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GAMI#readme","keywords":["github-copilot","agent-skills","deep-research","consulting","frameworks","ai-assistant","hallucination-prevention","swot","pest","mece","business-analysis","mcp","duckduckgo","brave-search","searxng","pdf-parsing","web-search"],"repository":{"type":"git","url":"git+https://github.com/nahisaho/SHIKIGAMI.git"},"description":"GitHub Copilot Agent Skills for Deep Research & Consulting - AI-Powered Research Assistant with 50+ Consulting Frameworks","maintainers":[{"name":"nahisaho","email":"nahisaho@microsoft.com"}],"readme":"# SHIKIGAMI\n\n**GitHub Copilot Agent Skills for Deep Research & McKinsey Consulting**\n\nマッキンゼーのコンサルティング手法（仮説駆動、MECE、ピラミッド原則、7Sフレームワーク等）を活用した深層リサーチ自動化Agent Skillsパッケージです。GitHub Copilot CLI と連携し、Copilot CLI の組み込み機能を活用しつつ、SHIKIGAMI独自のリサーチ・コンサルティング機能を提供します。\n\n## 🎯 できること\n\n- **対話的目的探索**: ユーザーの表層的な依頼から「真の目的」を探索\n- **Deep Research**: WebResearcherパラダイムに基づく反復的深層リサーチ\n- **マッキンゼー・フレームワーク分析**: 仮説駆動、イシューツリー、7S、ピラミッド原則等を適用\n- **高品質レポート生成**: ハルシネーション防止、ソース追跡、引用管理\n- **マルチポップ探索**: リサーチ中の分岐点を自動検出し、複数パスを同時探索\n- **ベクトル検索**: 過去のリサーチ資産をセマンティック検索で再利用\n- **品質検証ループ**: Build→Type→Lint→Test→Security→Diff の6フェーズ検証\n\n## 📦 インストール\n\n```bash\nnpm install @nahisaho/shikigami-mck\n```\n\nまたは、リポジトリをクローン:\n\n```bash\ngit clone https://github.com/nahisaho/SHIKIGAMI.git\n```\n\n## 🏗️ アーキテクチャ\n\nSHIKIGAMI v2.0.0 は GitHub Copilot CLI の組み込み機能を最大限活用する設計に移行しました。\n\n```mermaid\nflowchart TB\n    CLI[\"GitHub Copilot CLI<br/>web_search / web_fetch / store_memory / grep<br/>explore / research / rubber-duck agents<br/>/session / /context / /compact\"]\n    SHIKI[\"SHIKIGAMI v2.0.0\"]\n    SKILLS[\"Agent Skills (15 skills)<br/>Research<br/>Consulting<br/>Quality<br/>MUSUBIX SDD\"]\n    MCP[\"MCP Server (10 tools)<br/>embed / similarity<br/>semantic_search<br/>branch_detect / eval / select<br/>set_project / get_project<br/>save_prompt / save_research\"]\n\n    CLI --> SHIKI\n    SHIKI --> SKILLS\n    SHIKI --> MCP\n```\n\n## 🔧 Agent Skills（15スキル）\n\n### リサーチ・コンサルティング\n\n| スキル | 説明 |\n|--------|------|\n| `shikigami-planner` | 対話的目的探索・5 Whys・JTBD・リサーチ計画立案 |\n| `shikigami-deep-research` | Think→Report→Actionサイクルの反復的深層リサーチ |\n| `shikigami-consulting-framework` | マッキンゼー・フレームワーク分析（仮説駆動・7S・ピラミッド原則等） |\n| `shikigami-writing` | レポート生成・ハルシネーション検出・引用管理 |\n\n### 品質・検証\n\n| スキル | 説明 |\n|--------|------|\n| `build-fix` | ビルドエラーを分類・反復修正（TypeScript/ESLint/依存関係） |\n| `checkpoint` | セーフポイントの作成・復元・比較（Git統合） |\n| `e2e-runner` | Playwrightを使用したE2Eテスト生成・実行 |\n| `eval-harness` | pass@kメトリクスでAIコード生成品質を評価 |\n| `verification-loop` | Build→Type→Lint→Test→Security→Diff の6フェーズ検証 |\n\n### MUSUBIX SDD\n\n| スキル | 説明 |\n|--------|------|\n| `musubix-sdd-workflow` | 10憲法条項に従った仕様駆動開発ガイド |\n| `musubix-domain-inference` | 自動ドメイン検出・コンポーネント推論 |\n| `musubix-code-generation` | 設計仕様からのコード生成 |\n| `musubix-test-generation` | TDD/BDDによるテスト生成 |\n| `musubix-adr-generation` | Architecture Decision Records作成 |\n| `musubix-best-practices` | 学習済み17ベストプラクティスガイド |\n| `musubix-c4-design` | C4モデル設計ドキュメント作成 |\n| `musubix-technical-writing` | 技術ドキュメント作成 |\n| `musubix-ears-validation` | EARS形式要件の検証・作成 |\n| `musubix-traceability` | 要件・設計・コード・テスト間のトレーサビリティ管理 |\n\n## 🔌 MCP Server（10ツール）\n\n| ツール | 説明 |\n|--------|------|\n| `set_project` / `get_project` | リサーチプロジェクトの設定・取得 |\n| `save_prompt` | リサーチプロンプトの保存 |\n| `save_research` | リサーチ結果の保存 |\n| `embed` | テキストのベクトル埋め込み生成 |\n| `similarity` | テキスト間の類似度計算 |\n| `semantic_search` | 過去のリサーチ資産をセマンティック検索 |\n| `branch_detect` | リサーチ中の分岐シグナル検出 |\n| `branch_evaluate` | 分岐パスのスコアリング・評価 |\n| `branch_select` | 最適な探索パスの選択 |\n\n## 📝 Prompts\n\n| プロンプト | 説明 |\n|-----------|------|\n| `shikigami-purpose-discovery` | 目的探索を開始 |\n| `shikigami-deep-research` | Deep Researchを実行 |\n| `shikigami-framework-analysis` | マッキンゼーフレームワーク分析を実行 |\n| `shikigami-report-writing` | ピラミッド原則に基づくレポートを生成 |\n| `shikigami-full-research` | 統合リサーチを実行 |\n\n## 📚 マッキンゼー・フレームワーク一覧\n\n### 問題解決・仮説思考\n| フレームワーク | 説明 |\n|--------------|------|\n| **仮説駆動アプローチ** | 仮説を立て、検証し、結論を導く問題解決手法 |\n| **イシューツリー** | 問題をMECEに分解し、解決の優先順位を明確化 |\n| **ピラミッド原則** | 結論先行で論理的にコミュニケーション（Barbara Minto） |\n| **MECE** | 漏れなくダブりなく整理 |\n\n### 組織・戦略\n| フレームワーク | 説明 |\n|--------------|------|\n| **マッキンゼーの7S** | 組織の7要素（Strategy, Structure, Systems, Shared Values, Style, Staff, Skills）の整合性分析 |\n| **3つの地平線** | 短期（H1）・中期（H2）・長期（H3）の成長を同時管理 |\n| **成長のグラニュラリティ** | 成長を詳細に分解して真の成長源を特定 |\n\n### 業績改善\n| フレームワーク | 説明 |\n|--------------|------|\n| **間接費価値分析（OVA）** | 間接部門のコストと価値を分析し最適化 |\n| **プロフィットプール分析** | バリューチェーン全体の利益分布を可視化 |\n| **SCP分析** | 産業構造→企業行動→業績の因果関係を分析 |\n\n## 🔄 マッキンゼー式ワークフロー\n\n```mermaid\nflowchart TB\n    subgraph Phase1[\"Phase 1: 問題定義\"]\n        P1A[クライアントの真の課題特定] --> P1B[成功の定義]\n        P1B --> P1C[スコープと制約の確認]\n    end\n    \n    subgraph Phase2[\"Phase 2: 仮説構築\"]\n        P2A[初期仮説の設定] --> P2B[支持論拠の整理]\n        P2B --> P2C[反証可能な形式で表現]\n    end\n    \n    subgraph Phase3[\"Phase 3: イシュー分解\"]\n        P3A[イシューツリー作成] --> P3B[優先順位付け]\n        P3B --> P3C[分析計画策定]\n    end\n    \n    subgraph Phase4[\"Phase 4: データ収集・分析\"]\n        P4A[定量・定性データ収集] --> P4B[80/20分析]\n        P4B --> P4C[So What?で洞察抽出]\n    end\n    \n    subgraph Phase5[\"Phase 5: 統合・提言\"]\n        P5A[ピラミッド原則で構造化] --> P5B[アクションプラン策定]\n        P5B --> P5C[インパクト評価]\n    end\n    \n    Phase1 --> Phase2\n    Phase2 --> Phase3\n    Phase3 --> Phase4\n    Phase4 --> Phase5\n```\n\n## 🚀 使用例\n\n### 例1: 成長戦略の検討\n\n```\nユーザー: 「新規事業の方向性を検討したい」\n\nSHIKIGAMI:\n1. [Planner] 「成長の目標は何ですか？」→ 真の目的を探索\n2. [Deep Research] 市場情報を仮説駆動で収集（web_search / web_fetch 活用）\n3. [Framework] 3つの地平線、成長のグラニュラリティを適用\n4. [Writing] ピラミッド原則で構造化した提言レポートを生成\n```\n\n### 例2: 組織変革の診断\n\n```\nユーザー: 「組織の問題点を分析して」\n\nSHIKIGAMI:\n1. [Planner] 変革の目的・成功基準を確認\n2. [Deep Research] 組織情報を収集\n3. [Framework] マッキンゼーの7Sで整合性を分析\n4. [Writing] イシューツリーで課題を構造化したレポートを生成\n```\n\n### 例3: コスト削減プロジェクト\n\n```\nユーザー: 「間接費を削減したい」\n\nSHIKIGAMI:\n1. [Planner] 削減目標・制約を確認\n2. [Deep Research] 間接部門の機能・コストを調査\n3. [Framework] OVA（間接費価値分析）を適用\n4. [Writing] 優先順位付きの削減提言レポートを生成\n```\n\n## 📄 ライセンス\n\nMIT License\n\n## 👤 作者\n\nnahisaho\n","readmeFilename":"README.md"}