したタイトルを使用 - clean_title = strip_section_number(title) - result.append((prefix, clean_title, content)) - - return result - - -def _titles_match(struct_title: str, page_title: str) -> bool: - """構造ファイルのタイトルとページタイトルが一致するか判定する""" - # 完全一致 - if struct_title == page_title: - return True - # 正規化して比較（スペース、ハイフン、アンダースコアの違いを無視） - normalize = lambda s: re.sub(r"[\s\-_]+", " ", s.lower().strip()) - return normalize(struct_title) == normalize(page_title) - - -def main(): - parser = argparse.ArgumentParser( - description="DeepWiki生データを # Page: マーカーでファイル分割する" - ) - parser.add_argument( - "input", - help="入力ファイルパス（fetch_wiki.py の出力ファイル）", - ) - parser.add_argument( - "output_dir", - help="出力先ディレクトリパス（例: ./deepWiki/）", - ) - parser.add_argument( - "--structure", - default=None, - help="Wiki構造ファイルパス（read_wiki_structure の出力、セクション順序の決定に使用）", - ) - parser.add_argument( - "--dry-run", - action="store_true", - help="ファイル出力せず、分割結果のみ表示する", - ) - args = parser.parse_args() - - # 入力ファイル読み込み・分割 - if not os.path.exists(args.input): - print(f"エラー: 入力ファイルが見つかりません: {args.input}", file=sys.stderr) - sys.exit(1) - - print(f"入力ファイル読み込み中: {args.input}") - pages = parse_raw_file(args.input) - print(f"検出ページ数: {len(pages)}") - - if not pages: - print("エラー: ページが検出されませんでした。入力ファイルに `# Page:` マーカーが含まれているか確認してください。", file=sys.stderr) - sys.exit(1) - - # 構造ファイルがあればセクション順序を取得 - structure_titles = None - if args.structure: - if os.path.exists(args.structure): - structure_titles = parse_structure_file(args.structure) - print(f"構造ファイルから {len(structure_titles)} タイトルを読み込みました") - else: - print(f"警告: 構造ファイルが見つかりません: {args.structure}", file=sys.stderr) - - # prefix割り当て - prefixed_pages = assign_prefixes(pages, structure_titles) - - # 出力先ディレクトリ作成 - if not args.dry_run: - os.makedirs(args.output_dir, exist_ok=True) - - # ファイル出力 - print("\n--- 分割結果 ---") - for prefix, title, content in prefixed_pages: - filename = f"{prefix}{sanitize_filename(title)}.md" - filepath = os.path.join(args.output_dir, filename) - - content_size = len(content.encode("utf-8")) - line_count = content.count("\n") - print(f" {filename} ({content_size:,} bytes, {line_count} lines)") - - if not args.dry_run: - with open(filepath, "w", encoding="utf-8") as f: - f.write(content) - - print(f"\n合計: {len(prefixed_pages)} ファイル") - if args.dry_run: - print("（dry-runモード: ファイルは出力されていません）") - else: - print(f"出力先: {args.output_dir}") - - -if __name__ == "__main__": - main() diff --git a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/validate_gfm.py b/plugins/ndf-shared/skills/deepwiki-transfer/scripts/validate_gfm.py deleted file mode 100644 index 867257a0..00000000 --- a/plugins/ndf-shared/skills/deepwiki-transfer/scripts/validate_gfm.py +++ /dev/null @@ -1,324 +0,0 @@ -#!/usr/bin/env python3 -""" -Markdownファイルを GitHub Flavored Markdown (GFM) Spec に準拠するよう検証・修正するスクリプト。 - -主な修正: - - 言語指定のないコードブロックに適切な言語を推定・付与 - - テーブルのパイプ区切りとアライメント行の修正 - - 見出し前後の空行挿入 - - 末尾改行の統一 - -使用方法: - # 単一ファイルを検証・修正 - python validate_gfm.py ./deepWiki/01_Overview.md - - # ディレクトリ内の全 .md ファイルを一括処理 - python validate_gfm.py ./deepWiki/ - - # 検証のみ（修正しない） - python validate_gfm.py ./deepWiki/ --check-only - - # 修正内容の詳細表示 - python validate_gfm.py ./deepWiki/ --verbose -""" - -import argparse -import glob -import os -import re -import sys - - -# コードブロックの言語推定パターン -LANGUAGE_PATTERNS = { - "mermaid": [ - re.compile(r"^\s*(graph\s+(TD|TB|BT|RL|LR)|flowchart\s+(TD|TB|BT|RL|LR)|sequenceDiagram|classDiagram|stateDiagram|erDiagram|gantt|pie|gitGraph|journey)", re.MULTILINE), - ], - "php": [ - re.compile(r"<\?php"), - re.compile(r"\$this->"), - re.compile(r"(function|class|namespace|use)\s+\w+"), - re.compile(r"->(get|set|find|create|update|delete)\w*\("), - ], - "sql": [ - re.compile(r"^\s*(SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP|FROM|WHERE|JOIN|GROUP BY|ORDER BY)\s", re.MULTILINE | re.IGNORECASE), - ], - "json": [ - re.compile(r'^\s*\{[\s\S]*"[^"]+"\s*:', re.MULTILINE), - ], - "yaml": [ - re.compile(r"^\w+:\s*$", re.MULTILINE), - re.compile(r"^\s+-\s+\w+:", re.MULTILINE), - ], - "bash": [ - re.compile(r"^\s*(#!/bin/bash|#!/bin/sh|export\s|source\s|\$\(|apt-get|npm|pip|composer|docker|kubectl)", re.MULTILINE), - re.compile(r"^\s*\$\s+\w+", re.MULTILINE), - ], - "javascript": [ - re.compile(r"(const|let|var|function|=>\s*\{|import\s+.*from|require\()"), - ], - "typescript": [ - re.compile(r"(interface\s+\w+|type\s+\w+\s*=|:\s*(string|number|boolean|any)\b)"), - ], - "html": [ - re.compile(r"<(!DOCTYPE|html|head|body|div|span|table|form)\b", re.IGNORECASE), - ], - "css": [ - re.compile(r"^\s*[.#@]\w+.*\{", re.MULTILINE), - re.compile(r"(color|background|font-size|margin|padding|display)\s*:"), - ], - "python": [ - re.compile(r"(def\s+\w+|class\s+\w+|import\s+\w+|from\s+\w+\s+import)"), - ], - "xml": [ - re.compile(r"<\?xml\s"), - re.compile(r"<\w+\s+xmlns[=:]"), - ], -} - - -def detect_language(code_content: str) -> str | None: - """コードブロックの内容から言語を推定する""" - # スコアベースで最も適合する言語を選択 - scores = {} - for lang, patterns in LANGUAGE_PATTERNS.items(): - score = 0 - for pattern in patterns: - if pattern.search(code_content): - score += 1 - if score > 0: - scores[lang] = score - - if not scores: - return None - - # 最高スコアの言語を返す - return max(scores, key=scores.get) - - -def fix_code_blocks(content: str, verbose: bool = False) -> tuple[str, list[str]]: - """コードブロックに適切な言語指定を付与する""" - fixes = [] - lines = content.split("\n") - result = [] - i = 0 - - while i < len(lines): - line = lines[i] - - # 言語指定ありのコードブロック開始 → 閉じフェンスまでスキップ - if re.match(r"^```\w+", line): - result.append(line) - i += 1 - while i < len(lines) and not re.match(r"^```\s*$", lines[i]): - result.append(lines[i]) - i += 1 - if i < len(lines): - result.append(lines[i]) # 閉じフェンス - i += 1 - continue - - # 言語指定なしのコードブロック開始を検出 - if re.match(r"^```\s*$", line): - # コードブロックの内容を収集 - code_lines = [] - j = i + 1 - while j < len(lines) and not re.match(r"^```\s*$", lines[j]): - code_lines.append(lines[j]) - j += 1 - - code_content = "\n".join(code_lines) - detected = detect_language(code_content) - - if detected: - result.append(f"```{detected}") - fixes.append(f"行{i+1}: ``` → ```{detected}") - if verbose: - preview = code_content[:80].replace("\n", " ") - fixes[-1] += f" (内容: {preview}...)" - else: - result.append(line) - - # コンテンツ行を追加 - for cl in code_lines: - result.append(cl) - - # 閉じフェンスを追加してインデックスを進める - if j < len(lines): - result.append(lines[j]) # 閉じフェンス - i = j + 1 - else: - i = j - continue - - result.append(line) - i += 1 - - return "\n".join(result), fixes - - -def fix_table_formatting(content: str) -> tuple[str, list[str]]: - """テーブルのパイプ区切りとアライメント行を修正する""" - fixes = [] - lines = content.split("\n") - result = [] - i = 0 - - while i < len(lines): - line = lines[i] - - # テーブルヘッダー行の検出（パイプで区切られた行） - if "|" in line and i + 1 < len(lines): - # 次の行がアライメント行かチェック - next_line = lines[i + 1] - if re.match(r"^\s*\|[\s\-:|]+\|\s*$", next_line): - # アライメント行の列数がヘッダーと一致するか確認 - header_cols = len([c for c in line.split("|") if c.strip()]) - sep_cols = len([c for c in next_line.split("|") if c.strip() or c == ""]) - - # アライメント行のフォーマットを統一 - sep_parts = [p.strip() for p in next_line.split("|")] - sep_parts = [p for p in sep_parts if p or p == ""] - - result.append(line) - else: - result.append(line) - else: - result.append(line) - - i += 1 - - return "\n".join(result), fixes - - -def fix_heading_spacing(content: str) -> tuple[str, list[str]]: - """見出し前後に適切な空行を挿入する""" - fixes = [] - lines = content.split("\n") - result = [] - - for i, line in enumerate(lines): - # 見出し行の検出（# で始まる行） - if re.match(r"^#{1,6}\s+", line): - # 前の行が空行でない場合、空行を挿入（ファイル先頭を除く） - if result and result[-1].strip(): - result.append("") - fixes.append(f"行{i+1}: 見出し前に空行を挿入") - - result.append(line) - - # 見出し行の次が空行でない場合、空行を挿入 - if re.match(r"^#{1,6}\s+", line) and i + 1 < len(lines): - next_line = lines[i + 1] - if next_line.strip() and not re.match(r"^#{1,6}\s+", next_line): - # 次のループで追加されるので、ここでは何もしない - pass - - return "\n".join(result), fixes - - -def fix_trailing_newline(content: str) -> tuple[str, list[str]]: - """ファイル末尾の改行を統一する（1つの改行で終わる）""" - fixes = [] - if not content.endswith("\n"): - content += "\n" - fixes.append("ファイル末尾に改行を追加") - elif content.endswith("\n\n"): - content = content.rstrip("\n") + "\n" - fixes.append("ファイル末尾の余分な改行を削除") - return content, fixes - - -def validate_and_fix(filepath: str, check_only: bool = False, verbose: bool = False) -> list[str]: - """Markdownファイルを検証・修正する""" - with open(filepath, "r", encoding="utf-8") as f: - original = f.read() - - content = original - all_fixes = [] - - # 1. コードブロックの言語指定 - content, fixes = fix_code_blocks(content, verbose) - all_fixes.extend(fixes) - - # 2. テーブルフォーマット - content, fixes = fix_table_formatting(content) - all_fixes.extend(fixes) - - # 3. 見出し前後の空行 - content, fixes = fix_heading_spacing(content) - all_fixes.extend(fixes) - - # 4. 末尾改行 - content, fixes = fix_trailing_newline(content) - all_fixes.extend(fixes) - - # 修正を適用 - if all_fixes and not check_only: - with open(filepath, "w", encoding="utf-8") as f: - f.write(content) - - return all_fixes - - -def main(): - parser = argparse.ArgumentParser( - description="MarkdownファイルをGFM準拠に検証・修正する" - ) - parser.add_argument( - "path", - help="対象ファイルまたはディレクトリ", - ) - parser.add_argument( - "--check-only", - action="store_true", - help="検証のみ行い、ファイルを修正しない", - ) - parser.add_argument( - "--verbose", - "-v", - action="store_true", - help="修正内容の詳細を表示する", - ) - args = parser.parse_args() - - # 対象ファイル一覧を取得 - if os.path.isfile(args.path): - files = [args.path] - elif os.path.isdir(args.path): - files = sorted(glob.glob(os.path.join(args.path, "*.md"))) - else: - print(f"エラー: パスが見つかりません: {args.path}", file=sys.stderr) - sys.exit(1) - - if not files: - print(f"対象の .md ファイルが見つかりません: {args.path}") - sys.exit(0) - - total_fixes = 0 - files_with_fixes = 0 - - for filepath in files: - filename = os.path.basename(filepath) - fixes = validate_and_fix(filepath, args.check_only, args.verbose) - - if fixes: - files_with_fixes += 1 - total_fixes += len(fixes) - status = "要修正" if args.check_only else "修正済" - print(f" [{status}] {filename}: {len(fixes)} 件") - if args.verbose: - for fix in fixes: - print(f" - {fix}") - else: - if args.verbose: - print(f" [OK] {filename}") - - print(f"\n合計: {len(files)} ファイル, {files_with_fixes} ファイルに {total_fixes} 件の{'問題' if args.check_only else '修正'}") - - if args.check_only and total_fixes > 0: - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/plugins/ndf-shared/skills/deploy/SKILL.md b/plugins/ndf-shared/skills/deploy/SKILL.md index 769919d7..b338c84c 100644 --- a/plugins/ndf-shared/skills/deploy/SKILL.md +++ b/plugins/ndf-shared/skills/deploy/SKILL.md @@ -1,6 +1,6 @@ --- name: deploy -description: "Create deploy PRs from feature to environment branches." +description: "Create a deploy PR from the current feature branch to an environment branch such as qa/staging or release/v2. 破壊的操作のため、利用者が /ndf:deploy を明示的に指示したときのみ実行する（環境ブランチへデプロイ / qaに上げる / stagingに反映 / リリースブランチへPR）。deployブランチを作成し origin/main を取り込んでからPRを出す。" argument-hint: " (例: qa/staging, release/v2)" disable-model-invocation: true allowed-tools: diff --git a/plugins/ndf-shared/skills/docker-container-access/SKILL.md b/plugins/ndf-shared/skills/docker-container-access/SKILL.md index eadd1ffe..444a993f 100644 --- a/plugins/ndf-shared/skills/docker-container-access/SKILL.md +++ b/plugins/ndf-shared/skills/docker-container-access/SKILL.md @@ -67,7 +67,6 @@ ls -la /var/run/docker.sock 2>/dev/null && echo "DooD環境" || echo "DinDまた ## 関連Skill -- **python-execution**: Python実行環境の判定 - **corder-code-templates**: Dockerfileテンプレート ## 関連リソース diff --git a/plugins/ndf-shared/skills/git-gh-operations/01-common-errors.md b/plugins/ndf-shared/skills/git-gh-operations/01-common-errors.md deleted file mode 100644 index a7afa445..00000000 --- a/plugins/ndf-shared/skills/git-gh-operations/01-common-errors.md +++ /dev/null @@ -1,145 +0,0 @@ -# Git / gh 共通エラー事例集 - -## 1. git add pathspec エラー - -### 事象 -``` -fatal: pathspec 'lambda-batch/CarImageProcessingPipeline/src/foo.py' did not match any files -``` - -### 原因 -CWD が `/work/repo/lambda-batch/CarImageProcessingPipeline/` なのに、 -リポジトリルートからの相対パスで `git add` した。 - -`git status` はリポジトリルートからの相対パスで表示するが、 -`git add` は CWD からの相対パスで解決する。 - -### 予防策 -```bash -# Step 1: CWD確認 -pwd -# => /work/repo/lambda-batch/CarImageProcessingPipeline/ - -# Step 2: git status の出力を確認 -git status -# modified: lambda-batch/CarImageProcessingPipeline/src/foo.py -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -# これはリポジトリルートからの相対パス - -# Step 3: CWD からの相対パスに変換 -git add src/foo.py -# または -git add . # CWD以下のすべての変更 -``` - -## 2. gh api 404 エラー - -### 事象 -``` -gh api repos/owner/repo/pulls/comments/123/replies -f body='message' -# => 404 Not Found -``` - -### 原因 -POST メソッドが必要な API エンドポイントに GET でアクセスした。 -`gh api` はデフォルトで GET を使用する。 - -### 修正 -```bash -gh api -X POST repos/owner/repo/pulls/comments/123/replies -f body='message' -``` - -## 3. GitHub 自己 Approve エラー - -### 事象 -``` -Could not approve for pull request review. Can not approve your own pull request -``` - -### 原因 -GitHub はセキュリティ上、自分で作成した PR を APPROVE できない。 - -### 対策 -```bash -# pending review を削除してから COMMENT として再送信 -# method: "delete_pending" → method: "create" + event: "COMMENT" -``` - -## 4. AWS CLI [$LATEST] パースエラー - -### 事象 -``` -Unknown options: , , , -``` - -### 原因 -CloudWatch ログストリーム名に含まれる `[$LATEST]` が -`--query` JMESPath パーサーや shell の glob として解釈される。 - -### 対策 -```bash -# シングルクォートで囲んでも --query との組み合わせで問題が出る -# --output json + python パースが最も安全 -aws logs get-log-events \ - --log-group-name "/aws/lambda/func-name" \ - --log-stream-name '2026/02/18/[$LATEST]abc123' \ - --output json | python3 -c " -import sys, json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## 5. git commit メッセージの特殊文字 - -### 事象 -コミットメッセージに日本語や改行が含まれるとエスケープ問題が発生。 - -### 対策 -常に HEREDOC 形式を使用: -```bash -git commit -m "$(cat <<'EOF' -日本語メッセージ - -詳細説明 - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -注意: `<<'EOF'` （シングルクォート付き）で変数展開を抑制する。 - -## 6. gh pr checks が exit code 1 で止まる - -### 事象 -``` -gh pr checks 11765 2>&1 -# => チェック結果は表示されるが、1つでもfailがあると exit code 1 で終了 -# => Claude Code が「コマンド失敗」と判定して処理を中断 -``` - -### 原因 -`gh pr checks` は CI チェックに失敗があると非0の exit code を返す仕様。 -Claude Code の Bash ツールはコマンドの exit code が 0 以外だとエラーとして扱う。 - -### 対策 -常に `|| true` を付けて exit code を 0 にする: -```bash -# チェック一覧を取得（failがあっても止まらない） -gh pr checks 11765 2>&1 || true - -# --watch で完了待ちする場合も同様 -gh pr checks 11765 --watch 2>&1 || true - -# 失敗のみフィルタする場合 -gh pr checks 11765 2>&1 | grep -i fail || true -``` - -### 補足 -同様の問題が発生する gh コマンド: -- `gh run view RUN_ID --log-failed` （失敗ログ取得時） -- `gh pr diff` （差分が大きい場合にパイプ破損） - -いずれも `2>&1 || true` を付けることで安全に実行できる。 diff --git a/plugins/ndf-shared/skills/git-gh-operations/SKILL.md b/plugins/ndf-shared/skills/git-gh-operations/SKILL.md deleted file mode 100644 index 68b8a7a0..00000000 --- a/plugins/ndf-shared/skills/git-gh-operations/SKILL.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -name: git-gh-operations -description: "Resolve git and GitHub CLI operation errors." -when_to_use: "git / gh コマンドでエラーが出た or 操作方法に迷うとき。Triggers: 'git add', 'git commit', 'git push', 'gh pr', 'gh api', 'GitHub操作', 'gitエラー', 'fatal:', 'pathspec'" -allowed-tools: - - Bash - - Read ---- - -# Git / gh 操作スキル - -## 最重要ルール: CWD とパスの整合性 - -git コマンドはすべて **CWD からの相対パス** で解決される。 -操作前に必ず `pwd` で CWD を確認すること。 - -### パターン1: CWDがサブディレクトリの場合 - -``` -# CWD: /work/repo/lambda-batch/MyProject/ -# リポジトリルート: /work/repo/ - -# NG: リポジトリルートからのパスを指定 -git add lambda-batch/MyProject/src/foo.py -# => fatal: pathspec did not match any files - -# OK: CWDからの相対パスを指定 -git add src/foo.py - -# OK: 絶対パスを指定 -git add /work/repo/lambda-batch/MyProject/src/foo.py -``` - -### パターン2: 安全な方法 - -```bash -# 方法A: git -C でリポジトリルートを指定 -git -C /work/repo add lambda-batch/MyProject/src/foo.py - -# 方法B: CWD を変更せずに絶対パスを使用 -git add "$(git rev-parse --show-toplevel)/lambda-batch/MyProject/src/foo.py" - -# 方法C（推奨）: CWDからの相対パスを使用 -# まず pwd で確認してからパスを組み立てる -``` - -## git 操作チェックリスト - -### git add の前に - -1. `pwd` で CWD を確認 -2. `git status` で変更ファイルのパスを確認（表示されるパスはリポジトリルートからの相対パス） -3. `git status` の出力パスと CWD の関係を計算してから `git add` する - -### git commit の前に - -1. `git diff --cached` でステージング内容を確認 -2. HEREDOC形式でメッセージを渡す（改行・特殊文字の問題回避） - -```bash -git commit -m "$(cat <<'EOF' -コミットメッセージ - -Co-Authored-By: Claude Opus 4.6 -EOF -)" -``` - -## gh CLI / GitHub API の注意点 - -### パラメータ: `-f` vs `-F` - -```bash -# -f: 文字列パラメータ -gh api repos/OWNER/REPO/pulls/PR/comments -f body="テキスト" - -# -F: 非文字列パラメータ（数値、boolean、null、ファイル） -gh api repos/OWNER/REPO/pulls/PR/comments -F in_reply_to=2826074026 - -# 混在OK -gh api repos/OWNER/REPO/pulls/PR/comments -f body="返信テキスト" -F in_reply_to=2826074026 -``` - -### PRレビューコメントの取得 - -```bash -# コメント一覧を取得（id, path, body の先頭を表示） -gh api repos/OWNER/REPO/pulls/PR/comments \ - --jq '.[] | {id: .id, path: .path, body: (.body | split("\n")[0][:80])}' -``` - -### PRレビューコメントへの返信 - -```bash -# NG: /replies エンドポイントは存在しない（404になる） -gh api repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# NG: -X POST を付けても同じ（エンドポイント自体が存在しない） -gh api -X POST repos/OWNER/REPO/pulls/comments/{id}/replies -f body='...' -# => 404 Not Found - -# OK: in_reply_to パラメータを使って新規コメントとして投稿 -gh api repos/OWNER/REPO/pulls/PR/comments \ - -f body="返信テキスト" \ - -F in_reply_to=COMMENT_ID -``` - -### レビュースレッドの Resolve（GraphQL） - -```bash -# 1. 未解決スレッドのID一覧を取得 -gh api graphql -f query=' -query { - repository(owner: "OWNER", name: "REPO") { - pullRequest(number: PR) { - reviewThreads(first: 50) { - nodes { - id - isResolved - comments(first: 1) { - nodes { path body } - } - } - } - } - } -}' --jq '.data.repository.pullRequest.reviewThreads.nodes[] | select(.isResolved == false) | {id, path: .comments.nodes[0].path}' - -# 2. スレッドを Resolve -gh api graphql -f query=' -mutation { - resolveReviewThread(input: {threadId: "PRRT_xxx"}) { - thread { isResolved } - } -}' -``` - -### PR の CI チェック結果 - -`gh pr checks` は1つでもfailがあると **exit code 1** で終了する。 -Claude Codeではコマンド失敗と判定されて処理が止まるため、必ず `|| true` を付ける。 - -```bash -# NG: failがあるとexit code 1で止まる -gh pr checks PR --repo OWNER/REPO - -# OK: exit codeを常に0にして出力を取得 -gh pr checks PR --repo OWNER/REPO 2>&1 || true - -# OK: 失敗のみフィルタ -gh pr checks PR --repo OWNER/REPO 2>&1 | grep -i fail || true -``` - -#### 重要: CIの完了を待ってはいけない - -- `--watch` や完了までのポーリングは **禁止**。現在のステータスを一度スナップショットするだけでよい。 -- チェックが `in_progress` / `queued` / `pending` の場合は **完了を待たず次のステップへ進む**。 -- 対応対象は **コード修正で直せるfailのみ**。以下のような「ステータス確認系」チェックは無視する: - - `check_pr_requirements` 等、PR要件・メタ情報のみ検証するもの - - Lint/テストに非依存なラベル/タイトル/説明チェック - - 外部サービス起因で自己修復するトランジェントなfail（再実行で直るもの） -- 対応する: ビルド失敗・テスト失敗・型エラー・lint違反など、**リポジトリ内コードの修正で解消可能なもの**。 - -```bash -# 失敗ジョブのログ（エラー行のみ抽出） -gh run view RUN_ID --repo OWNER/REPO --log-failed 2>&1 \ - | grep -E '(FAIL|Error|Tests:)' | head -20 || true -``` - -### 自分のPRは Approve できない - -``` -# GitHub の制約: 自分で作成した PR に APPROVE レビューは不可 -# => "Can not approve your own pull request" -# 対策: event を "COMMENT" に変更して送信 -``` - -### PR作成時の body は HEREDOC - -```bash -# NG: \n がリテラルで混入する可能性 -gh pr create --title "タイトル" --body "行1\n行2" - -# OK: HEREDOC形式 -gh pr create --title "タイトル" --body "$(cat <<'EOF' -## Summary -- 変更内容 - -## Test plan -- [ ] テスト項目 -EOF -)" -``` - -## AWS CLI の注意点 - -### CloudWatch ログストリーム名の [$LATEST] - -```bash -# NG: --query で [$LATEST] を含む文字列がパースエラー -aws logs get-log-events --query 'events[*].message' --output text - -# OK: --output json にして python でパース -aws logs get-log-events --output json | python3 -c " -import sys,json -data = json.loads(sys.stdin.read()) -for e in data['events']: - print(e['message'].strip()) -" -``` - -## エラー事例集 - -| エラーメッセージ | 原因 | 対策 | -|----------------|------|------| -| `fatal: pathspec '...' did not match any files` | CWD とパスの不一致 | `pwd` 確認後、CWD相対パスで指定 | -| `404 Not Found` (gh api replies) | `/comments/{id}/replies` は存在しない | `in_reply_to` パラメータで投稿 | -| `422 Unprocessable` (gh api) | `-f` で数値を渡した | 数値は `-F` を使う | -| `Can not approve your own pull request` | 自己 Approve 不可 | `COMMENT` イベントに変更 | -| `gh pr checks` が exit code 1 | 1つでもfailがあると非0終了 | `gh pr checks ... 2>&1 \|\| true` | -| `Unknown options: , , ,` (aws cli) | `[$LATEST]` のシェルエスケープ | `--output json` + python パース | - -## 詳細ガイド - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-common-errors.md` | 詳細なエラー事例と再現手順 | エラー発生時 | diff --git a/plugins/ndf-shared/skills/google-chat/SKILL.md b/plugins/ndf-shared/skills/google-chat/SKILL.md deleted file mode 100644 index cffb61cf..00000000 --- a/plugins/ndf-shared/skills/google-chat/SKILL.md +++ /dev/null @@ -1,153 +0,0 @@ ---- -name: google-chat -description: "Read Google Chat spaces and messages." -when_to_use: "Google Chat スペースのメッセージ取得・スペース一覧が必要なとき。Triggers: 'Google Chat', 'chat.spaces', 'chat.messages', 'Chatスペース', 'メッセージ取得', 'チャット履歴'" -allowed-tools: - - Read - - Bash(python *) - - Bash(uv *) ---- - -# Google Chat アクセス - -## 概要 - -Google Chat API (Python) を使ってチャットスペースのメッセージを取得する。 -認証は `ndf:google-auth` スキルの共通モジュール (`get_credentials()`) を使用。 - -## 提供物 - -``` -google-chat/ -├── SKILL.md ← このファイル -├── pyproject.toml ← uv プロジェクト (Chat API 依存) -└── scripts/ - └── gchat_read.py ← CLI: メッセージ一覧 / スペース一覧 -``` - -`gchat_read.py` は実行時に `ndf:google-auth` スキルの `google_auth.py` を sys.path に追加して -`get_credentials()` を呼ぶ。`google-auth` 側で OAuth2 トークンを取得済みであれば追加の認証は不要。 - -## 前提条件 - -| 項目 | 値 | -|---|---| -| 認証 | `ndf:google-auth` スキル (共通 OAuth2 モジュール) | -| Python 実行 | `uv run --project ${CLAUDE_SKILL_DIR} python ...` または `uv run --with ...` | -| client_secret.json | `ndf:google-auth` の手順で配置済み | -| 既存トークン | `~/.config/gcloud/google_token.json` (`ndf:google-auth` で取得済み) | -| Cloud Console | **Google Chat API の有効化**が必要 | -| アカウント | Google Workspace (Business / Enterprise)。無料 Gmail では利用不可 | - -## URL から Space ID を取得 - -Google Chat URL の末尾がそのまま Space ID になる。 - -``` -https://mail.google.com/mail/u/0/#chat/space/AAQA6AWG1iE - ^^^^^^^^^^^ - これが Space ID -``` - -API 呼び出し時は `spaces/AAQA6AWG1iE` の形式で指定する (スクリプトは ID のみ受け取る)。 - -## クイックスタート - -```bash -SKILL_DIR=${CLAUDE_SKILL_DIR} -SCRIPT=$SKILL_DIR/scripts/gchat_read.py - -# スペース一覧を表示 -uv run --project $SKILL_DIR python $SCRIPT --list-spaces - -# メッセージ一覧 (デフォルト Space ID) -uv run --project $SKILL_DIR python $SCRIPT - -# Space ID を指定 -uv run --project $SKILL_DIR python $SCRIPT --space AAQA6AWG1iE - -# 日付フィルタ (RFC-3339 形式) -uv run --project $SKILL_DIR python $SCRIPT --space AAQA6AWG1iE \ - --after "2024-01-01T00:00:00+09:00" - -# 出力先を変更 -uv run --project $SKILL_DIR python $SCRIPT --space AAQA6AWG1iE --output /tmp/my_chat.json -``` - -### 出力 - -- `--output` で指定した JSON ファイル (デフォルト: `/tmp/gchat_messages.json`) -- 標準出力に直近 5 件のプレビュー - -## API パラメータリファレンス - -### ListMessagesRequest - -| パラメータ | 型 | 説明 | -|---|---|---| -| `parent` | string | `spaces/{space_id}` 形式 (必須) | -| `page_size` | int | 最大取得件数 (デフォルト 25、最大 1,000) | -| `page_token` | string | ページネーション用トークン | -| `filter` | string | `createTime` や `thread.name` でフィルタ | -| `order_by` | string | `createTime ASC` または `createTime DESC` | -| `show_deleted` | bool | 削除済みメッセージを含めるか | - -### フィルタ構文 - -``` -# 特定日時以降 -createTime > "2024-01-01T00:00:00+09:00" - -# 日付範囲 -createTime > "2024-03-01T00:00:00+09:00" AND createTime < "2024-04-01T00:00:00+09:00" - -# スレッド指定 -thread.name = "spaces/AAQA6AWG1iE/threads/THREAD_ID" -``` - -### 必要な OAuth スコープ - -| スコープ | 用途 | -|---|---| -| `chat.spaces.readonly` | スペース一覧取得 (読み取り専用) | -| `chat.messages.readonly` | メッセージ一覧取得 (読み取り専用) | -| `chat.messages` | メッセージ読み書き (送信が必要な場合) | - -`ndf:google-auth` でこれらのスコープを取得しておく: - -```bash -! python ${CLAUDE_SKILLS_DIR:-${CLAUDE_PROJECT_DIR}/.claude/skills}/google-auth/scripts/google_auth.py \ - chat.messages.readonly chat.spaces.readonly -``` - -## トラブルシューティング - -### 403 PERMISSION_DENIED - -- Google Cloud Console で Chat API が有効化されているか確認 -- OAuth クライアント ID が Desktop app 用か確認 -- Google Workspace (Business / Enterprise) アカウントでログインしているか確認 -- 無料 Gmail アカウントでは利用不可 - -### 403 Insufficient scopes - -スコープ変更時はトークンを削除して再認証 (`ndf:google-auth` の `--clear` を使う): - -```bash -python ${CLAUDE_SKILLS_DIR:-${CLAUDE_PROJECT_DIR}/.claude/skills}/google-auth/scripts/google_auth.py --clear -! python ${CLAUDE_SKILLS_DIR:-${CLAUDE_PROJECT_DIR}/.claude/skills}/google-auth/scripts/google_auth.py \ - chat.messages.readonly chat.spaces.readonly -``` - -### INVALID_ARGUMENT (filter) - -- 日付は RFC-3339 形式: `"2024-01-01T00:00:00+09:00"` -- スレッド名はフルパス: `spaces/SPACE_ID/threads/THREAD_ID` - -### `GOOGLE_APPLICATION_CREDENTIALS` の干渉 - -サービスアカウントを指している場合は明示的にクリアする: - -```bash -GOOGLE_APPLICATION_CREDENTIALS="" uv run --project $SKILL_DIR python $SCRIPT --list-spaces -``` diff --git a/plugins/ndf-shared/skills/google-chat/pyproject.toml b/plugins/ndf-shared/skills/google-chat/pyproject.toml deleted file mode 100644 index f3d5b808..00000000 --- a/plugins/ndf-shared/skills/google-chat/pyproject.toml +++ /dev/null @@ -1,11 +0,0 @@ -[project] -name = "google-chat-skill" -version = "0.1.0" -description = "Google Chat API アクセスの依存関係 (ndf:google-auth と組み合わせて使用)" -requires-python = ">=3.9" -dependencies = [ - "google-apps-chat>=0.1.0", - "google-auth>=2.0.0", - "google-auth-httplib2>=0.2.0", - "google-auth-oauthlib>=1.0.0", -] diff --git a/plugins/ndf-shared/skills/google-chat/scripts/gchat_read.py b/plugins/ndf-shared/skills/google-chat/scripts/gchat_read.py deleted file mode 100644 index b98f3829..00000000 --- a/plugins/ndf-shared/skills/google-chat/scripts/gchat_read.py +++ /dev/null @@ -1,168 +0,0 @@ -"""Google Chat API メッセージ読み取りスクリプト - -Usage: - # デフォルトSpace IDでメッセージ取得 - python3 gchat_read.py - - # Space IDを指定 - python3 gchat_read.py --space AAQA6AWG1iE - - # 日付フィルタ付き - python3 gchat_read.py --space AAQA6AWG1iE --after "2024-01-01T00:00:00+09:00" - - # 出力先を変更 - python3 gchat_read.py --space AAQA6AWG1iE --output /tmp/my_chat.json - - # スペース一覧を表示 - python3 gchat_read.py --list-spaces -""" - -import argparse -import json -import os -import sys -from pathlib import Path - -# google-auth スキルの get_credentials() を使う。 -# 環境変数 GOOGLE_AUTH_SCRIPTS で google-auth/scripts のパスを指定可能。 -# 未指定の場合は次の候補を順に探す: -# 1. ~/.claude/skills/google-auth/scripts (uttarov 互換) -# 2. ../../google-auth/scripts (ndf プラグイン内の隣接スキル) -_CANDIDATES = ( - os.environ.get("GOOGLE_AUTH_SCRIPTS"), - os.path.expanduser("~/.claude/skills/google-auth/scripts"), - str(Path(__file__).resolve().parent.parent.parent / "google-auth" / "scripts"), -) -for _p in _CANDIDATES: - if _p and os.path.isdir(_p): - if _p not in sys.path: - sys.path.insert(0, _p) - break -from google_auth import get_credentials as _get_credentials # type: ignore # noqa: E402 - -from google.apps import chat_v1 as google_chat # noqa: E402 - -SCOPES = [ - 'chat.messages.readonly', - 'chat.spaces.readonly', -] - -DEFAULT_SPACE_ID = os.environ.get('GCHAT_DEFAULT_SPACE') or None -DEFAULT_OUTPUT = '/tmp/gchat_messages.json' - - -def get_credentials(): - return _get_credentials(SCOPES) - - -def create_client(): - creds = get_credentials() - full_scopes = [f'https://www.googleapis.com/auth/{s}' for s in SCOPES] - return google_chat.ChatServiceClient( - credentials=creds, - client_options={"scopes": full_scopes}, - ) - - -def list_spaces(): - client = create_client() - request = google_chat.ListSpacesRequest(filter='space_type = "SPACE"') - for space in client.list_spaces(request): - print(f"{space.name} - {space.display_name}") - - -def list_messages(space_id, page_size=200, filter_str=None, order_by='createTime DESC'): - client = create_client() - kwargs = { - 'parent': f'spaces/{space_id}', - 'page_size': page_size, - 'order_by': order_by, - } - if filter_str: - kwargs['filter'] = filter_str - - request = google_chat.ListMessagesRequest(**kwargs) - messages = [] - for message in client.list_messages(request): - d = type(message).to_dict(message) - - # attachment からDriveリンクを抽出 - attachments = [] - for att in (d.get('attachment') or []): - drive_ref = att.get('drive_data_ref') or {} - file_id = drive_ref.get('drive_file_id') - content_name = att.get('content_name', '') - content_type = att.get('content_type', '') - if file_id: - url = f'https://drive.google.com/file/d/{file_id}/view' - if 'folder' in content_type: - url = f'https://drive.google.com/drive/folders/{file_id}' - elif 'spreadsheet' in content_type: - url = f'https://docs.google.com/spreadsheets/d/{file_id}/edit' - elif 'document' in content_type: - url = f'https://docs.google.com/document/d/{file_id}/edit' - elif 'presentation' in content_type: - url = f'https://docs.google.com/presentation/d/{file_id}/edit' - attachments.append({ - 'name': content_name, - 'type': content_type, - 'url': url, - }) - - # annotation からrich_linkを抽出 - rich_links = [] - for ann in (d.get('annotations') or []): - rl = ann.get('rich_link_metadata') - if rl and rl.get('uri'): - rich_links.append(rl['uri']) - - messages.append({ - 'name': message.name, - 'sender': message.sender.name if message.sender else None, - 'create_time': message.create_time.isoformat() if message.create_time else None, - 'text': message.text or '', - 'thread': message.thread.name if message.thread else None, - 'attachments': attachments, - 'rich_links': rich_links, - }) - return messages - - -def main(): - parser = argparse.ArgumentParser(description='Google Chat メッセージ読み取り') - parser.add_argument('--space', default=DEFAULT_SPACE_ID, - help='Space ID (URL末尾。env GCHAT_DEFAULT_SPACE で既定値も指定可)') - parser.add_argument('--output', default=DEFAULT_OUTPUT, help='出力ファイルパス') - parser.add_argument('--after', help='この日時以降のメッセージを取得 (RFC-3339形式)') - parser.add_argument('--before', help='この日時以前のメッセージを取得 (RFC-3339形式)') - parser.add_argument('--list-spaces', action='store_true', help='スペース一覧を表示') - 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- Bash - - Read - - Write - - Edit - - Glob - - Grep - - AskUserQuestion - - Task ---- - -# Knowledge Reorg Command - -AGENTS.md・Skills・docsを「AI Agent Knowledge Architecture Policy」に基づいて分析・整理する。 - -## 入力 - -$ARGUMENTS - -## ポリシー（AI Agent Knowledge Architecture） - -知識を以下の3層に分離する。**実際のパスはリポジトリやAIエージェントごとに異なる。** - -| 層 | 役割 | -|----|------| -| エントリポイント | ナビゲーション + ポリシー（軽量、300行以下推奨） | -| ドキュメント | リポジトリ知識（アーキテクチャ、モジュール説明、依存関係） | -| スキル | 実行可能なワークフロー（手順、コマンド、チェックリスト） | - -### マルチエージェント対応戦略 - -**推奨**: `AGENTS.md`を共通エントリポイントとし、各エージェント固有ファイルからインポートで参照する。 - -``` -AGENTS.md ← 共通エントリポイント（本体） -.claude/CLAUDE.md ← @../AGENTS.md でインポート -.gemini/GEMINI.md ← @../AGENTS.md でインポート -（Codex CLI） ← AGENTS.md を直接読込（設定不要） -（Kiro CLI） ← AGENTS.md を直接読込（設定不要） -``` - -#### エージェント固有ファイルの設定例 - -**Claude Code** (`.claude/CLAUDE.md`): -```markdown -@../AGENTS.md -``` - -**Gemini CLI** (`.gemini/GEMINI.md`): -```markdown -@../AGENTS.md -``` -または`.gemini/settings.json`で: -```json -{ "context": { "fileName": ["AGENTS.md", "GEMINI.md"] } } -``` - -**Codex CLI / Kiro CLI**: `AGENTS.md`をネイティブに読込。追加設定不要。 - -#### エージェント別ファイル配置リファレンス - -##### エントリポイント - -| エージェント | 固有ファイル | グローバル | AGENTS.md | -|-------------|------------|-----------|-----------| -| Claude Code | `.claude/CLAUDE.md` | `~/.claude/CLAUDE.md` | `@../AGENTS.md`でインポート | -| Kiro CLI | `.kiro/steering/*.md` | `~/.kiro/steering/*.md` | 直接読込 | -| Codex CLI | なし（AGENTS.md標準） | `~/.codex/AGENTS.md` | 直接読込 | -| Gemini CLI | `.gemini/GEMINI.md` | `~/.gemini/GEMINI.md` | `@../AGENTS.md`でインポート or settings.json | - -**補足**: -- Kiro CLIはsteering体系（`product.md`/`tech.md`/`structure.md`）で構造化指示を管理 -- Codex CLIは`AGENTS.override.md`による上書き機構あり -- Gemini CLIは`@`構文でネストインポート対応（循環検出あり、最大深度5） - -##### スキル配置 - -| エージェント | プロジェクト | ユーザー | -|-------------|------------|---------| -| Claude Code | `.claude/skills//SKILL.md` | `~/.claude/skills//SKILL.md` | -| Kiro CLI | `.kiro/skills//SKILL.md` | なし（エージェント経由） | -| Codex CLI | `.agents/skills//SKILL.md` | `~/.agents/skills//SKILL.md` | -| Gemini CLI | `.agents/skills//SKILL.md` or `.gemini/skills//SKILL.md` | `~/.agents/skills//SKILL.md` or `~/.gemini/skills//SKILL.md` | - -**共通**: 全エージェントが`SKILL.md`ファイルを採用（Agent Skillsオープンスタンダードに収束傾向）。 - -##### ドキュメント - -ドキュメントディレクトリはエージェント標準では規定されていない。`docs/`が一般的な慣例。 - -### 0) パス検出（実行時に最初に行う） - -整理を始める前に、対象リポジトリで以下を検出する: - -1. **エントリポイント**: 上表を参考に、存在するファイルを特定（複数エージェント対応の場合はすべて列挙） -2. **ドキュメントディレクトリ**: `docs/`が存在するか、なければ作成先を提案 -3. **スキルディレクトリ**: 上表を参考に、存在するディレクトリを特定 -4. **対象エージェント**: どのエージェント向けに整理するかを確認 - -検出結果を`AskUserQuestion`でユーザーに確認し、以降の作業で使用する。 - -### エントリポイントに含めるべきもの - -- リポジトリ概要 -- ドキュメントへのナビゲーション -- エージェント行動ルール -- スキルへの参照 - -### エントリポイントに含めてはいけないもの - -- 詳細なアーキテクチャ説明 -- データベーススキーマ -- 長い説明文 -- 運用手順（スキルへ） - -### ドキュメントに含めるべきもの - -- システムアーキテクチャ -- リポジトリ構造の説明 -- モジュール説明 -- 依存関係 -- インフラ概要 -- 設計思想 - -### スキルに含めるべきもの - -- ステップバイステップの手順 -- 実行コマンド -- バリデーションルール -- チェックリスト - -## 手順 - -### 1) 現状分析 - -まず「0) パス検出」でエントリポイント・ドキュメントディレクトリ・スキルディレクトリを特定した上で、以下を分析する: - -1. **エントリポイントの行数とトークン概算を計測** - - 300行以上なら「肥大化」と判定 - - 含まれている情報の種類を分類（ナビゲーション/知識/手順/ポリシー） - -2. **ドキュメントディレクトリの状態を確認** - - 存在するか - - どの程度の知識が格納されているか - -3. **スキルディレクトリの状態を確認** - - 各スキルの役割分類 - - 知識がスキルに混入していないか - -4. **Serena memoryの状態を確認**（`--migrate-memory`指定時） - - `.serena/memories/`の内容を一覧 - - 各メモリーの分類（知識→ドキュメント / 手順→スキル / 一時的→削除候補） - -### 2) 分析レポート作成 - -以下の形式でユーザーに報告する: - -```markdown -## 現状分析レポート - -### エントリポイント（{検出したファイル名}） -- 行数: X行（目標: 300行以下） -- 状態: 適正 / 肥大化 -- 含まれる情報の内訳: - - ナビゲーション: X行 - - 知識（ドキュメント移動候補）: X行 - - 手順（スキル移動候補）: X行 - - ポリシー: X行 - -### ドキュメント（{検出したパス}） -- 状態: 未作成 / 不足 / 適正 -- 不足している知識: [リスト] - -### スキル（{検出したパス}） -- 状態: 適正 / 知識混入あり -- 問題のあるスキル: [リスト] - -### Serena Memory（該当時） -- 移行対象: X件 - - ドキュメント移動: X件 - - スキル移動: X件 - - 削除候補: X件 -``` - -### 3) 整理計画の提案 - -`AskUserQuestion`で以下を確認: - -1. **対象スコープ**: AGENTS.md / skills / docs / all -2. **実行モード**: dry-run（レポートのみ） / 実行（実際に変更） -3. **Serena memory移行**: する / しない - -### 4) 実行（dry-runでない場合） - -#### エントリポイント整理 -- 詳細な知識をドキュメントディレクトリに抽出 -- 手順をスキルディレクトリに抽出 -- エントリポイントをナビゲーション+ポリシーのみに圧縮 -- ドキュメントへのリンクを追加 - -#### ドキュメント整理 -- 必要なサブディレクトリを作成（architecture/, modules/など） -- エントリポイントから抽出した知識を配置 -- Serena memoryから移行（該当時） - -#### スキル整理 -- 知識が混入しているスキルを特定 -- 知識部分をドキュメントに抽出し、スキルからはリンク参照に変更 - -#### Serena memory移行（該当時） -以下のルールで再配置: - -| Memory種別 | 移行先 | -|-----------|--------| -| リポジトリ構造 | ドキュメント | -| アーキテクチャ説明 | ドキュメント | -| モジュール説明 | ドキュメント | -| 依存関係 | ドキュメント | -| エージェント手順 | スキル | -| 一時的な調査結果 | 削除 | -| タスク履歴 | 削除 | - -### 5) 検証 - -整理後に以下を検証: -- エントリポイントが300行以下か -- 3層構造（エントリポイント/ドキュメント/スキル）が守られているか -- リンク切れがないか -- マルチエージェント互換性（Claude Code, Codex CLI, Gemini CLI, Kiro） - -### 6) 完了報告 - -```markdown -## 整理完了レポート - -### 変更サマリー -- エントリポイント: X行 → Y行（Z行削減） -- ドキュメント: X件のファイルを追加/更新 -- スキル: X件のスキルを整理 -- Serena memory: X件を移行、Y件を削除候補 - -### 作成/変更したファイル -- [ファイル一覧] - -### 次のステップ -- [ ] 変更内容のレビュー -- [ ] 不要なSerena memoryの削除（手動確認推奨） -- [ ] コミット＆PR作成（`/ndf:pr`） -``` - -## マルチエージェント互換性 - -整理時は以下を遵守: -- ツール固有のフォーマットを避ける -- Serena固有の知識ストレージに依存しない -- プレーンMarkdownを使用（図はmermaidで記述） -- 安定したディレクトリ構造を維持 - -## 注意事項 - -- `--dry-run`指定時はレポートのみ出力し、ファイルを変更しない -- 大規模な変更前は必ずユーザー確認を取る -- 既存のリンクやパス参照を壊さないよう注意する -- AGENTS.md内のClaude Code固有設定（CLAUDE.md参照など）は保持する diff --git a/plugins/ndf-shared/skills/logging-guidelines/SKILL.md b/plugins/ndf-shared/skills/logging-guidelines/SKILL.md index 007b9691..3ad64b34 100644 --- a/plugins/ndf-shared/skills/logging-guidelines/SKILL.md +++ b/plugins/ndf-shared/skills/logging-guidelines/SKILL.md @@ -1,7 +1,19 @@ --- name: logging-guidelines -description: "Design safe and useful application logging." -when_to_use: "コードにログを追加・修正・整理するとき。Triggers: 'ログ追加', 'log追加', 'logger', 'logging', 'ログレベル', 'log level', 'デバッグログ', 'エラーログ', 'logger.info', 'logger.error', 'print文をログに'" +description: "Choose log levels and write safe, useful application logs when adding or reworking logging in code（ログ追加 / logger / ログレベル / デバッグログ / エラーログ / print文をログに）. Use when editing source code that emits logs, to pick the level and keep secrets and personal data out of the output." +paths: + - "**/*.py" + - "**/*.ts" + - "**/*.tsx" + - "**/*.js" + - "**/*.jsx" + - "**/*.go" + - "**/*.rb" + - "**/*.java" + - "**/*.kt" + - "**/*.php" + - "**/*.rs" + - "**/*.sh" --- # ログ運用ガイドライン diff --git a/plugins/ndf-shared/skills/mcp-builder/LICENSE.txt b/plugins/ndf-shared/skills/mcp-builder/LICENSE.txt deleted file mode 100644 index 4f881c52..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/LICENSE.txt +++ /dev/null @@ -1,202 +0,0 @@ - - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks. - ---- - -# Process - -## 🚀 High-Level Workflow - -Creating a high-quality MCP server involves four main phases: - -### Phase 1: Deep Research and Planning - -#### 1.1 Understand Modern MCP Design - -**API Coverage vs. Workflow Tools:** -Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage. - -**Tool Naming and Discoverability:** -Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., `github_create_issue`, `github_list_repos`) and action-oriented naming. - -**Context Management:** -Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently. - -**Actionable Error Messages:** -Error messages should guide agents toward solutions with specific suggestions and next steps. - -#### 1.2 Study MCP Protocol Documentation - -**Navigate the MCP specification:** - -Start with the sitemap to find relevant pages: `https://modelcontextprotocol.io/sitemap.xml` - -Then fetch specific pages with `.md` suffix for markdown format (e.g., `https://modelcontextprotocol.io/specification/draft.md`). - -Key pages to review: -- Specification overview and architecture -- Transport mechanisms (streamable HTTP, stdio) -- Tool, resource, and prompt definitions - -#### 1.3 Study Framework Documentation - -**Recommended stack:** -- **Language**: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code, benefiting from its broad usage, static typing and good linting tools) -- **Transport**: Streamable HTTP for remote servers, using stateless JSON (simpler to scale and maintain, as opposed to stateful sessions and streaming responses). stdio for local servers. - -**Load framework documentation:** - -- **MCP Best Practices**: [📋 View Best Practices](./reference/mcp_best_practices.md) - Core guidelines - -**For TypeScript (recommended):** -- **TypeScript SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md` -- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns and examples - -**For Python:** -- **Python SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md` -- [🐍 Python Guide](./reference/python_mcp_server.md) - Python patterns and examples - -#### 1.4 Plan Your Implementation - -**Understand the API:** -Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed. - -**Tool Selection:** -Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations. - ---- - -### Phase 2: Implementation - -#### 2.1 Set Up Project Structure - -See language-specific guides for project setup: -- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json -- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization, dependencies - -#### 2.2 Implement Core Infrastructure - -Create shared utilities: -- API client with authentication -- Error handling helpers -- Response formatting (JSON/Markdown) -- Pagination support - -#### 2.3 Implement Tools - -For each tool: - -**Input Schema:** -- Use Zod (TypeScript) or Pydantic (Python) -- Include constraints and clear descriptions -- Add examples in field descriptions - -**Output Schema:** -- Define `outputSchema` where possible for structured data -- Use `structuredContent` in tool responses (TypeScript SDK feature) -- Helps clients understand and process tool outputs - -**Tool Description:** -- Concise summary of functionality -- Parameter descriptions -- Return type schema - -**Implementation:** -- Async/await for I/O operations -- Proper error handling with actionable messages -- Support pagination where applicable -- Return both text content and structured data when using modern SDKs - -**Annotations:** -- `readOnlyHint`: true/false -- `destructiveHint`: true/false -- `idempotentHint`: true/false -- `openWorldHint`: true/false - ---- - -### Phase 3: Review and Test - -#### 3.1 Code Quality - -Review for: -- No duplicated code (DRY principle) -- Consistent error handling -- Full type coverage -- Clear tool descriptions - -#### 3.2 Build and Test - -**TypeScript:** -- Run `npm run build` to verify compilation -- Test with MCP Inspector: `npx @modelcontextprotocol/inspector` - -**Python:** -- Verify syntax: `python -m py_compile your_server.py` -- Test with MCP Inspector - -See language-specific guides for detailed testing approaches and quality checklists. - ---- - -### Phase 4: Create Evaluations - -After implementing your MCP server, create comprehensive evaluations to test its effectiveness. - -**Load [✅ Evaluation Guide](./reference/evaluation.md) for complete evaluation guidelines.** - -#### 4.1 Understand Evaluation Purpose - -Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions. - -#### 4.2 Create 10 Evaluation Questions - -To create effective evaluations, follow the process outlined in the evaluation guide: - -1. **Tool Inspection**: List available tools and understand their capabilities -2. **Content Exploration**: Use READ-ONLY operations to explore available data -3. **Question Generation**: Create 10 complex, realistic questions -4. **Answer Verification**: Solve each question yourself to verify answers - -#### 4.3 Evaluation Requirements - -Ensure each question is: -- **Independent**: Not dependent on other questions -- **Read-only**: Only non-destructive operations required -- **Complex**: Requiring multiple tool calls and deep exploration -- **Realistic**: Based on real use cases humans would care about -- **Verifiable**: Single, clear answer that can be verified by string comparison -- **Stable**: Answer won't change over time - -#### 4.4 Output Format - -Create an XML file with this structure: - -```xml - - - Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat? - 3 - - - -``` - ---- - -# Reference Files - -## 📚 Documentation Library - -Load these resources as needed during development: - -### Core MCP Documentation (Load First) -- **MCP Protocol**: Start with sitemap at `https://modelcontextprotocol.io/sitemap.xml`, then fetch specific pages with `.md` suffix -- [📋 MCP Best Practices](./reference/mcp_best_practices.md) - Universal MCP guidelines including: - - Server and tool naming conventions - - Response format guidelines (JSON vs Markdown) - - Pagination best practices - - Transport selection (streamable HTTP vs stdio) - - Security and error handling standards - -### SDK Documentation (Load During Phase 1/2) -- **Python SDK**: Fetch from `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md` -- **TypeScript SDK**: Fetch from `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md` - -### Language-Specific Implementation Guides (Load During Phase 2) -- [🐍 Python Implementation Guide](./reference/python_mcp_server.md) - Complete Python/FastMCP guide with: - - Server initialization patterns - - Pydantic model examples - - Tool registration with `@mcp.tool` - - Complete working examples - - Quality checklist - -- [⚡ TypeScript Implementation Guide](./reference/node_mcp_server.md) - Complete TypeScript guide with: - - Project structure - - Zod schema patterns - - Tool registration with `server.registerTool` - - Complete working examples - - Quality checklist - -### Evaluation Guide (Load During Phase 4) -- [✅ Evaluation Guide](./reference/evaluation.md) - Complete evaluation creation guide with: - - Question creation guidelines - - Answer verification strategies - - XML format specifications - - Example questions and answers - - Running an evaluation with the provided scripts diff --git a/plugins/ndf-shared/skills/mcp-builder/reference/evaluation.md b/plugins/ndf-shared/skills/mcp-builder/reference/evaluation.md deleted file mode 100644 index 87e9bb78..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/reference/evaluation.md +++ /dev/null @@ -1,602 +0,0 @@ -# MCP Server Evaluation Guide - -## Overview - -This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions using only the tools provided. - ---- - -## Quick Reference - -### Evaluation Requirements -- Create 10 human-readable questions -- Questions must be READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE -- Each question requires multiple tool calls (potentially dozens) -- Answers must be single, verifiable values -- Answers must be STABLE (won't change over time) - -### Output Format -```xml - - - Your question here - Single verifiable answer - - -``` - ---- - -## Purpose of Evaluations - -The measure of quality of an MCP server is NOT how well or comprehensively the server implements tools, but how well these implementations (input/output schemas, docstrings/descriptions, functionality) enable LLMs with no other context and access ONLY to the MCP servers to answer realistic and difficult questions. - -## Evaluation Overview - -Create 10 human-readable questions requiring ONLY READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE, and IDEMPOTENT operations to answer. Each question should be: -- Realistic -- Clear and concise -- Unambiguous -- Complex, requiring potentially dozens of tool calls or steps -- Answerable with a single, verifiable value that you identify in advance - -## Question Guidelines - -### Core Requirements - -1. **Questions MUST be independent** - - Each question should NOT depend on the answer to any other question - - Should not assume prior write operations from processing another question - -2. **Questions MUST require ONLY NON-DESTRUCTIVE AND IDEMPOTENT tool use** - - Should not instruct or require modifying state to arrive at the correct answer - -3. **Questions must be REALISTIC, CLEAR, CONCISE, and COMPLEX** - - Must require another LLM to use multiple (potentially dozens of) tools or steps to answer - -### Complexity and Depth - -4. **Questions must require deep exploration** - - Consider multi-hop questions requiring multiple sub-questions and sequential tool calls - - Each step should benefit from information found in previous questions - -5. **Questions may require extensive paging** - - May need paging through multiple pages of results - - May require querying old data (1-2 years out-of-date) to find niche information - - The questions must be DIFFICULT - -6. **Questions must require deep understanding** - - Rather than surface-level knowledge - - May pose complex ideas as True/False questions requiring evidence - - May use multiple-choice format where LLM must search different hypotheses - -7. **Questions must not be solvable with straightforward keyword search** - - Do not include specific keywords from the target content - - Use synonyms, related concepts, or paraphrases - - Require multiple searches, analyzing multiple related items, extracting context, then deriving the answer - -### Tool Testing - -8. **Questions should stress-test tool return values** - - May elicit tools returning large JSON objects or lists, overwhelming the LLM - - Should require understanding multiple modalities of data: - - IDs and names - - Timestamps and datetimes (months, days, years, seconds) - - File IDs, names, extensions, and mimetypes - - URLs, GIDs, etc. - - Should probe the tool's ability to return all useful forms of data - -9. **Questions should MOSTLY reflect real human use cases** - - The kinds of information retrieval tasks that HUMANS assisted by an LLM would care about - -10. **Questions may require dozens of tool calls** - - This challenges LLMs with limited context - - Encourages MCP server tools to reduce information returned - -11. **Include ambiguous questions** - - May be ambiguous OR require difficult decisions on which tools to call - - Force the LLM to potentially make mistakes or misinterpret - - Ensure that despite AMBIGUITY, there is STILL A SINGLE VERIFIABLE ANSWER - -### Stability - -12. **Questions must be designed so the answer DOES NOT CHANGE** - - Do not ask questions that rely on "current state" which is dynamic - - For example, do not count: - - Number of reactions to a post - - Number of replies to a thread - - Number of members in a channel - -13. **DO NOT let the MCP server RESTRICT the kinds of questions you create** - - Create challenging and complex questions - - Some may not be solvable with the available MCP server tools - - Questions may require specific output formats (datetime vs. epoch time, JSON vs. MARKDOWN) - - Questions may require dozens of tool calls to complete - -## Answer Guidelines - -### Verification - -1. **Answers must be VERIFIABLE via direct string comparison** - - If the answer can be re-written in many formats, clearly specify the output format in the QUESTION - - Examples: "Use YYYY/MM/DD.", "Respond True or False.", "Answer A, B, C, or D and nothing else." - - Answer should be a single VERIFIABLE value such as: - - User ID, user name, display name, first name, last name - - Channel ID, channel name - - Message ID, string - - URL, title - - Numerical quantity - - Timestamp, datetime - - Boolean (for True/False questions) - - Email address, phone number - - File ID, file name, file extension - - Multiple choice answer - - Answers must not require special formatting or complex, structured output - - Answer will be verified using DIRECT STRING COMPARISON - -### Readability - -2. **Answers should generally prefer HUMAN-READABLE formats** - - Examples: names, first name, last name, datetime, file name, message string, URL, yes/no, true/false, a/b/c/d - - Rather than opaque IDs (though IDs are acceptable) - - The VAST MAJORITY of answers should be human-readable - -### Stability - -3. **Answers must be STABLE/STATIONARY** - - Look at old content (e.g., conversations that have ended, projects that have launched, questions answered) - - Create QUESTIONS based on "closed" concepts that will always return the same answer - - Questions may ask to consider a fixed time window to insulate from non-stationary answers - - Rely on context UNLIKELY to change - - Example: if finding a paper name, be SPECIFIC enough so answer is not confused with papers published later - -4. **Answers must be CLEAR and UNAMBIGUOUS** - - Questions must be designed so there is a single, clear answer - - Answer can be derived from using the MCP server tools - -### Diversity - -5. **Answers must be DIVERSE** - - Answer should be a single VERIFIABLE value in diverse modalities and formats - - User concept: user ID, user name, display name, first name, last name, email address, phone number - - Channel concept: channel ID, channel name, channel topic - - Message concept: message ID, message string, timestamp, month, day, year - -6. **Answers must NOT be complex structures** - - Not a list of values - - Not a complex object - - Not a list of IDs or strings - - Not natural language text - - UNLESS the answer can be straightforwardly verified using DIRECT STRING COMPARISON - - And can be realistically reproduced - - It should be unlikely that an LLM would return the same list in any other order or format - -## Evaluation Process - -### Step 1: Documentation Inspection - -Read the documentation of the target API to understand: -- Available endpoints and functionality -- If ambiguity exists, fetch additional information from the web -- Parallelize this step AS MUCH AS POSSIBLE -- Ensure each subagent is ONLY examining documentation from the file system or on the web - -### Step 2: Tool Inspection - -List the tools available in the MCP server: -- Inspect the MCP server directly -- Understand input/output schemas, docstrings, and descriptions -- WITHOUT calling the tools themselves at this stage - -### Step 3: Developing Understanding - -Repeat steps 1 & 2 until you have a good understanding: -- Iterate multiple times -- Think about the kinds of tasks you want to create -- Refine your understanding -- At NO stage should you READ the code of the MCP server implementation itself -- Use your intuition and understanding to create reasonable, realistic, but VERY challenging tasks - -### Step 4: Read-Only Content Inspection - -After understanding the API and tools, USE the MCP server tools: -- Inspect content using READ-ONLY and NON-DESTRUCTIVE operations ONLY -- Goal: identify specific content (e.g., users, channels, messages, projects, tasks) for creating realistic questions -- Should NOT call any tools that modify state -- Will NOT read the code of the MCP server implementation itself -- Parallelize this step with individual sub-agents pursuing independent explorations -- Ensure each subagent is only performing READ-ONLY, NON-DESTRUCTIVE, and IDEMPOTENT operations -- BE CAREFUL: SOME TOOLS may return LOTS OF DATA which would cause you to run out of CONTEXT -- Make INCREMENTAL, SMALL, AND TARGETED tool calls for exploration -- In all tool call requests, use the `limit` parameter to limit results (<10) -- Use pagination - -### Step 5: Task Generation - -After inspecting the content, create 10 human-readable questions: -- An LLM should be able to answer these with the MCP server -- Follow all question and answer guidelines above - -## Output Format - -Each QA pair consists of a question and an answer. The output should be an XML file with this structure: - -```xml - - - Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name? - Website Redesign - - - Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username. - sarah_dev - - - Look for pull requests that modified files in the /api directory and were merged between January 1 and January 31, 2024. How many different contributors worked on these PRs? - 7 - - - Find the repository with the most stars that was created before 2023. What is the repository name? - data-pipeline - - -``` - -## Evaluation Examples - -### Good Questions - -**Example 1: Multi-hop question requiring deep exploration (GitHub MCP)** -```xml - - Find the repository that was archived in Q3 2023 and had previously been the most forked project in the organization. What was the primary programming language used in that repository? - Python - -``` - -This question is good because: -- Requires multiple searches to find archived repositories -- Needs to identify which had the most forks before archival -- Requires examining repository details for the language -- Answer is a simple, verifiable value -- Based on historical (closed) data that won't change - -**Example 2: Requires understanding context without keyword matching (Project Management MCP)** -```xml - - Locate the initiative focused on improving customer onboarding that was completed in late 2023. The project lead created a retrospective document after completion. What was the lead's role title at that time? - Product Manager - -``` - -This question is good because: -- Doesn't use specific project name ("initiative focused on improving customer onboarding") -- Requires finding completed projects from specific timeframe -- Needs to identify the project lead and their role -- Requires understanding context from retrospective documents -- Answer is human-readable and stable -- Based on completed work (won't change) - -**Example 3: Complex aggregation requiring multiple steps (Issue Tracker MCP)** -```xml - - Among all bugs reported in January 2024 that were marked as critical priority, which assignee resolved the highest percentage of their assigned bugs within 48 hours? Provide the assignee's username. - alex_eng - -``` - -This question is good because: -- Requires filtering bugs by date, priority, and status -- Needs to group by assignee and calculate resolution rates -- Requires understanding timestamps to determine 48-hour windows -- Tests pagination (potentially many bugs to process) -- Answer is a single username -- Based on historical data from specific time period - -**Example 4: Requires synthesis across multiple data types (CRM MCP)** -```xml - - Find the account that upgraded from the Starter to Enterprise plan in Q4 2023 and had the highest annual contract value. What industry does this account operate in? - Healthcare - -``` - -This question is good because: -- Requires understanding subscription tier changes -- Needs to identify upgrade events in specific timeframe -- Requires comparing contract values -- Must access account industry information -- Answer is simple and verifiable -- Based on completed historical transactions - -### Poor Questions - -**Example 1: Answer changes over time** -```xml - - How many open issues are currently assigned to the engineering team? - 47 - -``` - -This question is poor because: -- The answer will change as issues are created, closed, or reassigned -- Not based on stable/stationary data -- Relies on "current state" which is dynamic - -**Example 2: Too easy with keyword search** -```xml - - Find the pull request with title "Add authentication feature" and tell me who created it. - developer123 - -``` - -This question is poor because: -- Can be solved with a straightforward keyword search for exact title -- Doesn't require deep exploration or understanding -- No synthesis or analysis needed - -**Example 3: Ambiguous answer format** -```xml - - List all the repositories that have Python as their primary language. - repo1, repo2, repo3, data-pipeline, ml-tools - -``` - -This question is poor because: -- Answer is a list that could be returned in any order -- Difficult to verify with direct string comparison -- LLM might format differently (JSON array, comma-separated, newline-separated) -- Better to ask for a specific aggregate (count) or superlative (most stars) - -## Verification Process - -After creating evaluations: - -1. **Examine the XML file** to understand the schema -2. **Load each task instruction** and in parallel using the MCP server and tools, identify the correct answer by attempting to solve the task YOURSELF -3. **Flag any operations** that require WRITE or DESTRUCTIVE operations -4. **Accumulate all CORRECT answers** and replace any incorrect answers in the document -5. **Remove any ``** that require WRITE or DESTRUCTIVE operations - -Remember to parallelize solving tasks to avoid running out of context, then accumulate all answers and make changes to the file at the end. - -## Tips for Creating Quality Evaluations - -1. **Think Hard and Plan Ahead** before generating tasks -2. **Parallelize Where Opportunity Arises** to speed up the process and manage context -3. **Focus on Realistic Use Cases** that humans would actually want to accomplish -4. **Create Challenging Questions** that test the limits of the MCP server's capabilities -5. **Ensure Stability** by using historical data and closed concepts -6. **Verify Answers** by solving the questions yourself using the MCP server tools -7. **Iterate and Refine** based on what you learn during the process - ---- - -# Running Evaluations - -After creating your evaluation file, you can use the provided evaluation harness to test your MCP server. - -## Setup - -1. **Install Dependencies** - - ```bash - pip install -r scripts/requirements.txt - ``` - - Or install manually: - ```bash - pip install anthropic mcp - ``` - -2. **Set API Key** - - ```bash - export ANTHROPIC_API_KEY=your_api_key_here - ``` - -## Evaluation File Format - -Evaluation files use XML format with `` elements: - -```xml - - - Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name? - Website Redesign - - - Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username. - sarah_dev - - -``` - -## Running Evaluations - -The evaluation script (`scripts/evaluation.py`) supports three transport types: - -**Important:** -- **stdio transport**: The evaluation script automatically launches and manages the MCP server process for you. Do not run the server manually. -- **sse/http transports**: You must start the MCP server separately before running the evaluation. The script connects to the already-running server at the specified URL. - -### 1. Local STDIO Server - -For locally-run MCP servers (script launches the server automatically): - -```bash -python scripts/evaluation.py \ - -t stdio \ - -c python \ - -a my_mcp_server.py \ - evaluation.xml -``` - -With environment variables: -```bash -python scripts/evaluation.py \ - -t stdio \ - -c python \ - -a my_mcp_server.py \ - -e API_KEY=abc123 \ - -e DEBUG=true \ - evaluation.xml -``` - -### 2. Server-Sent Events (SSE) - -For SSE-based MCP servers (you must start the server first): - -```bash -python scripts/evaluation.py \ - -t sse \ - -u https://example.com/mcp \ - -H "Authorization: Bearer token123" \ - -H "X-Custom-Header: value" \ - evaluation.xml -``` - -### 3. HTTP (Streamable HTTP) - -For HTTP-based MCP servers (you must start the server first): - -```bash -python scripts/evaluation.py \ - -t http \ - -u https://example.com/mcp \ - -H "Authorization: Bearer token123" \ - evaluation.xml -``` - -## Command-Line Options - -``` -usage: evaluation.py [-h] [-t {stdio,sse,http}] [-m MODEL] [-c COMMAND] - [-a ARGS [ARGS ...]] [-e ENV [ENV ...]] [-u URL] - [-H HEADERS [HEADERS ...]] [-o OUTPUT] - eval_file - -positional arguments: - eval_file Path to evaluation XML file - -optional arguments: - -h, --help Show help message - -t, --transport Transport type: stdio, sse, or http (default: stdio) - -m, --model Claude model to use (default: claude-3-7-sonnet-20250219) - -o, --output Output file for report (default: print to stdout) - -stdio options: - -c, --command Command to run MCP server (e.g., python, node) - -a, --args Arguments for the command (e.g., server.py) - -e, --env Environment variables in KEY=VALUE format - -sse/http options: - -u, --url MCP server URL - -H, --header HTTP headers in 'Key: Value' format -``` - -## Output - -The evaluation script generates a detailed report including: - -- **Summary Statistics**: - - Accuracy (correct/total) - - Average task duration - - Average tool calls per task - - Total tool calls - -- **Per-Task Results**: - - Prompt and expected response - - Actual response from the agent - - Whether the answer was correct (✅/❌) - - Duration and tool call details - - Agent's summary of its approach - - Agent's feedback on the tools - -### Save Report to File - -```bash -python scripts/evaluation.py \ - -t stdio \ - -c python \ - -a my_server.py \ - -o evaluation_report.md \ - evaluation.xml -``` - -## Complete Example Workflow - -Here's a complete example of creating and running an evaluation: - -1. **Create your evaluation file** (`my_evaluation.xml`): - -```xml - - - Find the user who created the most issues in January 2024. What is their username? - alice_developer - - - Among all pull requests merged in Q1 2024, which repository had the highest number? Provide the repository name. - backend-api - - - Find the project that was completed in December 2023 and had the longest duration from start to finish. How many days did it take? - 127 - - -``` - -2. **Install dependencies**: - -```bash -pip install -r scripts/requirements.txt -export ANTHROPIC_API_KEY=your_api_key -``` - -3. **Run evaluation**: - -```bash -python scripts/evaluation.py \ - -t stdio \ - -c python \ - -a github_mcp_server.py \ - -e GITHUB_TOKEN=ghp_xxx \ - -o github_eval_report.md \ - my_evaluation.xml -``` - -4. **Review the report** in `github_eval_report.md` to: - - See which questions passed/failed - - Read the agent's feedback on your tools - - Identify areas for improvement - - Iterate on your MCP server design - -## Troubleshooting - -### Connection Errors - -If you get connection errors: -- **STDIO**: Verify the command and arguments are correct -- **SSE/HTTP**: Check the URL is accessible and headers are correct -- Ensure any required API keys are set in environment variables or headers - -### Low Accuracy - -If many evaluations fail: -- Review the agent's feedback for each task -- Check if tool descriptions are clear and comprehensive -- Verify input parameters are well-documented -- Consider whether tools return too much or too little data -- Ensure error messages are actionable - -### Timeout Issues - -If tasks are timing out: -- Use a more capable model (e.g., `claude-3-7-sonnet-20250219`) -- Check if tools are returning too much data -- Verify pagination is working correctly -- Consider simplifying complex questions \ No newline at end of file diff --git a/plugins/ndf-shared/skills/mcp-builder/reference/mcp_best_practices.md b/plugins/ndf-shared/skills/mcp-builder/reference/mcp_best_practices.md deleted file mode 100644 index b9d343cc..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/reference/mcp_best_practices.md +++ /dev/null @@ -1,249 +0,0 @@ -# MCP Server Best Practices - -## Quick Reference - -### Server Naming -- **Python**: `{service}_mcp` (e.g., `slack_mcp`) -- **Node/TypeScript**: `{service}-mcp-server` (e.g., `slack-mcp-server`) - -### Tool Naming -- Use snake_case with service prefix -- Format: `{service}_{action}_{resource}` -- Example: `slack_send_message`, `github_create_issue` - -### Response Formats -- Support both JSON and Markdown formats -- JSON for programmatic processing -- Markdown for human readability - -### Pagination -- Always respect `limit` parameter -- Return `has_more`, `next_offset`, `total_count` -- Default to 20-50 items - -### Transport -- **Streamable HTTP**: For remote servers, multi-client scenarios -- **stdio**: For local integrations, command-line tools -- Avoid SSE (deprecated in favor of streamable HTTP) - ---- - -## Server Naming Conventions - -Follow these standardized naming patterns: - -**Python**: Use format `{service}_mcp` (lowercase with underscores) -- Examples: `slack_mcp`, `github_mcp`, `jira_mcp` - -**Node/TypeScript**: Use format `{service}-mcp-server` (lowercase with hyphens) -- Examples: `slack-mcp-server`, `github-mcp-server`, `jira-mcp-server` - -The name should be general, descriptive of the service being integrated, easy to infer from the task description, and without version numbers. - ---- - -## Tool Naming and Design - -### Tool Naming - -1. **Use snake_case**: `search_users`, `create_project`, `get_channel_info` -2. **Include service prefix**: Anticipate that your MCP server may be used alongside other MCP servers - - Use `slack_send_message` instead of just `send_message` - - Use `github_create_issue` instead of just `create_issue` -3. **Be action-oriented**: Start with verbs (get, list, search, create, etc.) -4. **Be specific**: Avoid generic names that could conflict with other servers - -### Tool Design - -- Tool descriptions must narrowly and unambiguously describe functionality -- Descriptions must precisely match actual functionality -- Provide tool annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) -- Keep tool operations focused and atomic - ---- - -## Response Formats - -All tools that return data should support multiple formats: - -### JSON Format (`response_format="json"`) -- Machine-readable structured data -- Include all available fields and metadata -- Consistent field names and types -- Use for programmatic processing - -### Markdown Format (`response_format="markdown"`, typically default) -- Human-readable formatted text -- Use headers, lists, and formatting for clarity -- Convert timestamps to human-readable format -- Show display names with IDs in parentheses -- Omit verbose metadata - ---- - -## Pagination - -For tools that list resources: - -- **Always respect the `limit` parameter** -- **Implement pagination**: Use `offset` or cursor-based pagination -- **Return pagination metadata**: Include `has_more`, `next_offset`/`next_cursor`, `total_count` -- **Never load all results into memory**: Especially important for large datasets -- **Default to reasonable limits**: 20-50 items is typical - -Example pagination response: -```json -{ - "total": 150, - "count": 20, - "offset": 0, - "items": [...], - "has_more": true, - "next_offset": 20 -} -``` - ---- - -## Transport Options - -### Streamable HTTP - -**Best for**: Remote servers, web services, multi-client scenarios - -**Characteristics**: -- Bidirectional communication over HTTP -- Supports multiple simultaneous clients -- Can be deployed as a web service -- Enables server-to-client notifications - -**Use when**: -- Serving multiple clients simultaneously -- Deploying as a cloud service -- Integration with web applications - -### stdio - -**Best for**: Local integrations, command-line tools - -**Characteristics**: -- Standard input/output stream communication -- Simple setup, no network configuration needed -- Runs as a subprocess of the client - -**Use when**: -- Building tools for local development environments -- Integrating with desktop applications -- Single-user, single-session scenarios - -**Note**: stdio servers should NOT log to stdout (use stderr for logging) - -### Transport Selection - -| Criterion | stdio | Streamable HTTP | -|-----------|-------|-----------------| -| **Deployment** | Local | Remote | -| **Clients** | Single | Multiple | -| **Complexity** | Low | Medium | -| **Real-time** | No | Yes | - ---- - -## Security Best Practices - -### Authentication and Authorization - -**OAuth 2.1**: -- Use secure OAuth 2.1 with certificates from recognized authorities -- Validate access tokens before processing requests -- Only accept tokens specifically intended for your server - -**API Keys**: -- Store API keys in environment variables, never in code -- Validate keys on server startup -- Provide clear error messages when authentication fails - -### Input Validation - -- Sanitize file paths to prevent directory traversal -- Validate URLs and external identifiers -- Check parameter sizes and ranges -- Prevent command injection in system calls -- Use schema validation (Pydantic/Zod) for all inputs - -### Error Handling - -- Don't expose internal errors to clients -- Log security-relevant errors server-side -- Provide helpful but not revealing error messages -- Clean up resources after errors - -### DNS Rebinding Protection - -For streamable HTTP servers running locally: -- Enable DNS rebinding protection -- Validate the `Origin` header on all incoming connections -- Bind to `127.0.0.1` rather than `0.0.0.0` - ---- - -## Tool Annotations - -Provide annotations to help clients understand tool behavior: - -| Annotation | Type | Default | Description | -|-----------|------|---------|-------------| -| `readOnlyHint` | boolean | false | Tool does not modify its environment | -| `destructiveHint` | boolean | true | Tool may perform destructive updates | -| `idempotentHint` | boolean | false | Repeated calls with same args have no additional effect | -| `openWorldHint` | boolean | true | Tool interacts with external entities | - -**Important**: Annotations are hints, not security guarantees. Clients should not make security-critical decisions based solely on annotations. - ---- - -## Error Handling - -- Use standard JSON-RPC error codes -- Report tool errors within result objects (not protocol-level errors) -- Provide helpful, specific error messages with suggested next steps -- Don't expose internal implementation details -- Clean up resources properly on errors - -Example error handling: -```typescript -try { - const result = performOperation(); - return { content: [{ type: "text", text: result }] }; -} catch (error) { - return { - isError: true, - content: [{ - type: "text", - text: `Error: ${error.message}. Try using filter='active_only' to reduce results.` - }] - }; -} -``` - ---- - -## Testing Requirements - -Comprehensive testing should cover: - -- **Functional testing**: Verify correct execution with valid/invalid inputs -- **Integration testing**: Test interaction with external systems -- **Security testing**: Validate auth, input sanitization, rate limiting -- **Performance testing**: Check behavior under load, timeouts -- **Error handling**: Ensure proper error reporting and cleanup - ---- - -## Documentation Requirements - -- Provide clear documentation of all tools and capabilities -- Include working examples (at least 3 per major feature) -- Document security considerations -- Specify required permissions and access levels -- Document rate limits and performance characteristics diff --git a/plugins/ndf-shared/skills/mcp-builder/reference/node_mcp_server.md b/plugins/ndf-shared/skills/mcp-builder/reference/node_mcp_server.md deleted file mode 100644 index f6e5df98..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/reference/node_mcp_server.md +++ /dev/null @@ -1,970 +0,0 @@ -# Node/TypeScript MCP Server Implementation Guide - -## Overview - -This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structure, server setup, tool registration patterns, input validation with Zod, error handling, and complete working examples. - ---- - -## Quick Reference - -### Key Imports -```typescript -import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; -import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js"; -import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; -import express from "express"; -import { z } from "zod"; -``` - -### Server Initialization -```typescript -const server = new McpServer({ - name: "service-mcp-server", - version: "1.0.0" -}); -``` - -### Tool Registration Pattern -```typescript -server.registerTool( - "tool_name", - { - title: "Tool Display Name", - description: "What the tool does", - inputSchema: { param: z.string() }, - outputSchema: { result: z.string() } - }, - async ({ param }) => { - const output = { result: `Processed: ${param}` }; - return { - content: [{ type: "text", text: JSON.stringify(output) }], - structuredContent: output // Modern pattern for structured data - }; - } -); -``` - ---- - -## MCP TypeScript SDK - -The official MCP TypeScript SDK provides: -- `McpServer` class for server initialization -- `registerTool` method for tool registration -- Zod schema integration for runtime input validation -- Type-safe tool handler implementations - -**IMPORTANT - Use Modern APIs Only:** -- **DO use**: `server.registerTool()`, `server.registerResource()`, `server.registerPrompt()` -- **DO NOT use**: Old deprecated APIs such as `server.tool()`, `server.setRequestHandler(ListToolsRequestSchema, ...)`, or manual handler registration -- The `register*` methods provide better type safety, automatic schema handling, and are the recommended approach - -See the MCP SDK documentation in the references for complete details. - -## Server Naming Convention - -Node/TypeScript MCP servers must follow this naming pattern: -- **Format**: `{service}-mcp-server` (lowercase with hyphens) -- **Examples**: `github-mcp-server`, `jira-mcp-server`, `stripe-mcp-server` - -The name should be: -- General (not tied to specific features) -- Descriptive of the service/API being integrated -- Easy to infer from the task description -- Without version numbers or dates - -## Project Structure - -Create the following structure for Node/TypeScript MCP servers: - -``` -{service}-mcp-server/ -├── package.json -├── tsconfig.json -├── README.md -├── src/ -│ ├── index.ts # Main entry point with McpServer initialization -│ ├── types.ts # TypeScript type definitions and interfaces -│ ├── tools/ # Tool implementations (one file per domain) -│ ├── services/ # API clients and shared utilities -│ ├── schemas/ # Zod validation schemas -│ └── constants.ts # Shared constants (API_URL, CHARACTER_LIMIT, etc.) -└── dist/ # Built JavaScript files (entry point: dist/index.js) -``` - -## Tool Implementation - -### Tool Naming - -Use snake_case for tool names (e.g., "search_users", "create_project", "get_channel_info") with clear, action-oriented names. - -**Avoid Naming Conflicts**: Include the service context to prevent overlaps: -- Use "slack_send_message" instead of just "send_message" -- Use "github_create_issue" instead of just "create_issue" -- Use "asana_list_tasks" instead of just "list_tasks" - -### Tool Structure - -Tools are registered using the `registerTool` method with the following requirements: -- Use Zod schemas for runtime input validation and type safety -- The `description` field must be explicitly provided - JSDoc comments are NOT automatically extracted -- Explicitly provide `title`, `description`, `inputSchema`, and `annotations` -- The `inputSchema` must be a Zod schema object (not a JSON schema) -- Type all parameters and return values explicitly - -```typescript -import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; -import { z } from "zod"; - -const server = new McpServer({ - name: "example-mcp", - version: "1.0.0" -}); - -// Zod schema for input validation -const UserSearchInputSchema = z.object({ - query: z.string() - .min(2, "Query must be at least 2 characters") - .max(200, "Query must not exceed 200 characters") - .describe("Search string to match against names/emails"), - limit: z.number() - .int() - .min(1) - .max(100) - .default(20) - .describe("Maximum results to return"), - offset: z.number() - .int() - .min(0) - .default(0) - .describe("Number of results to skip for pagination"), - response_format: z.nativeEnum(ResponseFormat) - .default(ResponseFormat.MARKDOWN) - .describe("Output format: 'markdown' for human-readable or 'json' for machine-readable") -}).strict(); - -// Type definition from Zod schema -type UserSearchInput = z.infer; - -server.registerTool( - "example_search_users", - { - title: "Search Example Users", - description: `Search for users in the Example system by name, email, or team. - -This tool searches across all user profiles in the Example platform, supporting partial matches and various search filters. It does NOT create or modify users, only searches existing ones. - -Args: - - query (string): Search string to match against names/emails - - limit (number): Maximum results to return, between 1-100 (default: 20) - - offset (number): Number of results to skip for pagination (default: 0) - - response_format ('markdown' | 'json'): Output format (default: 'markdown') - -Returns: - For JSON format: Structured data with schema: - { - "total": number, // Total number of matches found - "count": number, // Number of results in this response - "offset": number, // Current pagination offset - "users": [ - { - "id": string, // User ID (e.g., "U123456789") - "name": string, // Full name (e.g., "John Doe") - "email": string, // Email address - "team": string, // Team name (optional) - "active": boolean // Whether user is active - } - ], - "has_more": boolean, // Whether more results are available - "next_offset": number // Offset for next page (if has_more is true) - } - -Examples: - - Use when: "Find all marketing team members" -> params with query="team:marketing" - - Use when: "Search for John's account" -> params with query="john" - - Don't use when: You need to create a user (use example_create_user instead) - -Error Handling: - - Returns "Error: Rate limit exceeded" if too many requests (429 status) - - Returns "No users found matching ''" if search returns empty`, - inputSchema: UserSearchInputSchema, - annotations: { - readOnlyHint: true, - destructiveHint: false, - idempotentHint: true, - openWorldHint: true - } - }, - async (params: UserSearchInput) => { - try { - // Input validation is handled by Zod schema - // Make API request using validated parameters - const data = await makeApiRequest( - "users/search", - "GET", - undefined, - { - q: params.query, - limit: params.limit, - offset: params.offset - } - ); - - const users = data.users || []; - const total = data.total || 0; - - if (!users.length) { - return { - content: [{ - type: "text", - text: `No users found matching '${params.query}'` - }] - }; - } - - // Prepare structured output - const output = { - total, - count: users.length, - offset: params.offset, - users: users.map((user: any) => ({ - id: user.id, - name: user.name, - email: user.email, - ...(user.team ? { team: user.team } : {}), - active: user.active ?? true - })), - has_more: total > params.offset + users.length, - ...(total > params.offset + users.length ? { - next_offset: params.offset + users.length - } : {}) - }; - - // Format text representation based on requested format - let textContent: string; - if (params.response_format === ResponseFormat.MARKDOWN) { - const lines = [`# User Search Results: '${params.query}'`, "", - `Found ${total} users (showing ${users.length})`, ""]; - for (const user of users) { - lines.push(`## ${user.name} (${user.id})`); - lines.push(`- **Email**: ${user.email}`); - if (user.team) lines.push(`- **Team**: ${user.team}`); - lines.push(""); - } - textContent = lines.join("\n"); - } else { - textContent = JSON.stringify(output, null, 2); - } - - return { - content: [{ type: "text", text: textContent }], - structuredContent: output // Modern pattern for structured data - }; - } catch (error) { - return { - content: [{ - type: "text", - text: handleApiError(error) - }] - }; - } - } -); -``` - -## Zod Schemas for Input Validation - -Zod provides runtime type validation: - -```typescript -import { z } from "zod"; - -// Basic schema with validation -const CreateUserSchema = z.object({ - name: z.string() - .min(1, "Name is required") - .max(100, "Name must not exceed 100 characters"), - email: z.string() - .email("Invalid email format"), - age: z.number() - .int("Age must be a whole number") - .min(0, "Age cannot be negative") - .max(150, "Age cannot be greater than 150") -}).strict(); // Use .strict() to forbid extra fields - -// Enums -enum ResponseFormat { - MARKDOWN = "markdown", - JSON = "json" -} - -const SearchSchema = z.object({ - response_format: z.nativeEnum(ResponseFormat) - .default(ResponseFormat.MARKDOWN) - .describe("Output format") -}); - -// Optional fields with defaults -const PaginationSchema = z.object({ - limit: z.number() - .int() - .min(1) - .max(100) - .default(20) - .describe("Maximum results to return"), - offset: z.number() - .int() - .min(0) - .default(0) - .describe("Number of results to skip") -}); -``` - -## Response Format Options - -Support multiple output formats for flexibility: - -```typescript -enum ResponseFormat { - MARKDOWN = "markdown", - JSON = "json" -} - -const inputSchema = z.object({ - query: z.string(), - response_format: z.nativeEnum(ResponseFormat) - .default(ResponseFormat.MARKDOWN) - .describe("Output format: 'markdown' for human-readable or 'json' for machine-readable") -}); -``` - -**Markdown format**: -- Use headers, lists, and formatting for clarity -- Convert timestamps to human-readable format -- Show display names with IDs in parentheses -- Omit verbose metadata -- Group related information logically - -**JSON format**: -- Return complete, structured data suitable for programmatic processing -- Include all available fields and metadata -- Use consistent field names and types - -## Pagination Implementation - -For tools that list resources: - -```typescript -const ListSchema = z.object({ - limit: z.number().int().min(1).max(100).default(20), - offset: z.number().int().min(0).default(0) -}); - -async function listItems(params: z.infer) { - const data = await apiRequest(params.limit, params.offset); - - const response = { - total: data.total, - count: data.items.length, - offset: params.offset, - items: data.items, - has_more: data.total > params.offset + data.items.length, - next_offset: data.total > params.offset + data.items.length - ? params.offset + data.items.length - : undefined - }; - - return JSON.stringify(response, null, 2); -} -``` - -## Character Limits and Truncation - -Add a CHARACTER_LIMIT constant to prevent overwhelming responses: - -```typescript -// At module level in constants.ts -export const CHARACTER_LIMIT = 25000; // Maximum response size in characters - -async function searchTool(params: SearchInput) { - let result = generateResponse(data); - - // Check character limit and truncate if needed - if (result.length > CHARACTER_LIMIT) { - const truncatedData = data.slice(0, Math.max(1, data.length / 2)); - response.data = truncatedData; - response.truncated = true; - response.truncation_message = - `Response truncated from ${data.length} to ${truncatedData.length} items. ` + - `Use 'offset' parameter or add filters to see more results.`; - result = JSON.stringify(response, null, 2); - } - - return result; -} -``` - -## Error Handling - -Provide clear, actionable error messages: - -```typescript -import axios, { AxiosError } from "axios"; - -function handleApiError(error: unknown): string { - if (error instanceof AxiosError) { - if (error.response) { - switch (error.response.status) { - case 404: - return "Error: Resource not found. Please check the ID is correct."; - case 403: - return "Error: Permission denied. You don't have access to this resource."; - case 429: - return "Error: Rate limit exceeded. Please wait before making more requests."; - default: - return `Error: API request failed with status ${error.response.status}`; - } - } else if (error.code === "ECONNABORTED") { - return "Error: Request timed out. Please try again."; - } - } - return `Error: Unexpected error occurred: ${error instanceof Error ? error.message : String(error)}`; -} -``` - -## Shared Utilities - -Extract common functionality into reusable functions: - -```typescript -// Shared API request function -async function makeApiRequest( - endpoint: string, - method: "GET" | "POST" | "PUT" | "DELETE" = "GET", - data?: any, - params?: any -): Promise { - try { - const response = await axios({ - method, - url: `${API_BASE_URL}/${endpoint}`, - data, - params, - timeout: 30000, - headers: { - "Content-Type": "application/json", - "Accept": "application/json" - } - }); - return response.data; - } catch (error) { - throw error; - } -} -``` - -## Async/Await Best Practices - -Always use async/await for network requests and I/O operations: - -```typescript -// Good: Async network request -async function fetchData(resourceId: string): Promise { - const response = await axios.get(`${API_URL}/resource/${resourceId}`); - return response.data; -} - -// Bad: Promise chains -function fetchData(resourceId: string): Promise { - return axios.get(`${API_URL}/resource/${resourceId}`) - .then(response => response.data); // Harder to read and maintain -} -``` - -## TypeScript Best Practices - -1. **Use Strict TypeScript**: Enable strict mode in tsconfig.json -2. **Define Interfaces**: Create clear interface definitions for all data structures -3. **Avoid `any`**: Use proper types or `unknown` instead of `any` -4. **Zod for Runtime Validation**: Use Zod schemas to validate external data -5. **Type Guards**: Create type guard functions for complex type checking -6. **Error Handling**: Always use try-catch with proper error type checking -7. **Null Safety**: Use optional chaining (`?.`) and nullish coalescing (`??`) - -```typescript -// Good: Type-safe with Zod and interfaces -interface UserResponse { - id: string; - name: string; - email: string; - team?: string; - active: boolean; -} - -const UserSchema = z.object({ - id: z.string(), - name: z.string(), - email: z.string().email(), - team: z.string().optional(), - active: z.boolean() -}); - -type User = z.infer; - -async function getUser(id: string): Promise { - const data = await apiCall(`/users/${id}`); - return UserSchema.parse(data); // Runtime validation -} - -// Bad: Using any -async function getUser(id: string): Promise { - return await apiCall(`/users/${id}`); // No type safety -} -``` - -## Package Configuration - -### package.json - -```json -{ - "name": "{service}-mcp-server", - "version": "1.0.0", - "description": "MCP server for {Service} API integration", - "type": "module", - "main": "dist/index.js", - "scripts": { - "start": "node dist/index.js", - "dev": "tsx watch src/index.ts", - "build": "tsc", - "clean": "rm -rf dist" - }, - "engines": { - "node": ">=18" - }, - "dependencies": { - "@modelcontextprotocol/sdk": "^1.6.1", - "axios": "^1.7.9", - "zod": "^3.23.8" - }, - "devDependencies": { - "@types/node": "^22.10.0", - "tsx": "^4.19.2", - "typescript": "^5.7.2" - } -} -``` - -### tsconfig.json - -```json -{ - "compilerOptions": { - "target": "ES2022", - "module": "Node16", - "moduleResolution": "Node16", - "lib": ["ES2022"], - "outDir": "./dist", - "rootDir": "./src", - "strict": true, - "esModuleInterop": true, - "skipLibCheck": true, - "forceConsistentCasingInFileNames": true, - "declaration": true, - "declarationMap": true, - "sourceMap": true, - "allowSyntheticDefaultImports": true - }, - "include": ["src/**/*"], - "exclude": ["node_modules", "dist"] -} -``` - -## Complete Example - -```typescript -#!/usr/bin/env node -/** - * MCP Server for Example Service. - * - * This server provides tools to interact with Example API, including user search, - * project management, and data export capabilities. - */ - -import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; -import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; -import { z } from "zod"; -import axios, { AxiosError } from "axios"; - -// Constants -const API_BASE_URL = "https://api.example.com/v1"; -const CHARACTER_LIMIT = 25000; - -// Enums -enum ResponseFormat { - MARKDOWN = "markdown", - JSON = "json" -} - -// Zod schemas -const UserSearchInputSchema = z.object({ - query: z.string() - .min(2, "Query must be at least 2 characters") - .max(200, "Query must not exceed 200 characters") - .describe("Search string to match against names/emails"), - limit: z.number() - .int() - .min(1) - .max(100) - .default(20) - .describe("Maximum results to return"), - offset: z.number() - .int() - .min(0) - .default(0) - .describe("Number of results to skip for pagination"), - response_format: z.nativeEnum(ResponseFormat) - .default(ResponseFormat.MARKDOWN) - .describe("Output format: 'markdown' for human-readable or 'json' for machine-readable") -}).strict(); - -type UserSearchInput = z.infer; - -// Shared utility functions -async function makeApiRequest( - endpoint: string, - method: "GET" | "POST" | "PUT" | "DELETE" = "GET", - data?: any, - params?: any -): Promise { - try { - const response = await axios({ - method, - url: `${API_BASE_URL}/${endpoint}`, - data, - params, - timeout: 30000, - headers: { - "Content-Type": "application/json", - "Accept": "application/json" - } - }); - return response.data; - } catch (error) { - throw error; - } -} - -function handleApiError(error: unknown): string { - if (error instanceof AxiosError) { - if (error.response) { - switch (error.response.status) { - case 404: - return "Error: Resource not found. Please check the ID is correct."; - case 403: - return "Error: Permission denied. You don't have access to this resource."; - case 429: - return "Error: Rate limit exceeded. Please wait before making more requests."; - default: - return `Error: API request failed with status ${error.response.status}`; - } - } else if (error.code === "ECONNABORTED") { - return "Error: Request timed out. Please try again."; - } - } - return `Error: Unexpected error occurred: ${error instanceof Error ? error.message : String(error)}`; -} - -// Create MCP server instance -const server = new McpServer({ - name: "example-mcp", - version: "1.0.0" -}); - -// Register tools -server.registerTool( - "example_search_users", - { - title: "Search Example Users", - description: `[Full description as shown above]`, - inputSchema: UserSearchInputSchema, - annotations: { - readOnlyHint: true, - destructiveHint: false, - idempotentHint: true, - openWorldHint: true - } - }, - async (params: UserSearchInput) => { - // Implementation as shown above - } -); - -// Main function -// For stdio (local): -async function runStdio() { - if (!process.env.EXAMPLE_API_KEY) { - console.error("ERROR: EXAMPLE_API_KEY environment variable is required"); - process.exit(1); - } - - const transport = new StdioServerTransport(); - await server.connect(transport); - console.error("MCP server running via stdio"); -} - -// For streamable HTTP (remote): -async function runHTTP() { - if (!process.env.EXAMPLE_API_KEY) { - console.error("ERROR: EXAMPLE_API_KEY environment variable is required"); - process.exit(1); - } - - const app = express(); - app.use(express.json()); - - app.post('/mcp', async (req, res) => { - const transport = new StreamableHTTPServerTransport({ - sessionIdGenerator: undefined, - enableJsonResponse: true - }); - res.on('close', () => transport.close()); - await server.connect(transport); - await transport.handleRequest(req, res, req.body); - }); - - const port = parseInt(process.env.PORT || '3000'); - app.listen(port, () => { - console.error(`MCP server running on http://localhost:${port}/mcp`); - }); -} - -// Choose transport based on environment -const transport = process.env.TRANSPORT || 'stdio'; -if (transport === 'http') { - runHTTP().catch(error => { - console.error("Server error:", error); - process.exit(1); - }); -} else { - runStdio().catch(error => { - console.error("Server error:", error); - process.exit(1); - }); -} -``` - ---- - -## Advanced MCP Features - -### Resource Registration - -Expose data as resources for efficient, URI-based access: - -```typescript -import { ResourceTemplate } from "@modelcontextprotocol/sdk/types.js"; - -// Register a resource with URI template -server.registerResource( - { - uri: "file://documents/{name}", - name: "Document Resource", - description: "Access documents by name", - mimeType: "text/plain" - }, - async (uri: string) => { - // Extract parameter from URI - const match = uri.match(/^file:\/\/documents\/(.+)$/); - if (!match) { - throw new Error("Invalid URI format"); - } - - const documentName = match[1]; - const content = await loadDocument(documentName); - - return { - contents: [{ - uri, - mimeType: "text/plain", - text: content - }] - }; - } -); - -// List available resources dynamically -server.registerResourceList(async () => { - const documents = await getAvailableDocuments(); - return { - resources: documents.map(doc => ({ - uri: `file://documents/${doc.name}`, - name: doc.name, - mimeType: "text/plain", - description: doc.description - })) - }; -}); -``` - -**When to use Resources vs Tools:** -- **Resources**: For data access with simple URI-based parameters -- **Tools**: For complex operations requiring validation and business logic -- **Resources**: When data is relatively static or template-based -- **Tools**: When operations have side effects or complex workflows - -### Transport Options - -The TypeScript SDK supports two main transport mechanisms: - -#### Streamable HTTP (Recommended for Remote Servers) - -```typescript -import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js"; -import express from "express"; - -const app = express(); -app.use(express.json()); - -app.post('/mcp', async (req, res) => { - // Create new transport for each request (stateless, prevents request ID collisions) - const transport = new StreamableHTTPServerTransport({ - sessionIdGenerator: undefined, - enableJsonResponse: true - }); - - res.on('close', () => transport.close()); - - await server.connect(transport); - await transport.handleRequest(req, res, req.body); -}); - -app.listen(3000); -``` - -#### stdio (For Local Integrations) - -```typescript -import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; - -const transport = new StdioServerTransport(); -await server.connect(transport); -``` - -**Transport selection:** -- **Streamable HTTP**: Web services, remote access, multiple clients -- **stdio**: Command-line tools, local development, subprocess integration - -### Notification Support - -Notify clients when server state changes: - -```typescript -// Notify when tools list changes -server.notification({ - method: "notifications/tools/list_changed" -}); - -// Notify when resources change -server.notification({ - method: "notifications/resources/list_changed" -}); -``` - -Use notifications sparingly - only when server capabilities genuinely change. - ---- - -## Code Best Practices - -### Code Composability and Reusability - -Your implementation MUST prioritize composability and code reuse: - -1. **Extract Common Functionality**: - - Create reusable helper functions for operations used across multiple tools - - Build shared API clients for HTTP requests instead of duplicating code - - Centralize error handling logic in utility functions - - Extract business logic into dedicated functions that can be composed - - Extract shared markdown or JSON field selection & formatting functionality - -2. **Avoid Duplication**: - - NEVER copy-paste similar code between tools - - If you find yourself writing similar logic twice, extract it into a function - - Common operations like pagination, filtering, field selection, and formatting should be shared - - Authentication/authorization logic should be centralized - -## Building and Running - -Always build your TypeScript code before running: - -```bash -# Build the project -npm run build - -# Run the server -npm start - -# Development with auto-reload -npm run dev -``` - -Always ensure `npm run build` completes successfully before considering the implementation complete. - -## Quality Checklist - -Before finalizing your Node/TypeScript MCP server implementation, ensure: - -### Strategic Design -- [ ] Tools enable complete workflows, not just API endpoint wrappers -- [ ] Tool names reflect natural task subdivisions -- [ ] Response formats optimize for agent context efficiency -- [ ] Human-readable identifiers used where appropriate -- [ ] Error messages guide agents toward correct usage - -### Implementation Quality -- [ ] FOCUSED IMPLEMENTATION: Most important and valuable tools implemented -- [ ] All tools registered using `registerTool` with complete configuration -- [ ] All tools include `title`, `description`, `inputSchema`, and `annotations` -- [ ] Annotations correctly set (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) -- [ ] All tools use Zod schemas for runtime input validation with `.strict()` enforcement -- [ ] All Zod schemas have proper constraints and descriptive error messages -- [ ] All tools have comprehensive descriptions with explicit input/output types -- [ ] Descriptions include return value examples and complete schema documentation -- [ ] Error messages are clear, actionable, and educational - -### TypeScript Quality -- [ ] TypeScript interfaces are defined for all data structures -- [ ] Strict TypeScript is enabled in tsconfig.json -- [ ] No use of `any` type - use `unknown` or proper types instead -- [ ] All async functions have explicit Promise return types -- [ ] Error handling uses proper type guards (e.g., `axios.isAxiosError`, `z.ZodError`) - -### Advanced Features (where applicable) -- [ ] Resources registered for appropriate data endpoints -- [ ] Appropriate transport configured (stdio or streamable HTTP) -- [ ] Notifications implemented for dynamic server capabilities -- [ ] Type-safe with SDK interfaces - -### Project Configuration -- [ ] Package.json includes all necessary dependencies -- [ ] Build script produces working JavaScript in dist/ directory -- [ ] Main entry point is properly configured as dist/index.js -- [ ] Server name follows format: `{service}-mcp-server` -- [ ] tsconfig.json properly configured with strict mode - -### Code Quality -- [ ] Pagination is properly implemented where applicable -- [ ] Large responses check CHARACTER_LIMIT constant and truncate with clear messages -- [ ] Filtering options are provided for potentially large result sets -- [ ] All network operations handle timeouts and connection errors gracefully -- [ ] Common functionality is extracted into reusable functions -- [ ] Return types are consistent across similar operations - -### Testing and Build -- [ ] `npm run build` completes successfully without errors -- [ ] dist/index.js created and executable -- [ ] Server runs: `node dist/index.js --help` -- [ ] All imports resolve correctly -- [ ] Sample tool calls work as expected \ No newline at end of file diff --git a/plugins/ndf-shared/skills/mcp-builder/reference/python_mcp_server.md b/plugins/ndf-shared/skills/mcp-builder/reference/python_mcp_server.md deleted file mode 100644 index cf7ec996..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/reference/python_mcp_server.md +++ /dev/null @@ -1,719 +0,0 @@ -# Python MCP Server Implementation Guide - -## Overview - -This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples. - ---- - -## Quick Reference - -### Key Imports -```python -from mcp.server.fastmcp import FastMCP -from pydantic import BaseModel, Field, field_validator, ConfigDict -from typing import Optional, List, Dict, Any -from enum import Enum -import httpx -``` - -### Server Initialization -```python -mcp = FastMCP("service_mcp") -``` - -### Tool Registration Pattern -```python -@mcp.tool(name="tool_name", annotations={...}) -async def tool_function(params: InputModel) -> str: - # Implementation - pass -``` - ---- - -## MCP Python SDK and FastMCP - -The official MCP Python SDK provides FastMCP, a high-level framework for building MCP servers. It provides: -- Automatic description and inputSchema generation from function signatures and docstrings -- Pydantic model integration for input validation -- Decorator-based tool registration with `@mcp.tool` - -**For complete SDK documentation, use WebFetch to load:** -`https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md` - -## Server Naming Convention - -Python MCP servers must follow this naming pattern: -- **Format**: `{service}_mcp` (lowercase with underscores) -- **Examples**: `github_mcp`, `jira_mcp`, `stripe_mcp` - -The name should be: -- General (not tied to specific features) -- Descriptive of the service/API being integrated -- Easy to infer from the task description -- Without version numbers or dates - -## Tool Implementation - -### Tool Naming - -Use snake_case for tool names (e.g., "search_users", "create_project", "get_channel_info") with clear, action-oriented names. - -**Avoid Naming Conflicts**: Include the service context to prevent overlaps: -- Use "slack_send_message" instead of just "send_message" -- Use "github_create_issue" instead of just "create_issue" -- Use "asana_list_tasks" instead of just "list_tasks" - -### Tool Structure with FastMCP - -Tools are defined using the `@mcp.tool` decorator with Pydantic models for input validation: - -```python -from pydantic import BaseModel, Field, ConfigDict -from mcp.server.fastmcp import FastMCP - -# Initialize the MCP server -mcp = FastMCP("example_mcp") - -# Define Pydantic model for input validation -class ServiceToolInput(BaseModel): - '''Input model for service tool operation.''' - model_config = ConfigDict( - str_strip_whitespace=True, # Auto-strip whitespace from strings - validate_assignment=True, # Validate on assignment - extra='forbid' # Forbid extra fields - ) - - param1: str = Field(..., description="First parameter description (e.g., 'user123', 'project-abc')", min_length=1, max_length=100) - param2: Optional[int] = Field(default=None, description="Optional integer parameter with constraints", ge=0, le=1000) - tags: Optional[List[str]] = Field(default_factory=list, description="List of tags to apply", max_items=10) - -@mcp.tool( - name="service_tool_name", - annotations={ - "title": "Human-Readable Tool Title", - "readOnlyHint": True, # Tool does not modify environment - "destructiveHint": False, # Tool does not perform destructive operations - "idempotentHint": True, # Repeated calls have no additional effect - "openWorldHint": False # Tool does not interact with external entities - } -) -async def service_tool_name(params: ServiceToolInput) -> str: - '''Tool description automatically becomes the 'description' field. - - This tool performs a specific operation on the service. It validates all inputs - using the ServiceToolInput Pydantic model before processing. - - Args: - params (ServiceToolInput): Validated input parameters containing: - - param1 (str): First parameter description - - param2 (Optional[int]): Optional parameter with default - - tags (Optional[List[str]]): List of tags - - Returns: - str: JSON-formatted response containing operation results - ''' - # Implementation here - pass -``` - -## Pydantic v2 Key Features - -- Use `model_config` instead of nested `Config` class -- Use `field_validator` instead of deprecated `validator` -- Use `model_dump()` instead of deprecated `dict()` -- Validators require `@classmethod` decorator -- Type hints are required for validator methods - -```python -from pydantic import BaseModel, Field, field_validator, ConfigDict - -class CreateUserInput(BaseModel): - model_config = ConfigDict( - str_strip_whitespace=True, - validate_assignment=True - ) - - name: str = Field(..., description="User's full name", min_length=1, max_length=100) - email: str = Field(..., description="User's email address", pattern=r'^[\w\.-]+@[\w\.-]+\.\w+$') - age: int = Field(..., description="User's age", ge=0, le=150) - - @field_validator('email') - @classmethod - def validate_email(cls, v: str) -> str: - if not v.strip(): - raise ValueError("Email cannot be empty") - return v.lower() -``` - -## Response Format Options - -Support multiple output formats for flexibility: - -```python -from enum import Enum - -class ResponseFormat(str, Enum): - '''Output format for tool responses.''' - MARKDOWN = "markdown" - JSON = "json" - -class UserSearchInput(BaseModel): - query: str = Field(..., description="Search query") - response_format: ResponseFormat = Field( - default=ResponseFormat.MARKDOWN, - description="Output format: 'markdown' for human-readable or 'json' for machine-readable" - ) -``` - -**Markdown format**: -- Use headers, lists, and formatting for clarity -- Convert timestamps to human-readable format (e.g., "2024-01-15 10:30:00 UTC" instead of epoch) -- Show display names with IDs in parentheses (e.g., "@john.doe (U123456)") -- Omit verbose metadata (e.g., show only one profile image URL, not all sizes) -- Group related information logically - -**JSON format**: -- Return complete, structured data suitable for programmatic processing -- Include all available fields and metadata -- Use consistent field names and types - -## Pagination Implementation - -For tools that list resources: - -```python -class ListInput(BaseModel): - limit: Optional[int] = Field(default=20, description="Maximum results to return", ge=1, le=100) - offset: Optional[int] = Field(default=0, description="Number of results to skip for pagination", ge=0) - -async def list_items(params: ListInput) -> str: - # Make API request with pagination - data = await api_request(limit=params.limit, offset=params.offset) - - # Return pagination info - response = { - "total": data["total"], - "count": len(data["items"]), - "offset": params.offset, - "items": data["items"], - "has_more": data["total"] > params.offset + len(data["items"]), - "next_offset": params.offset + len(data["items"]) if data["total"] > params.offset + len(data["items"]) else None - } - return json.dumps(response, indent=2) -``` - -## Error Handling - -Provide clear, actionable error messages: - -```python -def _handle_api_error(e: Exception) -> str: - '''Consistent error formatting across all tools.''' - if isinstance(e, httpx.HTTPStatusError): - if e.response.status_code == 404: - return "Error: Resource not found. Please check the ID is correct." - elif e.response.status_code == 403: - return "Error: Permission denied. You don't have access to this resource." - elif e.response.status_code == 429: - return "Error: Rate limit exceeded. Please wait before making more requests." - return f"Error: API request failed with status {e.response.status_code}" - elif isinstance(e, httpx.TimeoutException): - return "Error: Request timed out. Please try again." - return f"Error: Unexpected error occurred: {type(e).__name__}" -``` - -## Shared Utilities - -Extract common functionality into reusable functions: - -```python -# Shared API request function -async def _make_api_request(endpoint: str, method: str = "GET", **kwargs) -> dict: - '''Reusable function for all API calls.''' - async with httpx.AsyncClient() as client: - response = await client.request( - method, - f"{API_BASE_URL}/{endpoint}", - timeout=30.0, - **kwargs - ) - response.raise_for_status() - return response.json() -``` - -## Async/Await Best Practices - -Always use async/await for network requests and I/O operations: - -```python -# Good: Async network request -async def fetch_data(resource_id: str) -> dict: - async with httpx.AsyncClient() as client: - response = await client.get(f"{API_URL}/resource/{resource_id}") - response.raise_for_status() - return response.json() - -# Bad: Synchronous request -def fetch_data(resource_id: str) -> dict: - response = requests.get(f"{API_URL}/resource/{resource_id}") # Blocks - return response.json() -``` - -## Type Hints - -Use type hints throughout: - -```python -from typing import Optional, List, Dict, Any - -async def get_user(user_id: str) -> Dict[str, Any]: - data = await fetch_user(user_id) - return {"id": data["id"], "name": data["name"]} -``` - -## Tool Docstrings - -Every tool must have comprehensive docstrings with explicit type information: - -```python -async def search_users(params: UserSearchInput) -> str: - ''' - Search for users in the Example system by name, email, or team. - - This tool searches across all user profiles in the Example platform, - supporting partial matches and various search filters. It does NOT - create or modify users, only searches existing ones. - - Args: - params (UserSearchInput): Validated input parameters containing: - - query (str): Search string to match against names/emails (e.g., "john", "@example.com", "team:marketing") - - limit (Optional[int]): Maximum results to return, between 1-100 (default: 20) - - offset (Optional[int]): Number of results to skip for pagination (default: 0) - - Returns: - str: JSON-formatted string containing search results with the following schema: - - Success response: - { - "total": int, # Total number of matches found - "count": int, # Number of results in this response - "offset": int, # Current pagination offset - "users": [ - { - "id": str, # User ID (e.g., "U123456789") - "name": str, # Full name (e.g., "John Doe") - "email": str, # Email address (e.g., "john@example.com") - "team": str # Team name (e.g., "Marketing") - optional - } - ] - } - - Error response: - "Error: " or "No users found matching ''" - - Examples: - - Use when: "Find all marketing team members" -> params with query="team:marketing" - - Use when: "Search for John's account" -> params with query="john" - - Don't use when: You need to create a user (use example_create_user instead) - - Don't use when: You have a user ID and need full details (use example_get_user instead) - - Error Handling: - - Input validation errors are handled by Pydantic model - - Returns "Error: Rate limit exceeded" if too many requests (429 status) - - Returns "Error: Invalid API authentication" if API key is invalid (401 status) - - Returns formatted list of results or "No users found matching 'query'" - ''' -``` - -## Complete Example - -See below for a complete Python MCP server example: - -```python -#!/usr/bin/env python3 -''' -MCP Server for Example Service. - -This server provides tools to interact with Example API, including user search, -project management, and data export capabilities. -''' - -from typing import Optional, List, Dict, Any -from enum import Enum -import httpx -from pydantic import BaseModel, Field, field_validator, ConfigDict -from mcp.server.fastmcp import FastMCP - -# Initialize the MCP server -mcp = FastMCP("example_mcp") - -# Constants -API_BASE_URL = "https://api.example.com/v1" - -# Enums -class ResponseFormat(str, Enum): - '''Output format for tool responses.''' - MARKDOWN = "markdown" - JSON = "json" - -# Pydantic Models for Input Validation -class UserSearchInput(BaseModel): - '''Input model for user search operations.''' - model_config = ConfigDict( - str_strip_whitespace=True, - validate_assignment=True - ) - - query: str = Field(..., description="Search string to match against names/emails", min_length=2, max_length=200) - limit: Optional[int] = Field(default=20, description="Maximum results to return", ge=1, le=100) - offset: Optional[int] = Field(default=0, description="Number of results to skip for pagination", ge=0) - response_format: ResponseFormat = Field(default=ResponseFormat.MARKDOWN, description="Output format") - - @field_validator('query') - @classmethod - def validate_query(cls, v: str) -> str: - if not v.strip(): - raise ValueError("Query cannot be empty or whitespace only") - return v.strip() - -# Shared utility functions -async def _make_api_request(endpoint: str, method: str = "GET", **kwargs) -> dict: - '''Reusable function for all API calls.''' - async with httpx.AsyncClient() as client: - response = await client.request( - method, - f"{API_BASE_URL}/{endpoint}", - timeout=30.0, - **kwargs - ) - response.raise_for_status() - return response.json() - -def _handle_api_error(e: Exception) -> str: - '''Consistent error formatting across all tools.''' - if isinstance(e, httpx.HTTPStatusError): - if e.response.status_code == 404: - return "Error: Resource not found. Please check the ID is correct." - elif e.response.status_code == 403: - return "Error: Permission denied. You don't have access to this resource." - elif e.response.status_code == 429: - return "Error: Rate limit exceeded. Please wait before making more requests." - return f"Error: API request failed with status {e.response.status_code}" - elif isinstance(e, httpx.TimeoutException): - return "Error: Request timed out. Please try again." - return f"Error: Unexpected error occurred: {type(e).__name__}" - -# Tool definitions -@mcp.tool( - name="example_search_users", - annotations={ - "title": "Search Example Users", - "readOnlyHint": True, - "destructiveHint": False, - "idempotentHint": True, - "openWorldHint": True - } -) -async def example_search_users(params: UserSearchInput) -> str: - '''Search for users in the Example system by name, email, or team. - - [Full docstring as shown above] - ''' - try: - # Make API request using validated parameters - data = await _make_api_request( - "users/search", - params={ - "q": params.query, - "limit": params.limit, - "offset": params.offset - } - ) - - users = data.get("users", []) - total = data.get("total", 0) - - if not users: - return f"No users found matching '{params.query}'" - - # Format response based on requested format - if params.response_format == ResponseFormat.MARKDOWN: - lines = [f"# User Search Results: '{params.query}'", ""] - lines.append(f"Found {total} users (showing {len(users)})") - lines.append("") - - for user in users: - lines.append(f"## {user['name']} ({user['id']})") - lines.append(f"- **Email**: {user['email']}") - if user.get('team'): - lines.append(f"- **Team**: {user['team']}") - lines.append("") - - return "\n".join(lines) - - else: - # Machine-readable JSON format - import json - response = { - "total": total, - "count": len(users), - "offset": params.offset, - "users": users - } - return json.dumps(response, indent=2) - - except Exception as e: - return _handle_api_error(e) - -if __name__ == "__main__": - mcp.run() -``` - ---- - -## Advanced FastMCP Features - -### Context Parameter Injection - -FastMCP can automatically inject a `Context` parameter into tools for advanced capabilities like logging, progress reporting, resource reading, and user interaction: - -```python -from mcp.server.fastmcp import FastMCP, Context - -mcp = FastMCP("example_mcp") - -@mcp.tool() -async def advanced_search(query: str, ctx: Context) -> str: - '''Advanced tool with context access for logging and progress.''' - - # Report progress for long operations - await ctx.report_progress(0.25, "Starting search...") - - # Log information for debugging - await ctx.log_info("Processing query", {"query": query, "timestamp": datetime.now()}) - - # Perform search - results = await search_api(query) - await ctx.report_progress(0.75, "Formatting results...") - - # Access server configuration - server_name = ctx.fastmcp.name - - return format_results(results) - -@mcp.tool() -async def interactive_tool(resource_id: str, ctx: Context) -> str: - '''Tool that can request additional input from users.''' - - # Request sensitive information when needed - api_key = await ctx.elicit( - prompt="Please provide your API key:", - input_type="password" - ) - - # Use the provided key - return await api_call(resource_id, api_key) -``` - -**Context capabilities:** -- `ctx.report_progress(progress, message)` - Report progress for long operations -- `ctx.log_info(message, data)` / `ctx.log_error()` / `ctx.log_debug()` - Logging -- `ctx.elicit(prompt, input_type)` - Request input from users -- `ctx.fastmcp.name` - Access server configuration -- `ctx.read_resource(uri)` - Read MCP resources - -### Resource Registration - -Expose data as resources for efficient, template-based access: - -```python -@mcp.resource("file://documents/{name}") -async def get_document(name: str) -> str: - '''Expose documents as MCP resources. - - Resources are useful for static or semi-static data that doesn't - require complex parameters. They use URI templates for flexible access. - ''' - document_path = f"./docs/{name}" - with open(document_path, "r") as f: - return f.read() - -@mcp.resource("config://settings/{key}") -async def get_setting(key: str, ctx: Context) -> str: - '''Expose configuration as resources with context.''' - settings = await load_settings() - return json.dumps(settings.get(key, {})) -``` - -**When to use Resources vs Tools:** -- **Resources**: For data access with simple parameters (URI templates) -- **Tools**: For complex operations with validation and business logic - -### Structured Output Types - -FastMCP supports multiple return types beyond strings: - -```python -from typing import TypedDict -from dataclasses import dataclass -from pydantic import BaseModel - -# TypedDict for structured returns -class UserData(TypedDict): - id: str - name: str - email: str - -@mcp.tool() -async def get_user_typed(user_id: str) -> UserData: - '''Returns structured data - FastMCP handles serialization.''' - return {"id": user_id, "name": "John Doe", "email": "john@example.com"} - -# Pydantic models for complex validation -class DetailedUser(BaseModel): - id: str - name: str - email: str - created_at: datetime - metadata: Dict[str, Any] - -@mcp.tool() -async def get_user_detailed(user_id: str) -> DetailedUser: - '''Returns Pydantic model - automatically generates schema.''' - user = await fetch_user(user_id) - return DetailedUser(**user) -``` - -### Lifespan Management - -Initialize resources that persist across requests: - -```python -from contextlib import asynccontextmanager - -@asynccontextmanager -async def app_lifespan(): - '''Manage resources that live for the server's lifetime.''' - # Initialize connections, load config, etc. - db = await connect_to_database() - config = load_configuration() - - # Make available to all tools - yield {"db": db, "config": config} - - # Cleanup on shutdown - await db.close() - -mcp = FastMCP("example_mcp", lifespan=app_lifespan) - -@mcp.tool() -async def query_data(query: str, ctx: Context) -> str: - '''Access lifespan resources through context.''' - db = ctx.request_context.lifespan_state["db"] - results = await db.query(query) - return format_results(results) -``` - -### Transport Options - -FastMCP supports two main transport mechanisms: - -```python -# stdio transport (for local tools) - default -if __name__ == "__main__": - mcp.run() - -# Streamable HTTP transport (for remote servers) -if __name__ == "__main__": - mcp.run(transport="streamable_http", port=8000) -``` - -**Transport selection:** -- **stdio**: Command-line tools, local integrations, subprocess execution -- **Streamable HTTP**: Web services, remote access, multiple clients - ---- - -## Code Best Practices - -### Code Composability and Reusability - -Your implementation MUST prioritize composability and code reuse: - -1. **Extract Common Functionality**: - - Create reusable helper functions for operations used across multiple tools - - Build shared API clients for HTTP requests instead of duplicating code - - Centralize error handling logic in utility functions - - Extract business logic into dedicated functions that can be composed - - Extract shared markdown or JSON field selection & formatting functionality - -2. **Avoid Duplication**: - - NEVER copy-paste similar code between tools - - If you find yourself writing similar logic twice, extract it into a function - - Common operations like pagination, filtering, field selection, and formatting should be shared - - Authentication/authorization logic should be centralized - -### Python-Specific Best Practices - -1. **Use Type Hints**: Always include type annotations for function parameters and return values -2. **Pydantic Models**: Define clear Pydantic models for all input validation -3. **Avoid Manual Validation**: Let Pydantic handle input validation with constraints -4. **Proper Imports**: Group imports (standard library, third-party, local) -5. **Error Handling**: Use specific exception types (httpx.HTTPStatusError, not generic Exception) -6. **Async Context Managers**: Use `async with` for resources that need cleanup -7. **Constants**: Define module-level constants in UPPER_CASE - -## Quality Checklist - -Before finalizing your Python MCP server implementation, ensure: - -### Strategic Design -- [ ] Tools enable complete workflows, not just API endpoint wrappers -- [ ] Tool names reflect natural task subdivisions -- [ ] Response formats optimize for agent context efficiency -- [ ] Human-readable identifiers used where appropriate -- [ ] Error messages guide agents toward correct usage - -### Implementation Quality -- [ ] FOCUSED IMPLEMENTATION: Most important and valuable tools implemented -- [ ] All tools have descriptive names and documentation -- [ ] Return types are consistent across similar operations -- [ ] Error handling is implemented for all external calls -- [ ] Server name follows format: `{service}_mcp` -- [ ] All network operations use async/await -- [ ] Common functionality is extracted into reusable functions -- [ ] Error messages are clear, actionable, and educational -- [ ] Outputs are properly validated and formatted - -### Tool Configuration -- [ ] All tools implement 'name' and 'annotations' in the decorator -- [ ] Annotations correctly set (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) -- [ ] All tools use Pydantic BaseModel for input validation with Field() definitions -- [ ] All Pydantic Fields have explicit types and descriptions with constraints -- [ ] All tools have comprehensive docstrings with explicit input/output types -- [ ] Docstrings include complete schema structure for dict/JSON returns -- [ ] Pydantic models handle input validation (no manual validation needed) - -### Advanced Features (where applicable) -- [ ] Context injection used for logging, progress, or elicitation -- [ ] Resources registered for appropriate data endpoints -- [ ] Lifespan management implemented for persistent connections -- [ ] Structured output types used (TypedDict, Pydantic models) -- [ ] Appropriate transport configured (stdio or streamable HTTP) - -### Code Quality -- [ ] File includes proper imports including Pydantic imports -- [ ] Pagination is properly implemented where applicable -- [ ] Filtering options are provided for potentially large result sets -- [ ] All async functions are properly defined with `async def` -- [ ] HTTP client usage follows async patterns with proper context managers -- [ ] Type hints are used throughout the code -- [ ] Constants are defined at module level in UPPER_CASE - -### Testing -- [ ] Server runs successfully: `python your_server.py --help` -- [ ] All imports resolve correctly -- [ ] Sample tool calls work as expected -- [ ] Error scenarios handled gracefully \ No newline at end of file diff --git a/plugins/ndf-shared/skills/mcp-builder/scripts/connections.py b/plugins/ndf-shared/skills/mcp-builder/scripts/connections.py deleted file mode 100644 index ffcd0da3..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/scripts/connections.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Lightweight connection handling for MCP servers.""" - -from abc import ABC, abstractmethod -from contextlib import AsyncExitStack -from typing import Any - -from mcp import ClientSession, StdioServerParameters -from mcp.client.sse import sse_client -from mcp.client.stdio import stdio_client -from mcp.client.streamable_http import streamablehttp_client - - -class MCPConnection(ABC): - """Base class for MCP server connections.""" - - def __init__(self): - self.session = None - self._stack = None - - @abstractmethod - def _create_context(self): - """Create the connection context based on connection type.""" - - async def __aenter__(self): - """Initialize MCP server connection.""" - self._stack = AsyncExitStack() - await self._stack.__aenter__() - - try: - ctx = self._create_context() - result = await self._stack.enter_async_context(ctx) - - if len(result) == 2: - read, write = result - elif len(result) == 3: - read, write, _ = result - else: - raise ValueError(f"Unexpected context result: {result}") - - session_ctx = ClientSession(read, write) - self.session = await self._stack.enter_async_context(session_ctx) - await self.session.initialize() - return self - except BaseException: - await self._stack.__aexit__(None, None, None) - raise - - async def __aexit__(self, exc_type, exc_val, exc_tb): - """Clean up MCP server connection resources.""" - if self._stack: - await self._stack.__aexit__(exc_type, exc_val, exc_tb) - self.session = None - self._stack = None - - async def list_tools(self) -> list[dict[str, Any]]: - """Retrieve available tools from the MCP server.""" - response = await self.session.list_tools() - return [ - { - "name": tool.name, - "description": tool.description, - "input_schema": tool.inputSchema, - } - for tool in response.tools - ] - - async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> Any: - """Call a tool on the MCP server with provided arguments.""" - result = await self.session.call_tool(tool_name, arguments=arguments) - return result.content - - -class MCPConnectionStdio(MCPConnection): - """MCP connection using standard input/output.""" - - def __init__(self, command: str, args: list[str] = None, env: dict[str, str] = None): - super().__init__() - self.command = command - self.args = args or [] - self.env = env - - def _create_context(self): - return stdio_client( - StdioServerParameters(command=self.command, args=self.args, env=self.env) - ) - - -class MCPConnectionSSE(MCPConnection): - """MCP connection using Server-Sent Events.""" - - def __init__(self, url: str, headers: dict[str, str] = None): - super().__init__() - self.url = url - self.headers = headers or {} - - def _create_context(self): - return sse_client(url=self.url, headers=self.headers) - - -class MCPConnectionHTTP(MCPConnection): - """MCP connection using Streamable HTTP.""" - - def __init__(self, url: str, headers: dict[str, str] = None): - super().__init__() - self.url = url - self.headers = headers or {} - - def _create_context(self): - return streamablehttp_client(url=self.url, headers=self.headers) - - -def create_connection( - transport: str, - command: str = None, - args: list[str] = None, - env: dict[str, str] = None, - url: str = None, - headers: dict[str, str] = None, -) -> MCPConnection: - """Factory function to create the appropriate MCP connection. - - Args: - transport: Connection type ("stdio", "sse", or "http") - command: Command to run (stdio only) - args: Command arguments (stdio only) - env: Environment variables (stdio only) - url: Server URL (sse and http only) - headers: HTTP headers (sse and http only) - - Returns: - MCPConnection instance - """ - transport = transport.lower() - - if transport == "stdio": - if not command: - raise ValueError("Command is required for stdio transport") - return MCPConnectionStdio(command=command, args=args, env=env) - - elif transport == "sse": - if not url: - raise ValueError("URL is required for sse transport") - return MCPConnectionSSE(url=url, headers=headers) - - elif transport in ["http", "streamable_http", "streamable-http"]: - if not url: - raise ValueError("URL is required for http transport") - return MCPConnectionHTTP(url=url, headers=headers) - - else: - raise ValueError(f"Unsupported transport type: {transport}. Use 'stdio', 'sse', or 'http'") diff --git a/plugins/ndf-shared/skills/mcp-builder/scripts/evaluation.py b/plugins/ndf-shared/skills/mcp-builder/scripts/evaluation.py deleted file mode 100644 index 41778569..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/scripts/evaluation.py +++ /dev/null @@ -1,373 +0,0 @@ -"""MCP Server Evaluation Harness - -This script evaluates MCP servers by running test questions against them using Claude. -""" - -import argparse -import asyncio -import json -import re -import sys -import time -import traceback -import xml.etree.ElementTree as ET -from pathlib import Path -from typing import Any - -from anthropic import Anthropic - -from connections import create_connection - -EVALUATION_PROMPT = """You are an AI assistant with access to tools. - -When given a task, you MUST: -1. Use the available tools to complete the task -2. Provide summary of each step in your approach, wrapped in tags -3. Provide feedback on the tools provided, wrapped in tags -4. Provide your final response, wrapped in tags - -Summary Requirements: -- In your tags, you must explain: - - The steps you took to complete the task - - Which tools you used, in what order, and why - - The inputs you provided to each tool - - The outputs you received from each tool - - A summary for how you arrived at the response - -Feedback Requirements: -- In your tags, provide constructive feedback on the tools: - - Comment on tool names: Are they clear and descriptive? - - Comment on input parameters: Are they well-documented? Are required vs optional parameters clear? - - Comment on descriptions: Do they accurately describe what the tool does? - - Comment on any errors encountered during tool usage: Did the tool fail to execute? Did the tool return too many tokens? - - Identify specific areas for improvement and explain WHY they would help - - Be specific and actionable in your suggestions - -Response Requirements: -- Your response should be concise and directly address what was asked -- Always wrap your final response in tags -- If you cannot solve the task return NOT_FOUND -- For numeric responses, provide just the number -- For IDs, provide just the ID -- For names or text, provide the exact text requested -- Your response should go last""" - - -def parse_evaluation_file(file_path: Path) -> list[dict[str, Any]]: - """Parse XML evaluation file with qa_pair elements.""" - try: - tree = ET.parse(file_path) - root = tree.getroot() - evaluations = [] - - for qa_pair in root.findall(".//qa_pair"): - question_elem = qa_pair.find("question") - answer_elem = qa_pair.find("answer") - - if question_elem is not None and answer_elem is not None: - evaluations.append({ - "question": (question_elem.text or "").strip(), - "answer": (answer_elem.text or "").strip(), - }) - - return evaluations - except Exception as e: - print(f"Error parsing evaluation file {file_path}: {e}") - return [] - - -def extract_xml_content(text: str, tag: str) -> str | None: - """Extract content from XML tags.""" - pattern = rf"<{tag}>(.*?)" - matches = re.findall(pattern, text, re.DOTALL) - return matches[-1].strip() if matches else None - - -async def agent_loop( - client: Anthropic, - model: str, - question: str, - tools: list[dict[str, Any]], - connection: Any, -) -> tuple[str, dict[str, Any]]: - """Run the agent loop with MCP tools.""" - messages = [{"role": "user", "content": question}] - - response = await asyncio.to_thread( - client.messages.create, - model=model, - max_tokens=4096, - system=EVALUATION_PROMPT, - messages=messages, - tools=tools, - ) - - messages.append({"role": "assistant", "content": response.content}) - - tool_metrics = {} - - while response.stop_reason == "tool_use": - tool_use = next(block for block in response.content if block.type == "tool_use") - tool_name = tool_use.name - tool_input = tool_use.input - - tool_start_ts = time.time() - try: - tool_result = await connection.call_tool(tool_name, tool_input) - tool_response = json.dumps(tool_result) if isinstance(tool_result, (dict, list)) else str(tool_result) - except Exception as e: - tool_response = f"Error executing tool {tool_name}: {str(e)}\n" - tool_response += traceback.format_exc() - tool_duration = time.time() - tool_start_ts - - if tool_name not in tool_metrics: - tool_metrics[tool_name] = {"count": 0, "durations": []} - tool_metrics[tool_name]["count"] += 1 - tool_metrics[tool_name]["durations"].append(tool_duration) - - messages.append({ - "role": "user", - "content": [{ - "type": "tool_result", - "tool_use_id": tool_use.id, - "content": tool_response, - }] - }) - - response = await asyncio.to_thread( - client.messages.create, - model=model, - max_tokens=4096, - system=EVALUATION_PROMPT, - messages=messages, - tools=tools, - ) - messages.append({"role": "assistant", "content": response.content}) - - response_text = next( - (block.text for block in response.content if hasattr(block, "text")), - None, - ) - return response_text, tool_metrics - - -async def evaluate_single_task( - client: Anthropic, - model: str, - qa_pair: dict[str, Any], - tools: list[dict[str, Any]], - connection: Any, - task_index: int, -) -> dict[str, Any]: - """Evaluate a single QA pair with the given tools.""" - start_time = time.time() - - print(f"Task {task_index + 1}: Running task with question: {qa_pair['question']}") - response, tool_metrics = await agent_loop(client, model, qa_pair["question"], tools, connection) - - response_value = extract_xml_content(response, "response") - summary = extract_xml_content(response, "summary") - feedback = extract_xml_content(response, "feedback") - - duration_seconds = time.time() - start_time - - return { - "question": qa_pair["question"], - "expected": qa_pair["answer"], - "actual": response_value, - "score": int(response_value == qa_pair["answer"]) if response_value else 0, - "total_duration": duration_seconds, - "tool_calls": tool_metrics, - "num_tool_calls": sum(len(metrics["durations"]) for metrics in tool_metrics.values()), - "summary": summary, - "feedback": feedback, - } - - -REPORT_HEADER = """ -# Evaluation Report - -## Summary - -- **Accuracy**: {correct}/{total} ({accuracy:.1f}%) -- **Average Task Duration**: {average_duration_s:.2f}s -- **Average Tool Calls per Task**: {average_tool_calls:.2f} -- **Total Tool Calls**: {total_tool_calls} - ---- -""" - -TASK_TEMPLATE = """ -### Task {task_num} - -**Question**: {question} -**Ground Truth Answer**: `{expected_answer}` -**Actual Answer**: `{actual_answer}` -**Correct**: {correct_indicator} -**Duration**: {total_duration:.2f}s -**Tool Calls**: {tool_calls} - -**Summary** -{summary} - -**Feedback** -{feedback} - ---- -""" - - -async def run_evaluation( - eval_path: Path, - connection: Any, - model: str = "claude-3-7-sonnet-20250219", -) -> str: - """Run evaluation with MCP server tools.""" - print("🚀 Starting Evaluation") - - client = Anthropic() - - tools = await connection.list_tools() - print(f"📋 Loaded {len(tools)} tools from MCP server") - - qa_pairs = parse_evaluation_file(eval_path) - print(f"📋 Loaded {len(qa_pairs)} evaluation tasks") - - results = [] - for i, qa_pair in enumerate(qa_pairs): - print(f"Processing task {i + 1}/{len(qa_pairs)}") - result = await evaluate_single_task(client, model, qa_pair, tools, connection, i) - results.append(result) - - correct = sum(r["score"] for r in results) - accuracy = (correct / len(results)) * 100 if results else 0 - average_duration_s = sum(r["total_duration"] for r in results) / len(results) if results else 0 - average_tool_calls = sum(r["num_tool_calls"] for r in results) / len(results) if results else 0 - total_tool_calls = sum(r["num_tool_calls"] for r in results) - - report = REPORT_HEADER.format( - correct=correct, - total=len(results), - accuracy=accuracy, - average_duration_s=average_duration_s, - average_tool_calls=average_tool_calls, - total_tool_calls=total_tool_calls, - ) - - report += "".join([ - TASK_TEMPLATE.format( - task_num=i + 1, - question=qa_pair["question"], - expected_answer=qa_pair["answer"], - actual_answer=result["actual"] or "N/A", - correct_indicator="✅" if result["score"] else "❌", - total_duration=result["total_duration"], - tool_calls=json.dumps(result["tool_calls"], indent=2), - summary=result["summary"] or "N/A", - feedback=result["feedback"] or "N/A", - ) - for i, (qa_pair, result) in enumerate(zip(qa_pairs, results)) - ]) - - return report - - -def parse_headers(header_list: list[str]) -> dict[str, str]: - """Parse header strings in format 'Key: Value' into a dictionary.""" - headers = {} - if not header_list: - return headers - - for header in header_list: - if ":" in header: - key, value = header.split(":", 1) - headers[key.strip()] = value.strip() - else: - print(f"Warning: Ignoring malformed header: {header}") - return headers - - -def parse_env_vars(env_list: list[str]) -> dict[str, str]: - """Parse environment variable strings in format 'KEY=VALUE' into a dictionary.""" - env = {} - if not env_list: - return env - - for env_var in env_list: - if "=" in env_var: - key, value = env_var.split("=", 1) - env[key.strip()] = value.strip() - else: - print(f"Warning: Ignoring malformed environment variable: {env_var}") - return env - - -async def main(): - parser = argparse.ArgumentParser( - description="Evaluate MCP servers using test questions", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Evaluate a local stdio MCP server - python evaluation.py -t stdio -c python -a my_server.py eval.xml - - # Evaluate an SSE MCP server - python evaluation.py -t sse -u https://example.com/mcp -H "Authorization: Bearer token" eval.xml - - # Evaluate an HTTP MCP server with custom model - python evaluation.py -t http -u https://example.com/mcp -m claude-3-5-sonnet-20241022 eval.xml - """, - ) - - parser.add_argument("eval_file", type=Path, help="Path to evaluation XML file") - parser.add_argument("-t", "--transport", choices=["stdio", "sse", "http"], default="stdio", help="Transport type (default: stdio)") - parser.add_argument("-m", "--model", default="claude-3-7-sonnet-20250219", help="Claude model to use (default: claude-3-7-sonnet-20250219)") - - stdio_group = parser.add_argument_group("stdio options") - stdio_group.add_argument("-c", "--command", help="Command to run MCP server (stdio only)") - stdio_group.add_argument("-a", "--args", nargs="+", help="Arguments for the command (stdio only)") - stdio_group.add_argument("-e", "--env", nargs="+", help="Environment variables in KEY=VALUE format (stdio only)") - - remote_group = parser.add_argument_group("sse/http options") - remote_group.add_argument("-u", "--url", help="MCP server URL (sse/http only)") - remote_group.add_argument("-H", "--header", nargs="+", dest="headers", help="HTTP headers in 'Key: Value' format (sse/http only)") - - parser.add_argument("-o", "--output", type=Path, help="Output file for evaluation report (default: stdout)") - - args = parser.parse_args() - - if not args.eval_file.exists(): - print(f"Error: Evaluation file not found: {args.eval_file}") - sys.exit(1) - - headers = parse_headers(args.headers) if args.headers else None - env_vars = parse_env_vars(args.env) if args.env else None - - try: - connection = create_connection( - transport=args.transport, - command=args.command, - args=args.args, - env=env_vars, - url=args.url, - headers=headers, - ) - except ValueError as e: - print(f"Error: {e}") - sys.exit(1) - - print(f"🔗 Connecting to MCP server via {args.transport}...") - - async with connection: - print("✅ Connected successfully") - report = await run_evaluation(args.eval_file, connection, args.model) - - if args.output: - args.output.write_text(report) - print(f"\n✅ Report saved to {args.output}") - else: - print("\n" + report) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/plugins/ndf-shared/skills/mcp-builder/scripts/example_evaluation.xml b/plugins/ndf-shared/skills/mcp-builder/scripts/example_evaluation.xml deleted file mode 100644 index 41e4459b..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/scripts/example_evaluation.xml +++ /dev/null @@ -1,22 +0,0 @@ - - - Calculate the compound interest on $10,000 invested at 5% annual interest rate, compounded monthly for 3 years. What is the final amount in dollars (rounded to 2 decimal places)? - 11614.72 - - - A projectile is launched at a 45-degree angle with an initial velocity of 50 m/s. Calculate the total distance (in meters) it has traveled from the launch point after 2 seconds, assuming g=9.8 m/s². Round to 2 decimal places. - 87.25 - - - A sphere has a volume of 500 cubic meters. Calculate its surface area in square meters. Round to 2 decimal places. - 304.65 - - - Calculate the population standard deviation of this dataset: [12, 15, 18, 22, 25, 30, 35]. Round to 2 decimal places. - 7.61 - - - Calculate the pH of a solution with a hydrogen ion concentration of 3.5 × 10^-5 M. Round to 2 decimal places. - 4.46 - - diff --git a/plugins/ndf-shared/skills/mcp-builder/scripts/requirements.txt b/plugins/ndf-shared/skills/mcp-builder/scripts/requirements.txt deleted file mode 100644 index e73e5d1e..00000000 --- a/plugins/ndf-shared/skills/mcp-builder/scripts/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -anthropic>=0.39.0 -mcp>=1.1.0 diff --git a/plugins/ndf-shared/skills/official-skills-autoloader/SKILL.md b/plugins/ndf-shared/skills/official-skills-autoloader/SKILL.md index 40a852ec..46e3dace 100644 --- a/plugins/ndf-shared/skills/official-skills-autoloader/SKILL.md +++ b/plugins/ndf-shared/skills/official-skills-autoloader/SKILL.md @@ -1,7 +1,6 @@ --- name: official-skills-autoloader -description: "Install and use official document Skills on demand." -when_to_use: "Use when user requests Word/Excel/PowerPoint/PDF creation/editing, frontend design, webapp testing, or other tasks handled by Anthropic's official skills collection. Triggers: 'Word作成', 'Excel出力', 'スライド生成', 'PDF作成', '.docx', '.pptx', '.xlsx', '.pdf', 'create docx', 'generate excel', 'make slides', 'create pdf'." +description: "Install an Anthropic official Skill on demand and run it. Use when the request needs Word/Excel/PowerPoint/PDF creation or editing, frontend design, webapp testing, or MCP server scaffolding（Word作成 / Excel出力 / スライド生成 / PDF作成 / .docx / .pptx / .xlsx / .pdf / MCPサーバーを作りたい）. Claude Code 専用。" allowed-tools: - Bash - Read @@ -24,6 +23,13 @@ allowed-tools: | HTML/Reactアプリ生成 / Artifacts | `web-artifacts-builder` | | 新規Skill作成 | `skill-creator` | | Claude API / SDK開発 | `claude-api` | +| MCPサーバー作成 | `mcp-builder` | + +## 対応ランタイム + +**Claude Code 専用**。インストール先の `~/.claude/skills/` を読むのは Claude Code だけで、Codex は `.agents/skills/`、Kiro CLI は `.kiro/skills/` を読む。両ランタイムでは公式 Skill の自動読込は行われないため、配布するとしても Claude Code の manifest に限る。 + +なお現在この Skill はどの manifest にも載っておらず、配布物へ含まれていない。`description` を直しても配布されるまで発動はしない。 ## 動作手順 diff --git a/plugins/ndf-shared/skills/python-execution/01-uv-setup.md b/plugins/ndf-shared/skills/python-execution/01-uv-setup.md deleted file mode 100644 index aafd4f1c..00000000 --- a/plugins/ndf-shared/skills/python-execution/01-uv-setup.md +++ /dev/null @@ -1,85 +0,0 @@ -# uv詳細セットアップガイド - -> **Note**: 基本的な`uv sync`と`uv run python`はSKILL.mdを参照。このファイルは初回セットアップや詳細設定が必要な場合のみ参照。 - -## uvインストール - -```bash -# Linux/macOS -curl -LsSf https://astral.sh/uv/install.sh | sh - -# pip経由（代替） -pip install uv - -# 確認 -uv --version -``` - -## 依存関係管理 - -```bash -# uv.lockがある場合（推奨） -uv sync - -# uv.lockがない場合 -uv lock && uv sync - -# 開発用依存関係も含める -uv sync --dev - -# 特定のextraを含める -uv sync --extra test -``` - -## Pythonバージョン管理 - -```bash -# 特定バージョンをインストール -uv python install 3.12 -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.12 - -# インストール済みバージョン一覧 -uv python list -``` - -## 実行オプション - -```bash -# スクリプト実行 -uv run python script.py - -# モジュール実行 -uv run python -m pytest -uv run python -m mypy . - -# 引数付き -uv run python script.py --arg value - -# インタラクティブシェル -uv run python -``` - -## プロジェクト初期化（新規作成時） - -```bash -# 新規プロジェクト作成 -uv init my-project -cd my-project - -# 依存関係追加 -uv add requests -uv add --dev pytest - -# ロックファイル生成 -uv lock -``` - -## uv環境の利点 - -- **高速**: Rustで実装、pip比10-100倍速 -- **再現性**: uv.lockで完全な依存関係固定 -- **Pythonバージョン管理**: pyenvなしでバージョン切り替え -- **グローバル環境を汚染しない**: プロジェクト単位で隔離 diff --git a/plugins/ndf-shared/skills/python-execution/02-troubleshooting.md b/plugins/ndf-shared/skills/python-execution/02-troubleshooting.md deleted file mode 100644 index 3475b7ec..00000000 --- a/plugins/ndf-shared/skills/python-execution/02-troubleshooting.md +++ /dev/null @@ -1,112 +0,0 @@ -# Python実行 トラブルシューティング - -## よくある問題と解決策 - -### Q: `uv: command not found` - -**原因**: uvがインストールされていない - -**解決策**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -# シェルを再起動するか、パスを通す -source ~/.bashrc # または ~/.zshrc -``` - -### Q: `ModuleNotFoundError` - -**原因**: 依存関係がインストールされていない - -**解決策**: -```bash -# uv環境の場合 -uv sync - -# venv環境の場合 -.venv/bin/pip install -r requirements.txt - -# pyproject.tomlがある場合 -.venv/bin/pip install -e . -``` - -### Q: `python: command not found` - -**原因**: Pythonがインストールされていない、またはパスが通っていない - -**解決策**: -```bash -# python3を試す -python3 --version - -# uvでPythonをインストール -uv python install 3.12 -``` - -### Q: 異なるPythonバージョンが必要 - -**解決策（uv環境）**: -```bash -# 特定バージョンをインストール -uv python install 3.11 - -# プロジェクトで使用するバージョンを固定 -uv python pin 3.11 - -# そのバージョンで実行 -uv run python script.py -``` - -### Q: `pyproject.toml`はあるが`uv.lock`がない - -**解決策**: -```bash -# ロックファイルを生成 -uv lock - -# 依存関係をインストール -uv sync -``` - -### Q: 仮想環境が壊れている - -**解決策**: -```bash -# 仮想環境を削除して再作成 -rm -rf .venv - -# uv環境の場合 -uv sync - -# 手動で作成する場合 -python3 -m venv .venv -.venv/bin/pip install -r requirements.txt -``` - -### Q: パーミッションエラー - -**解決策**: -```bash -# 仮想環境を使用（推奨） -uv sync -uv run python script.py - -# どうしてもグローバルにインストールする場合（非推奨） -pip install --user package_name -``` - -### Q: SSL証明書エラー - -**解決策**: -```bash -# macOSの場合 -/Applications/Python\ 3.x/Install\ Certificates.command - -# または環境変数で一時的に無効化（非推奨） -export PYTHONHTTPSVERIFY=0 -``` - -## 関連リソース - -- [uv公式ドキュメント](https://docs.astral.sh/uv/) -- [Python venv](https://docs.python.org/3/library/venv.html) -- [pyproject.toml仕様](https://packaging.python.org/en/latest/specifications/pyproject-toml/) diff --git a/plugins/ndf-shared/skills/python-execution/SKILL.md b/plugins/ndf-shared/skills/python-execution/SKILL.md deleted file mode 100644 index a07705aa..00000000 --- a/plugins/ndf-shared/skills/python-execution/SKILL.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -name: python-execution -description: "Detect and run the right Python environment." -when_to_use: "Python スクリプトを実行 / セットアップするとき。Triggers: 'python', 'uv', 'スクリプト', 'python環境'" -allowed-tools: - - Read - - Bash - - Glob ---- - -# Python Execution Skill - -## 概要 - -Pythonコードを実行する前に、プロジェクトの実行環境を調査し、適切な方法で実行するためのガイドラインです。 - -## Step 1: 環境検出 - -```bash -ls -la pyproject.toml uv.lock .venv/ venv/ requirements.txt 2>/dev/null -``` - -## Step 2: 実行コマンド選択 - -| 検出ファイル | 実行方法 | 優先度 | -|-------------|---------|-------| -| `pyproject.toml` | `uv run python` | 最高 | -| `.venv/` | `.venv/bin/python` | 中 | -| `venv/` | `venv/bin/python` | 中 | -| 何もなし | `python3` | 最低 | - -## Step 3: 実行 - -### uv環境（pyproject.tomlあり） - -```bash -# 依存関係インストール（初回のみ） -uv sync - -# 実行 -uv run python script.py -uv run python -m module_name -``` - -**uvがない場合のインストール**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -source ~/.bashrc # パスを反映 -``` - -### venv環境（.venv/あり） - -```bash -# 依存関係インストール（初回のみ） -.venv/bin/pip install -r requirements.txt - -# 実行 -.venv/bin/python script.py -``` - -### システムPython - -```bash -python3 script.py -``` - -## ベストプラクティス - -| DO | DON'T | -|----|-------| -| 実行前に環境を調査 | 環境を確認せずに実行 | -| README.md/CLAUDE.mdの指示を優先 | グローバル環境に依存関係をインストール | -| pyproject.tomlがあればuv使用 | source activateに依存 | -| 仮想環境のPythonをパス指定で実行 | python2を使用 | - -## 詳細ガイド（必要時のみ参照） - -| ファイル | 内容 | 参照タイミング | -|---------|------|--------------| -| `01-uv-setup.md` | uv詳細セットアップ、Pythonバージョン管理 | 初回セットアップ時 | -| `02-troubleshooting.md` | エラー解決策 | 問題発生時 | - -## 関連Skill - -- **corder-code-templates**: Pythonコードテンプレート -- **corder-test-generation**: Pythonテスト生成 diff --git a/plugins/ndf-shared/skills/qa-security-scan/SKILL.md b/plugins/ndf-shared/skills/qa-security-scan/SKILL.md index f8e86e93..0adfc80f 100644 --- a/plugins/ndf-shared/skills/qa-security-scan/SKILL.md +++ b/plugins/ndf-shared/skills/qa-security-scan/SKILL.md @@ -1,7 +1,6 @@ --- name: qa-security-scan -description: "Run OWASP-focused security checks." -when_to_use: "Use when conducting security testing or vulnerability assessment. Triggers: 'security scan', 'vulnerability check', 'OWASP', 'security test', 'セキュリティスキャン', '脆弱性チェック', 'セキュリティテスト'" +description: "Run an OWASP Top 10 security review of code, authentication, authorization, and data protection. Use when asked for a security scan, vulnerability assessment, or a security review of a change（セキュリティスキャン / 脆弱性チェック / セキュリティレビュー / OWASP / 認証認可の確認 / SQLインジェクションの確認）." --- # QA Security Scan Skill diff --git a/plugins/ndf-shared/skills/skill-stats/SKILL.md b/plugins/ndf-shared/skills/skill-stats/SKILL.md index d500b0a6..93a95cb2 100644 --- a/plugins/ndf-shared/skills/skill-stats/SKILL.md +++ b/plugins/ndf-shared/skills/skill-stats/SKILL.md @@ -100,4 +100,3 @@ Triggers キーワードは `Triggers:` と `明示トリガ:` のどちらの ## 関連スキル - `/ndf:markdown-writing` — 結果を読みやすく整形するためのガイドライン -- `/ndf:python-execution` — Python実行環境の判定