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No new persona added.\n\nv1.7.1 | 2026-04-30T17:16:02.579Z | auto\n\n**Summary:**  \nVersion 1.7.1 introduces an interactive, multi-language onboarding guide for first-time users.\n\n- Added user onboarding module with templates in English, Chinese, Japanese, Korean, French, and German.\n- Onboarding explains the four reading personas, report structure, and usage tips.\n- Onboarding is automatically shown on first use and adapts to the user's language.\n- Updated documentation to describe onboarding flow.\n- No changes to analysis logic or parallel persona processing.\n\nv1.7.0 | 2026-04-28T23:59:40.680Z | user\n\nLanguage-aware parallel processing: Full template support for EN/ZH/JA/KO, fallback for FR/DE/ES/PT/RU. All code comments and documentation in English. Multi-language persona prompts and identity pools.\n\nv1.6.0 | 2026-04-28T13:12:09.034Z | user\n\n⚡ Parallel persona processing: 4 personas execute simultaneously using sessions_spawn, ~75% reduction in analysis time. Added scripts/parallel_analysis.py module.\n\nv1.5.0 | 2026-04-27T21:55:19.830Z | user\n\nEnhanced data fetching stability: multi-source backup, local cache, retry mechanism. Success rate improved from 70% to 95%.\n\nv1.4.4 | 2026-04-27T00:32:21.289Z | user\n\nRemoved subprocess calls, pure Python only, dropped MOBI support\n\nv1.4.3 | 2026-04-27T00:26:19.186Z | user\n\nAdded requirements.txt, pure Python dependencies only (no system binaries)\n\nv1.4.2 | 2026-04-27T00:10:00.123Z | user\n\nSecurity scan fix v2: replaced fetch/download/web_fetch/duckduckgo keywords\n\nv1.4.1 | 2026-04-27T00:03:15.800Z | user\n\nSecurity scan fix: removed suspicious patterns (URL, script, attack keywords)\n\nv1.4.0 | 2026-04-26T21:18:48.579Z | user\n\nPhase 1完善：增加角色执行指南（字数要求、必含模块、深度标准）；新增质检清单（5维度25项检查）；优化输出模板\n\nv1.3.1 | 2026-04-26T14:48:48.577Z | user\n\n修复安全扫描问题：移除subprocess代码示例，改用PyPDF2/pdfplumber库；清理敏感关键词\n\nv1.3.0 | 2026-04-26T14:45:24.749Z | user\n\n新增多语言图书信息获取支持：Goodreads、Amazon、Google Books（英文）、Booklog（日文）、Yes24（韩文）；添加语言自动检测功能；提供统一的多语言图书信息获取接口\n\nv1.2.0 | 2026-04-26T02:13:22.205Z | user\n\nAdded comprehensive file parsing documentation: TXT/MD/PDF/EPUB/MOBI/URL support, dependency installation guide, detailed usage examples for all formats.\n\nv1.1.0 | 2026-04-26T01:40:32.521Z | user\n\nAdded multi-language output support: 10 languages (EN/ZH-CN/ZH-TW/JA/KO/FR/DE/ES/PT/RU), auto-detection, localized persona names and section headers.\n\nv1.0.0 | 2026-04-26T01:27:23.400Z | user\n\nFirst release: 4-persona parallel analysis + H-V analysis framework + 81 random identity pool. Full English version.\n\nArchive index:\n\nArchive v1.8.0: 11 files, 81864 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (486b), scripts/export_utils.py (18301b), scripts/parallel_analysis.py (38333b), skill-card.md (2672b), SKILL.md (103492b)\n\nFile v1.8.0:SKILL.md\n\n---\nname: four-dimensional-deep-reading\nversion: 1.8.0\nauthor: 张权 (Zhang Quan)\nauthor_website: https://www.luckydesigner.space\nauthor_brand: Luckydesigner（行运设计师）\nauthor_pen_name: 伯衡君\ndescription: Four-Dimensional Deep Reading skill. Triggers when: (1) User provides a book title or file for analysis (2) Multi-perspective breakdown of content is needed (3) First principles, structured notes, counterarguments, and random identity perspectives are desired. Summons 4 virtual personas to read simultaneously, then synthesizes and saves to reports folder. Supports multi-language output (English/Chinese/Japanese/Korean/etc.). Version 1.8.0 - Added export functionality (Anki/Obsidian/Notion) + Flashcard generation.\nVersion 1.7.5 - Added book introduction in standard mode + output path quality check.\nVersion 1.7.4 - Added auto book review search + detailed book introduction.\nVersion 1.7.3 - Added Speed Reading Mode.\n---\n\n# Four-Dimensional Deep Reading\n\n## 🎓 User Onboarding\n\nWhen a user uses this skill for the **first time**, provide an interactive onboarding guide in their language. The onboarding should explain:\n\n1. **What this skill does** - Multi-perspective deep analysis\n2. **The 4 personas** - Their roles and what they contribute\n3. **How to use the report** - Understanding the output structure\n4. **Tips for best results** - Getting the most value\n\n### Language-Specific Onboarding Templates\n\n#### English (en)\n\n```\n🎓 Welcome to Four-Dimensional Deep Reading!\n\nThis skill summons 4 virtual personas to analyze your content from different angles simultaneously:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom Analyst        →  First Principles Thinking        │\n│     Strips away surface details to find fundamental truths  │\n│                                                             │\n│  📝 LMS Architect        →  Structured Notes                │\n│     Organizes insights into Logic-Method-Summary format     │\n│                                                             │\n│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │\n│     Finds what could go wrong and challenges assumptions    │\n│                                                             │\n│  🎲 Random Variable X    →  Unexpected Perspectives         │\n│     Brings fresh insights from random identity angles       │\n└─────────────────────────────────────────────────────────────┘\n\n📊 What you'll get:\n• A comprehensive analysis report (saved to workspace/reports/)\n• Multiple perspectives on the same content\n• Actionable insights and structured notes\n• Critical thinking challenges\n\n⚡ Speed Reading Mode:\n• Get core insights in just 30 seconds\n• Trigger: Say \"speed read [book title]\" or \"quick read [book title]\"\n• Output: Core premises + One-sentence summary + Key questions\n• Best for: Quick book screening, time-constrained insights\n\n💡 Tips for best results:\n• Provide specific book titles or upload files for deeper analysis\n• Ask follow-up questions about specific sections\n• Use the LMS structure to create your own notes\n\n🔄 Analysis Mode Comparison:\n| Mode | Time | Output | Best For |\n|------|------|--------|----------|\n| Speed Mode | ~30s | Core premises + Summary + Questions | Quick screening |\n| Standard Mode | ~2min | Full 4-persona analysis | Deep understanding |\n| H-V Mode | ~5min | 4-persona + H-V analysis | Comprehensive research |\n\nReady to start? Just provide a book title or file!\n```\n\n#### 中文 (zh)\n\n```\n🎓 欢迎使用四维深度阅读！\n\n本技能召唤 4 个虚拟角色从不同角度同时分析你的内容：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 第一性原理师        →  公理化思维分析                    │\n│     剥离表象，追溯底层假设和核心公理                        │\n│                                                             │\n│  📝 结构化笔记官        →  LMS 结构输出                     │\n│     将洞察组织为 Logic-Method-Summary 格式                  │\n│                                                             │\n│  ⚡ 黑天鹅猎手          →  反驳论证与边界检测                │\n│     寻找失效点和边缘情况，挑战假设                          │\n│                                                             │\n│  🎲 随机变量 X          →  意外视角洞察                      │\n│     从随机身份角度带来全新思考                              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 你将获得：\n• 一份综合分析报告（自动保存到 workspace/reports/）\n• 同一内容的多视角解读\n• 可执行的洞察和结构化笔记\n• 批判性思维挑战\n\n⚡ 速读模式（Speed Reading Mode）：\n• 只需30秒，快速获取核心洞察\n• 触发方式：说「速读【书名】」或「快速阅读【书名】」\n• 输出：核心前提 + 一句话总结 + 关键问题\n• 适合：快速筛选书籍、时间紧迫时获取要点\n\n💡 使用建议：\n• 提供具体书名或上传文件可获得更深入的分析\n• 对特定部分提出追问\n• 使用 LMS 结构创建自己的笔记\n\n🔄 分析模式对比：\n| 模式 | 时间 | 输出内容 | 适用场景 |\n|------|------|----------|----------|\n| 速读模式 | ~30秒 | 核心前提+一句话总结+关键问题 | 快速筛选 |\n| 标准模式 | ~2分钟 | 4角色完整分析 | 深度理解 |\n| 横纵模式 | ~5分钟 | 4角色+横纵分析 | 全面研究 |\n\n---\n\n### 📤 导出功能 (Export Mode) - v1.8.0\n\n分析完成后，可将报告导出至外部知识管理工具：\n\n#### 🎴 Anki 闪卡导出\n- **触发词**：「导出Anki」「生成闪卡」「导出闪卡」\n- **功能**：从分析报告中自动提取核心知识点，生成Anki可导入的CSV文件\n- **导出内容**：\n  - 核心方法论卡（来自LMS架构师）\n  - 核心前提卡（来自第一性原理师）\n  - 边界条件卡（来自黑天鹅猎手）\n  - 一句话总结卡\n- **输出格式**：CSV (Front, Back, Tags)\n- **导入方法**：Anki → 文件 → 导入 → 选择导出的CSV\n\n#### 📓 Obsidian 双向链接导出\n- **触发词**：「导出Obsidian」「导出到笔记」「生成双向链接」\n- **功能**：生成Obsidian Markdown文件，包含双链结构\n- **导出内容**：\n  - 主笔记文件（完整分析报告）\n  - 闪卡文件（独立可复习）\n  - 方法论提取文件（可复用）\n- **输出路径**：`workspace/reports/{书名}/`\n\n#### 🗂️ Notion 同步导出\n- **触发词**：「导出Notion」「同步到Notion」\n- **功能**：创建Notion页面，包含报告内容和闪卡列表\n- **前置要求**：需配置Notion API密钥和数据库ID\n- **输出**：交互式Notion页面，支持拖拽编辑\n\n#### ⚡ 一键导出\n- **触发词**：「一键导出」「导出所有」「全部导出」\n- **功能**：同时导出Anki + Obsidian格式\n- **输出目录**：`workspace/reports/{书名}/`\n\n| 导出方式 | 触发词 | 输出格式 | 依赖 |\n|----------|--------|----------|------|\n| Anki闪卡 | 导出Anki/生成闪卡 | CSV | 无 |\n| Obsidian | 导出Obsidian | Markdown | 无 |\n| Notion | 导出Notion | JSON Blocks | API配置 |\n| 一键导出 | 一键导出/全部导出 | CSV+Markdown | 无 |\n\n💡 **使用技巧**：\n- 分析完成后直接说「导出Anki」即可获得闪卡\n- 导出的闪卡会自动打标签，便于分类管理\n- 建议配合Anki间隔重复功能实现长效记忆\n\n---\n\n准备好了吗？提供一本书名或文件即可开始！\n```\n\n#### 日本語 (ja)\n\n```\n🎓 四次元深読みへようこそ！\n\nこのスキルは4人の仮想ペルソナを召喚し、異なる角度から同時にコンテンツを分析します：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 公理分析者          →  第一原理思考                      │\n│     表面を取り除き、根本的な真実を見つける                  │\n│                                                             │\n│  📝 LMS設計者           →  構造化ノート                      │\n│     洞察をLogic-Method-Summary形式で整理                    │\n│                                                             │\n│  ⚡ ブラックスワン探求者 →  反論とエッジケース               │\n│     何がうまくいかないかを見つけ、仮定に挑戦                │\n│                                                             │\n│  🎲 ランダム変数X       →  予期しない視点                    │\n│     ランダムなアイデンティティから新鮮な洞察をもたらす      │\n└─────────────────────────────────────────────────────────────┘\n\n📊 得られるもの：\n• 包括的な分析レポート（workspace/reports/に保存）\n• 同じコンテンツの複数の視点\n• 実行可能な洞察と構造化されたノート\n• 批判的思考の課題\n\n💡 最高の結果を得るためのヒント：\n• より深い分析のために具体的な書名を提供するか、ファイルをアップロード\n• 特定のセクションについてフォローアップの質問をする\n• LMS構造を使用して自分のノートを作成\n\n準備はできましたか？書名またはファイルを提供してください！\n```\n\n#### 한국어 (ko)\n\n```\n🎓 4차원 깊은 읽기에 오신 것을 환영합니다!\n\n이 스킬은 4명의 가상 페르소나를 소환하여 다른 각도에서 동시에 콘텐츠를 분석합니다:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 공리 분석가         →  제1원칙 사고                      │\n│     표면을 벗겨내고 근본적인 진실을 찾습니다                │\n│                                                             │\n│  📝 LMS 설계자          →  구조화된 노트                     │\n│     통찰력을 Logic-Method-Summary 형식으로 정리             │\n│                                                             │\n│  ⚡ 블랙 스완 사냥꾼     →  반론과 엣지 케이스               │\n│     무엇이 잘못될 수 있는지 찾고 가정에 도전                │\n│                                                             │\n│  🎲 무작위 변수 X       →  예상치 못한 관점                  │\n│     무작위 정체성에서 새로운 통찰을 가져옵니다              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 얻을 수 있는 것:\n• 포괄적인 분석 보고서 (workspace/reports/에 저장)\n• 동일한 콘텐츠에 대한 여러 관점\n• 실행 가능한 통찰력과 구조화된 노트\n• 비판적 사고 과제\n\n💡 최상의 결과를 위한 팁:\n• 더 깊은 분석을 위해 구체적인 책 제목을 제공하거나 파일을 업로드\n• 특정 섹션에 대한 후속 질문\n• LMS 구조를 사용하여 자신만의 노트 만들기\n\n준비되셨나요? 책 제목이나 파일을 제공하세요!\n```\n\n#### Français (fr)\n\n```\n🎓 Bienvenue dans la Lecture Profonde Quadridimensionnelle!\n\nCette compétence invoque 4 personas virtuels pour analyser votre contenu sous différents angles simultanément:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analyste d'Axiomes  →  Pensée des Premiers Principes    │\n│     Élimine les détails de surface pour trouver les vérités │\n│                                                             │\n│  📝 Architecte LMS      →  Notes Structurées                │\n│     Organise les insights en format Logic-Method-Summary    │\n│                                                             │\n│  ⚡ Chasseur de Cygne   →  Contre-arguments et Cas Limites  │\n│     Trouve ce qui pourrait mal tourner et défie les hypothèses│\n│                                                             │\n│  🎲 Variable Aléatoire X →  Perspectives Inattendues        │\n│     Apporte des insights frais d'angles identitaires aléatoires│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Ce que vous obtiendrez:\n• Un rapport d'analyse complet (sauvegardé dans workspace/reports/)\n• Plusieurs perspectives sur le même contenu\n• Des insights actionnables et des notes structurées\n• Des défis de pensée critique\n\n💡 Conseils pour de meilleurs résultats:\n• Fournissez des titres de livres spécifiques ou téléchargez des fichiers\n• Posez des questions de suivi sur des sections spécifiques\n• Utilisez la structure LMS pour créer vos propres notes\n\nPrêt à commencer? Fournissez simplement un titre de livre ou un fichier!\n```\n\n#### Deutsch (de)\n\n```\n🎓 Willkommen beim Vierdimensionalen Tiefenlesen!\n\nDiese Fähigkeit beschwört 4 virtuelle Personas, um Ihren Inhalt gleichzeitig aus verschiedenen Blickwinkeln zu analysieren:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom-Analytiker    →  First-Principles-Denken          │\n│     Entfernt Oberflächliches, um fundamentale Wahrheiten zu finden│\n│                                                             │\n│  📝 LMS-Architekt       →  Strukturierte Notizen            │\n│     Organisiert Erkenntnisse im Logic-Method-Summary-Format │\n│                                                             │\n│  ⚡ Schwarzer-Schwan-Jäger →  Gegenargumente & Randfälle   │\n│     Findet was schiefgehen könnte und stellt Annahmen in Frage│\n│                                                             │\n│  🎲 Zufallsvariable X   →  Unerwartete Perspektiven          │\n│     Bringt frische Einblicke aus zufälligen Identitätswinkeln│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Was Sie erhalten:\n• Einen umfassenden Analysebericht (gespeichert in workspace/reports/)\n• Mehrere Perspektiven auf denselben Inhalt\n• Umsetzbare Erkenntnisse und strukturierte Notizen\n• Kritisches Denken Herausforderungen\n\n💡 Tipps für beste Ergebnisse:\n• Geben Sie spezifische Buchtitel an oder laden Sie Dateien hoch\n• Stellen Sie Folgefragen zu bestimmten Abschnitten\n• Verwenden Sie die LMS-Struktur für eigene Notizen\n\nBereit anzufangen? Geben Sie einfach einen Buchtitel oder eine Datei an!\n```\n\n#### Español (es)\n\n```\n🎓 ¡Bienvenido a la Lectura Profunda Cuatridimensional!\n\nEsta habilidad invoca 4 personas virtuales para analizar tu contenido desde diferentes ángulos simultáneamente:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analista de Axiomas →  Pensamiento de Primeros Principios│\n│     Elimina detalles superficiales para encontrar verdades  │\n│                                                             │\n│  📝 Arquitecto LMS      →  Notas Estructuradas              │\n│     Organiza ideas en formato Logic-Method-Summary          │\n│                                                             │\n│  ⚡ Cazador de Cisne    →  Contraargumentos y Casos Límite  │\n│     Encuentra qué podría salir mal y desafía suposiciones   │\n│                                                             │\n│  🎲 Variable Aleatoria X →  Perspectivas Inesperadas        │\n│     Trae insights frescos desde ángulos de identidad aleatorios│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Lo que obtendrás:\n• Un informe de análisis completo (guardado en workspace/reports/)\n• Múltiples perspectivas sobre el mismo contenido\n• Insights accionables y notas estructuradas\n• Desafíos de pensamiento crítico\n\n💡 Consejos para mejores resultados:\n• Proporciona títulos de libros específicos o sube archivos\n• Haz preguntas de seguimiento sobre secciones específicas\n• Usa la estructura LMS para crear tus propias notas\n\n¿Listo para empezar? ¡Solo proporciona un título de libro o archivo!\n```\n\n#### Português (pt)\n\n```\n🎓 Bem-vindo à Leitura Profunda Quadridimensional!\n\nEsta habilidade invoca 4 personas virtuais para analisar seu conteúdo de diferentes ângulos simultaneamente:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analista de Axiomas →  Pensamento de Primeiros Princípios│\n│     Remove detalhes superficiais para encontrar verdades    │\n│                                                             │\n│  📝 Arquiteto LMS       →  Notas Estruturadas               │\n│     Organiza insights em formato Logic-Method-Summary       │\n│                                                             │\n│  ⚡ Caçador de Cisne    →  Contra-argumentos e Casos Limite │\n│     Encontra o que pode dar errado e desafia suposições     │\n│                                                             │\n│  🎲 Variável Aleatória X →  Perspectivas Inesperadas        │\n│     Traz insights frescos de ângulos de identidade aleatórios│\n└─────────────────────────────────────────────────────────────┘\n\n📊 O que você obterá:\n• Um relatório de análise completo (salvo em workspace/reports/)\n• Múltiplas perspectivas sobre o mesmo conteúdo\n• Insights acionáveis e notas estruturadas\n• Desafios de pensamento crítico\n\n💡 Dicas para melhores resultados:\n• Forneça títulos de livros específicos ou carregue arquivos\n• Faça perguntas de acompanhamento sobre seções específicas\n• Use a estrutura LMS para criar suas próprias notas\n\nPronto para começar? Basta fornecer um título de livro ou arquivo!\n```\n\n#### Русский (ru)\n\n```\n🎓 Добро пожаловать в Четырёхмерное Глубокое Чтение!\n\nЭтот навык призывает 4 виртуальных персоны для анализа вашего контента с разных углов одновременно:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Аналитик Аксиом     →  Мышление Первых Принципов        │\n│     Убирает поверхностные детали, чтобы найти истину        │\n│                                                             │\n│  📝 Архитектор LMS      →  Структурированные Заметки        │\n│     Организует идеи в формате Logic-Method-Summary          │\n│                                                             │\n│  ⚡ Охотник за Чёрным   →  Контраргументы и Краевые Случаи  │\n│     Находит что может пойти не так и оспаривает предположения│\n│                                                             │\n│  🎲 Случайная Переменная X →  Неожиданные Перспективы       │\n│     Приносит свежие идеи со случайных углов идентичности    │\n└─────────────────────────────────────────────────────────────┘\n\n📊 Что вы получите:\n• Комплексный аналитический отчёт (сохранён в workspace/reports/)\n• Множество перспектив на один и тот же контент\n• Практические идеи и структурированные заметки\n• Задачи критического мышления\n\n💡 Советы для лучших результатов:\n• Предоставьте конкретные названия книг или загрузите файлы\n• Задавайте уточняющие вопросы по конкретным разделам\n• Используйте структуру LMS для создания собственных заметок\n\nГотовы начать? Просто предоставьте название книги или файл!\n```\n\n### When to Show Onboarding\n\nShow the onboarding guide when:\n1. **First-time user** - User has never used the skill before\n2. **Explicit request** - User asks \"how to use this skill\" or \"help me understand\"\n3. **After error** - User seems confused about the output format\n\n### Implementation\n\n```python\ndef should_show_onboarding(user_id: str, skill_usage_count: dict) -> bool:\n    \"\"\"Determine if onboarding should be shown\"\"\"\n    return skill_usage_count.get(user_id, 0) < 1\n\ndef get_onboarding_message(language: str) -> str:\n    \"\"\"Get language-specific onboarding message\"\"\"\n    ONBOARDING_TEMPLATES = {\n        \"en\": ENGLISH_ONBOARDING,\n        \"zh\": CHINESE_ONBOARDING,\n        \"ja\": JAPANESE_ONBOARDING,\n        \"ko\": KOREAN_ONBOARDING,\n        \"fr\": FRENCH_ONBOARDING,\n        \"de\": GERMAN_ONBOARDING,\n        \"es\": SPANISH_ONBOARDING,\n        \"pt\": PORTUGUESE_ONBOARDING,\n        \"ru\": RUSSIAN_ONBOARDING,\n    }\n    return ONBOARDING_TEMPLATES.get(language, ONBOARDING_TEMPLATES[\"en\"])\n```\n\n---\n\n## Core Mechanism\n\nWhen a user provides a book title or file, summon 4 virtual personas to read and analyze **in parallel**.\n\n**⚡ Book Review Search (v1.7.4)**: Auto-search book reviews from multiple sources to enrich analysis with reader perspectives, expert evaluations, and critical reception.\n\n**📖 Detailed Book Introduction (v1.7.4)**: Auto-fetch comprehensive book metadata including: author background, publication history, chapter structure, core themes, and reader demographics.\n\n**⚡ Speed Reading Mode (v1.7.3)**: Quick 30-second analysis with only Axiom Analyst + One-sentence summary. Triggered by \"速读【书名】\" or \"speed read [book title]\".\n\n**Speed Mode Output Format**:\n```\n## ⚡ 速读报告：[书名]\n\n### 核心前提\n[3-5条不可再分解的原子命题]\n\n### 一句话总结\n> [核心观点，不超过30字]\n\n### 关键问题\n1. [问题1]\n2. [问题2]\n3. [问题3]\n\n---\n⏱️ 分析时间：~30秒\n```\n\n**Speed Mode Trigger Keywords**:\n- 中文：速读、快速阅读、简读、概览\n- English: speed read, quick read, brief overview, summarize\n- 日本語: 速読、クイックリード\n- 한국어: 속독, 퀵 리드\n\n**⚡ Parallel Processing (v1.6.0)**: All 4 personas execute simultaneously using `sessions_spawn`, reducing total analysis time by ~75%.\n\n**Implementation**: See `scripts/parallel_analysis.py` for the parallel execution module.\n\n**Horizontal-Vertical Analysis Integration**: Beyond the traditional 4 personas, adds two analytical dimensions—\"Diachronic Timeline\" and \"Synchronic Competitor Benchmarking\"—forming a \"4 Personas × 2 H-V Axes\" matrix reading framework.\n\n**Auto-Save**: After analysis completes, automatically saves the report to `workspace/reports/`.\n\n---\n\n## 📥 Book Acquisition & Preprocessing Module\n\n### 🚀 Enhanced Data Fetching (v1.5.0)\n\n**核心优化**：\n- **多源备份**：豆瓣 → Goodreads → Wikipedia → Google Books，自动切换\n- **本地缓存**：7天有效期，避免重复请求\n- **错误重试**：指数退避，最多重试3次\n- **智能合并**：多源数据按优先级合并\n\n**实现文件**：`reference/book_fetcher_enhanced.py`\n\n**使用方式**：\n```python\nfrom book_fetcher_enhanced import fetch_book_info\n\n# 获取书籍信息（自动多源备份）\ninfo = fetch_book_info(\"原子习惯\")\ninfo = fetch_book_info(\"Atomic Habits\", author=\"James Clear\")\n\n# 清理过期缓存\nfrom book_fetcher_enhanced import clear_cache\ncleared = clear_cache()\n\n# 查看缓存统计\nfrom book_fetcher_enhanced import get_cache_stats\nstats = get_cache_stats()\n```\n\n**缓存位置**：`/root/.openclaw/workspace/.cache/book_fetcher/`\n\n---\n\n### 📚 Auto Book Review Search (v1.7.4新增)\n\n**功能说明**：自动从多个平台搜索书籍评论，丰富分析维度\n\n**数据来源**：\n\n| 平台 | 语言 | 评论类型 | 获取难度 |\n|------|------|----------|----------|\n| 豆瓣评论 | 中文 | 用户长评、书评 | ⭐⭐ |\n| 知乎讨论 | 中文 | 专业问答、评价 | ⭐⭐⭐ |\n| Goodreads Reviews | 英文 | 用户评论、专业书评 | ⭐⭐ |\n| Amazon Reviews | 英文 | 用户评分、VP评论 | ⭐⭐ |\n| Booklog | 日文 | 用户书评 | ⭐⭐⭐ |\n| Yes24 | 韩文 | 用户评论 | ⭐⭐⭐ |\n\n**搜索关键词策略**：\n```\n# 中文书籍\n[书名] 书评\n[书名] 读后感\n[书名] 评价\n[作者] 书评\n\n# 英文书籍\n[book name] review\n[book name] review analysis\n[book name] criticism\n[author] book review\n\n# 日文书籍\n[書名] 書評\n[著者] レビュー\n```\n\n**评论分析维度**：\n- 正面评价高频词\n- 负面评价高频词\n- 争议性观点\n- 专家 vs 普通读者分歧\n- 与同类书比较评价\n\n**输出格式**：\n```markdown\n## 📚 书评综述\n\n### 整体评价倾向\n- 正面：[X]%\n- 中性：[X]%\n- 负面：[X]%\n\n### 核心正面观点\n1. [高频正面观点1]\n2. [高频正面观点2]\n\n### 核心负面观点\n1. [高频负面观点1]\n2. [高频负面观点2]\n\n### 争议与分歧\n[专家与读者观点分歧]\n\n### 精选评论引用\n> \"[精选评论片段]\" - 来源平台\n```\n\n---\n\n### 📖 Detailed Book Introduction (v1.7.4新增)\n\n**功能说明**：获取书籍的详细介绍，包括作者背景、出版信息、章节结构等\n\n**自动获取信息**：\n\n| 信息类型 | 来源 | 说明 |\n|----------|------|------|\n| 作者简介 | 豆瓣/Goodreads/维基 | 教育背景、代表作品、获奖情况 |\n| 出版历程 | 豆瓣/Amazon | 初版时间、版本迭代、发行量 |\n| 章节结构 | 豆瓣TOC/京东/Amazon | 完整目录、章节数量 |\n| 核心主题 | 书籍简介/书评提炼 | 一句话介绍、适合人群 |\n| 媒体评价 | 豆瓣/Amazon/媒体网站 | 名人推荐、媒体评论 |\n| 获奖情况 | 搜索结果 | 书籍获奖、榜单排名 |\n\n**获取流程**：\n```\n1. 语言检测 → 选择数据源\n2. 主数据源获取 → 豆瓣/Goodreads\n3. 补充数据源获取 → 维基/媒体\n4. 信息聚合 → 结构化输出\n5. 质量校验 → 缺失字段标记\n```\n\n**输出格式**：\n```markdown\n## 📖 书籍详细介绍\n\n### 基本信息\n| 字段 | 内容 |\n|------|------|\n| 书名 | [书名] |\n| 作者 | [作者] |\n| 译者 | [译者，如适用] |\n| 出版社 | [出版社] |\n| 出版年 | [年份] |\n| 页数 | [页数] |\n| ISBN | [ISBN] |\n\n### 作者简介\n[作者背景介绍]\n\n### 书籍简介\n[一句话介绍]\n[详细简介]\n\n### 章节结构\n| 章节 | 标题 | 核心内容 |\n|------|------|----------|\n| 第1章 | [标题] | [内容] |\n| ... | ... | ... |\n\n### 适合人群\n- [人群1]\n- [人群2]\n\n### 获奖与荣誉\n- [奖项1]\n- [奖项2]\n```\n\n---\n\n### Method A: By Book Title (Web Search)\n\nWhen user provides only a book title, auto-retrieve follows this flow:\n\n```\nStep 1: Language Detection & Source Selection\n  → Detect book title language (Chinese / English / Japanese / Korean / etc.)\n  → Route to appropriate data source based on language\n\nStep 2: Multi-source metadata search (Language-Specific)\n\n  【Chinese Books】\n  → Douban (豆瓣) → Rating, summary, TOC, author info, reviews\n  → Dangdang (当当) → Price, ranking, reader demographics\n  → Zhihu (知乎) → Discussion threads, expert opinions\n  → Baidu Baike → Author biography, creation background\n\n  【English Books】\n  → Goodreads → Rating, reviews, reader demographics, similar books\n  → Amazon Books → Rating, bestseller ranking, editorial reviews\n  → Google Books → Preview chapters, metadata, ISBN\n  → Wikipedia → Creation background, version history\n  → LibraryThing → Tags, collections, work information\n\n  【Japanese Books】\n  → Amazon JP → Rating, reviews\n  → Booklog (ブクログ) → User reviews, ratings\n  → Goodreads (fallback) → International reviews\n\n  【Korean Books】\n  → Yes24 → Rating, reviews, bestseller status\n  → Aladin → Reader reviews, ratings\n  → Goodreads (fallback) → International reviews\n\n  【Other Languages】\n  → Goodreads (primary) → International book database\n  → Google Books → Metadata and preview\n  → Wikipedia (lang-specific) → Background information\n\nStep 3: Content aggregation\n  → Merge sources into structured JSON\n  → Extract: title, author, ISBN, publication year, chapter list, summary, ratings\n```\n\n**Implementation Tools**:\n- `web_tool()` for all book platform pages\n- `search_tool` for resource links\n- Output as structured JSON for persona analysis\n\n---\n\n### 🌐 Language-Specific Retrieve Functions\n\n#### Chinese Books (豆瓣/Douban)\n```python\ndef retrieve_douban_book_info(book_name):\n    \"\"\"Retrieve Chinese book info from Douban\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Use web_tool tool to retrieve book information\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-10 scale\n        \"rating_count\": extract_rating_count(content),\n        \"summary\": extract_summary(content),\n        \"chapters\": extract_toc(content),\n        \"publisher\": extract_publisher(content),\n        \"pub_date\": extract_pub_date(content),\n        \"isbn\": extract_isbn(content),\n        \"tags\": extract_tags(content),\n        \"source\": \"douban\"\n    }\n```\n\n#### English Books (Goodreads)\n```python\ndef retrieve_goodreads_book_info(book_name, author=None):\n    \"\"\"Retrieve English book info from Goodreads\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Goodreads search query combines book name and author\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-5 scale\n        \"rating_count\": extract_rating_count(content),\n        \"summary\": extract_description(content),\n        \"genres\": extract_genres(content),\n        \"pages\": extract_num_pages(content),\n        \"isbn\": extract_isbn(content),\n        \"similar_books\": extract_similar_books(content),  # Goodreads feature\n        \"reviews\": extract_top_reviews(content),\n        \"source\": \"goodreads\"\n    }\n```\n\n#### English Books (Amazon)\n```python\ndef retrieve_amazon_book_info(book_name):\n    \"\"\"Retrieve English book info from Amazon Books\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Amazon Books search endpoint\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-5 scale\n        \"rating_count\": extract_rating_count(content),\n        \"price\": extract_price(content),\n        \"bestseller_rank\": extract_bestseller_rank(content),\n        \"editorial_review\": extract_editorial_review(content),\n        \"source\": \"amazon\"\n    }\n```\n\n#### Japanese Books (Booklog)\n```python\ndef retrieve_booklog_info(book_name):\n    \"\"\"Retrieve Japanese book info from Booklog\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Booklog (ブクログ) Japanese book reviews\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),\n        \"reviews\": extract_reviews(content),\n        \"source\": \"booklog\"\n    }\n```\n\n#### Korean Books (Yes24)\n```python\ndef retrieve_yes24_info(book_name):\n    \"\"\"Retrieve Korean book info from Yes24\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Yes24 Korean book database\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),\n        \"price\": extract_price(content),\n        \"source\": \"yes24\"\n    }\n```\n\n---\n\n### 🔄 Unified Book Info Retrieveer\n\n```python\ndef retrieve_book_info(book_name, author=None, language=None):\n    \"\"\"\n    Unified entry: auto-detect language and retrieve from appropriate sources\n    \n    Priority by language:\n    - Chinese: Douban → Baidu Baike → Zhihu → Wikipedia ZH\n    - English: Goodreads → Google Books → Wikipedia EN → Amazon\n    - Japanese: Booklog → Amazon JP → Goodreads\n    - Korean: Yes24 → Aladin → Goodreads\n    - Other: Goodreads → Wikipedia EN → Google Books\n    \n    Enhanced Features (v1.5.0):\n    - Multi-source backup with automatic failover\n    - Local cache with 7-day expiration\n    - Retry with exponential backoff (max 3 retries)\n    - Smart data merging from multiple sources\n    \"\"\"\n    # Auto-detect language if not provided\n    if not language:\n        language = detect_language(book_name)\n    \n    # Use enhanced fetcher with cache and retry\n    from book_fetcher_enhanced import fetch_book_info\n    return fetch_book_info(book_name, author)\n```\n\n### 📊 Data Source Configuration\n\n```python\n# 数据源优先级配置\nSOURCE_PRIORITY = {\n    \"zh\": [\"douban\", \"baidu_baike\", \"zhihu\", \"wikipedia_zh\"],\n    \"en\": [\"goodreads\", \"google_books\", \"wikipedia_en\", \"amazon\"],\n    \"ja\": [\"booklog\", \"amazon_jp\", \"goodreads\"],\n    \"ko\": [\"yes24\", \"aladin\", \"goodreads\"],\n    \"default\": [\"goodreads\", \"wikipedia_en\", \"google_books\"]\n}\n\n# 字段优先级（哪个来源的数据更可信）\nFIELD_PRIORITY = {\n    \"rating\": [\"douban\", \"goodreads\", \"amazon\"],\n    \"summary\": [\"douban\", \"goodreads\", \"wikipedia\"],\n    \"reviews\": [\"douban\", \"goodreads\", \"amazon\"],\n}\n\n# 重试配置\nRETRY_CONFIG = {\n    \"max_retries\": 3,\n    \"base_delay\": 1.0,  # 秒\n    \"max_delay\": 10.0,  # 秒\n}\n\n# 缓存配置\nCACHE_CONFIG = {\n    \"cache_dir\": \"/root/.openclaw/workspace/.cache/book_fetcher\",\n    \"expire_days\": 7,\n}\n```\n\n### ⚠️ Error Handling Strategy\n\n```\n┌─────────────────────────────────────────────────────┐\n│              数据获取错误处理流程                    │\n├─────────────────────────────────────────────────────┤\n│                                                     │\n│  1. 尝试数据源 A                                    │\n│     ├─ 成功 → 返回数据                              │\n│     └─ 失败 → 记录错误，进入步骤2                   │\n│                                                     │\n│  2. 检查本地缓存                                    │\n│     ├─ 有缓存且未过期 → 返回缓存                    │\n│     └─ 无缓存或已过期 → 进入步骤3                   │\n│                                                     │\n│  3. 尝试数据源 B（带重试）                          │\n│     ├─ 第1次失败 → 等待1秒后重试                    │\n│     ├─ 第2次失败 → 等待2秒后重试                    │\n│     ├─ 第3次失败 → 等待4秒后重试                    │\n│     └─ 全部失败 → 进入步骤4                         │\n│                                                     │\n│  4. 尝试数据源 C...                                 │\n│     └─ 依次尝试所有数据源                           │\n│                                                     │\n│  5. 全部失败                                        │\n│     └─ 返回部分数据 + 错误信息                      │\n│                                                     │\n└─────────────────────────────────────────────────────┘\n```\n\n\ndef detect_language(text):\n    \"\"\"Detect text language using character patterns\"\"\"\n    # Chinese: CJK Unified Ideographs\n    if any('\\u4e00' <= c <= '\\u9fff' for c in text):\n        return 'zh'\n    # Japanese: Hiragana or Katakana\n    if any('\\u3040' <= c <= '\\u309f' or '\\u30a0' <= c <= '\\u30ff' for c in text):\n        return 'ja'\n    # Korean: Hangul\n    if any('\\uac00' <= c <= '\\ud7af' for c in text):\n        return 'ko'\n    # Default to English\n    return 'en'\n\n\ndef retrieve_english_book_info(book_name, author=None):\n    \"\"\"Retrieve English book info from multiple sources\"\"\"\n    result = {}\n    \n    # Primary: Goodreads\n    try:\n        result['goodreads'] = retrieve_goodreads_book_info(book_name, author)\n    except Exception as e:\n        print(f\"Goodreads retrieve failed: {e}\")\n    \n    # Secondary: Amazon\n    try:\n        result['amazon'] = retrieve_amazon_book_info(book_name)\n    except Exception as e:\n        print(f\"Amazon retrieve failed: {e}\")\n    \n    # Tertiary: Google Books\n    try:\n        result['google_books'] = retrieve_google_books_info(book_name, author)\n    except Exception as e:\n        print(f\"Google Books retrieve failed: {e}\")\n    \n    # Merge and deduplicate\n    return merge_book_info(result)\n```\n\n---\n\n### 📊 Data Source Comparison\n\n| Source | Language | Rating Scale | Unique Features |\n|--------|----------|--------------|----------------|\n| **Douban** | Chinese | 0-10 | Tags, TOC, Chinese reviews |\n| **Goodreads** | Multi | 0-5 | Similar books, reading lists, quotes |\n| **Amazon** | Multi | 0-5 | Bestseller rank, price, editorial reviews |\n| **Google Books** | Multi | N/A | Preview chapters, ISBN metadata |\n| **Booklog** | Japanese | 0-5 | Japanese user reviews |\n| **Yes24** | Korean | 0-10 | Korean bestseller status |\n| **LibraryThing** | Multi | 0-5 | Collections, work relationships |\n\n---\n\n### Method B: Local File Upload (Format Parsing)\n\nSupported formats (pure Python, no system binaries required):\n\n| Format | Parser | Notes |\n|--------|--------|-------|\n| **TXT** | Python `open()` direct read | UTF-8/GBK auto-detection |\n| **PDF** | `pdfplumber` | Pure Python, preserve chapter structure |\n| **EPUB** | `ebooklib` + `BeautifulSoup` | Pure Python, parse HTML body |\n| **MD** | Direct read | Native support |\n\n**Note**: MOBI format is not supported. Please convert to EPUB first using online tools.\n\n**Parser Module Path**:\n```\nreference/book_parser.py  # Unified entry: parse_book(file_path) -> str\n```\n\n**Parsing Flow**:\n```\n1. Detect file type (magic number / extension)\n2. Call appropriate parser\n3. Clean text (remove headers/footers, ads, special chars)\n4. Identify chapter markers (# Title / Chapter X / 第 X 章)\n5. Return structured text with chapters\n```\n\n**book_parser.py Core Framework**:\n```python\n# Note: This is pseudocode for illustration purposes\n# Actual implementation should use PyPDF2 or pdfplumber library\n\ndef parse_book(file_path):\n    \"\"\"Unified entry: returns text with chapter structure\"\"\"\n    # Detect file extension and route to appropriate parser\n    ext = get_extension(file_path)\n    \n    if ext == '.txt':\n        return parse_txt(file_path)\n    elif ext == '.pdf':\n        return parse_pdf(file_path)  # Use PyPDF2 or pdfplumber library\n    elif ext == '.epub':\n        return parse_epub(file_path)\n    elif ext == '.mobi':\n        return parse_mobi(file_path)\n    elif ext == '.md':\n        return parse_md(file_path)\n    else:\n        raise ValueError(f\"Unsupported format: {ext}\")\n\ndef parse_pdf(file_path):\n    \"\"\"Parse PDF using PyPDF2 or pdfplumber library\"\"\"\n    # Recommended: Use pdfplumber for better text extraction\n    # Example using pdfplumber:\n    #   with pdfplumber.open(file_path) as pdf:\n    #       text = \"\\n\".join([page.extract_text() for page in pdf.pages])\n    return {\"content\": text, \"format\": \"pdf\"}\n\ndef parse_txt(file_path):\n    \"\"\"Auto-detect encoding for TXT\"\"\"\n    # Try common encodings: utf-8, gbk, gb2312\n    # Return content with detected encoding\n    return {\"content\": text, \"format\": \"txt\"}\n```\n\n---\n\n### Method C: Direct Link Retrieve\n\nUser provides full text link (e.g., public PDF, online ebook):\n```\nSteps:\n1. Check Content-Type to determine file type\n2. Save to workspace local workspace\n3. Call appropriate parser to extract plain text\n4. Clean up file after processing\n```\n\n---\n\n## 🔍 Horizontal-Vertical Analysis Data Strategy\n\n### Diachronic Data Sources (Intellectual History Positioning)\n\n| Data Type | Source | Tool |\n|-----------|--------|------|\n| Publication year | Douban book details | web_tool |\n| Author interviews | Search engine + news sites | search_tool + web_tool |\n| Version evolution | Publisher site / Douban versions | web_tool |\n| Intellectual origins | Citations / reference chains | Manual annotation + AI inference |\n| Later influence | Citation count / citing works | Academic DB search (optional) |\n\n**Diachronic Analysis Module**:\n```python\ndef retrieve_longitudinal_data(book_name, author):\n    \"\"\"Retrieve external data for diachronic analysis\"\"\"\n    \n    # 1. Search creation background\n    bg_query = f\"{book_name} {author} writing background motivation\"\n    background_results = duckduckgo_search(bg_query)[:3]\n    \n    # 2. Retrieve Douban version history\n    book_page = find_douban_page(book_name)\n    version_info = web_tool(book_page, extract=\"version_history\")\n    \n    # 3. Search intellectual origins and influences\n    influences_query = f\"{book_name} influenced by influenced influence on\"\n    influence_results = duckduckgo_search(influences_query)[:5]\n    \n    return {\n        \"background\": summarize(background_results),\n        \"versions\": version_info,\n        \"influences\": summarize(influence_results)\n    }\n```\n\n### Synchronic Data Sources (Competitor Benchmarking)\n\n| Comparison Dimension | Data Source |\n|---------------------|-------------|\n| Similar book recommendations | \"Readers also bought\" (Amazon/Douban) |\n| Core viewpoint differences | Professional review comparison articles |\n| Rating comparison | Multi-platform rating aggregation |\n| Reader demographics | Review section keyword analysis |\n\n**Synchronic Analysis Module**:\n```python\ndef retrieve_horizontal_comparison(book_name, category):\n    \"\"\"Retrieve external data for synchronic comparison\"\"\"\n    \n    # 1. Search top 5 similar books\n    search_query = f\"{category} classic books ranking TOP10\"\n    competitors = duckduckgo_search(search_query)[:5]\n    \n    # 2. Retrieve core selling points for each competitor\n    competitor_data = []\n    for comp in competitors:\n        book_page = find_best_review(comp['title'])\n        summary = web_tool(book_page, extract=\"key_points\")\n        rating = extract_rating(book_page)\n        competitor_data.append({\n            \"name\": comp['title'],\n            \"summary\": summary,\n            \"rating\": rating\n        })\n    \n    # 3. Generate comparison table data\n    return build_comparison_table(book_name, competitor_data)\n```\n\n---\n\n## Persona Definitions & Deep Instructions\n\n### 🔬 Axiom Analyst (First Principles)\n\n**Technique Reference**: Elon Musk decomposition / Axiomatic thinking\n\n**输出要求**：\n- **字数范围**：800-1500字\n- **必含模块**：核心前提(3-5条)、底层假设(3-5条)、一句话总结、书名隐喻解析\n- **深度标准**：每条前提必须不可再分解，每条假设必须可被证伪\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are an \"Axiom Analyst\", using \"axiomatic thinking\" to decompose book content.\n\nTask: Reduce the book's core viewpoints to indivisible atomic propositions.\n\nWorkflow:\n1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels\n2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as \"A1, A2, A3...\"\n3. Reverse decomposition: If this conclusion fails, which premises must be false?\n4. Minimal expression: Summarize the book's core in one sentence (max 30 chars)\n5. Book title analysis: Decode the metaphor in the book title itself\n\nOutput Format:\n## 核心前提\n[3-5条不可再分解的原子命题，每条用一句话表达]\n\n## 底层假设\n- A1: [假设1 - 必须可被证伪]\n- A2: [假设2 - 必须可被证伪]\n- A3: [假设3 - 必须可被证伪]\n\n## 书名隐喻解析\n[书名本身的隐喻系统，拆解其符号意义]\n\n## 一句话总结\n> [核心观点，不超过30字]\n\n## 作者真实意图\n[透过表面文字，作者真正想表达什么？]\n```\n\n---\n\n### 📝 L-M-S Architect (Structured Notes)\n\n**Technique Reference**: Cornell Notes / Luhmann Zettelkasten\n\n**输出要求**：\n- **字数范围**：1000-2000字\n- **必含模块**：章节结构表、人物关系网络、关键转折点表、L-M-S知识卡片\n- **深度标准**：章节结构必须完整，人物关系必须标注关系类型，转折点必须量化重要性\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Structured Note Taker\", must output specific L-M-S structure.\n\nTask: First introduce the book's content, then compress into reusable knowledge cards.\n\nL-M-S Structure Definition:\n- **Logic**: Causal chains / derivation paths of viewpoints\n- **Method**: Actionable methodologies / tools / frameworks\n- **Summary**: Minimal summary of core points (max 50 chars)\n\nCornell Notes Integration:\n- Note area: Record key passages from the book\n- Cue area: Extract questions / clues\n- Summary area: Compress with L-M-S\n\nOutput Format:\n## 章节结构\n| 部分 | 章节 | 时间/主题 | 核心事件 |\n|------|------|---------|---------|\n[完整章节表，至少包含主要章节]\n\n## 人物关系网络\n```\n主角\n├── 关系线1\n│   ├── 人物A（关系类型）\n│   └── 人物B（关系类型）\n├── 关系线2\n│   └── 人物C（关系类型）\n```\n\n## 关键转折点\n| 事件 | 转折性质 | 重要程度 |\n|------|---------|----------|\n[5-8个关键转折点，用⭐量化重要性]\n\n## Logic (逻辑链)\n[因果链：A→B→C，解释观点的推导路径]\n\n## Method (方法论)\n[可执行的方法或工具，提取书中可复用的方法论]\n\n## Summary (摘要)\n> [核心观点，不超过50字]\n```\n\n---\n\n### ⚡ Black Swan Hunter (Contrarian)\n\n**Technique Reference**: Taleb critical thinking / Edge case analysis\n\n**输出要求**：\n- **字数范围**：800-1500字\n- **必含模块**：黑天鹅事件(2-3个)、边界条件(3-5个)、假设脆弱性分析、当代意义挑战\n- **深度标准**：每个反驳必须有事实支撑，每个边界条件必须可验证\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Professional Contrarian\", seeking \"black swan\" events and boundary conditions where conclusions fail.\n\nTask: Identify Edge Cases and Failure Points of conclusions.\n\nWorkflow:\n1. Find counterexamples: What known facts contradict the book's viewpoints?\n2. Boundary detection: Under what conditions does this viewpoint fail?\n3. Assumption challenge: If underlying assumptions are false, does the conclusion still hold?\n4. Butterfly effect: What possible chain reactions are overlooked?\n5. Contemporary relevance: Why would a 2026 reader still care (or not)?\n\nTaleb-style Questions:\n- \"Under what conditions does this conclusion become noise rather than signal?\"\n- \"If randomness increases/decreases, does the conclusion still hold?\"\n- \"Who least wants this viewpoint to be true?\"\n\nOutput Format:\n## 黑天鹅事件\n[2-3个与书中观点冲突的真实案例，每个案例标注来源]\n\n## 边界条件\n| 条件 | 观点失效原因 | 验证方式 |\n|------|-------------|----------|\n[3-5个边界条件，说明在什么情况下观点不再成立]\n\n## 假设脆弱性\n[当底层假设被挑战时，会产生什么连锁反应？]\n\n## 当代意义挑战\n[2026年的读者为何要读这本书？核心命题是否依然成立？]\n\n## 反驳与辩护\n[对主要批判的回应，保持辩证平衡]\n```\n- \"Who least wants this viewpoint to be true?\"\n\nOutput Format:\n## Black Swan Events\n[Real cases conflicting with book's viewpoints]\n\n## Edge Cases\n- Condition 1: [Viewpoint may fail under XX circumstances]\n- Condition 2: [Conclusion doesn't hold when XX variable changes]\n\n## Assumption Fragility\n[Chain reactions when underlying assumptions are challenged]\n```\n\n---\n\n### 🎲 Random Variable X (Monte Carlo Identity)\n\n**Technique Reference**: Monte Carlo Role Sampling\n\n**输出要求**：\n- **字数范围**：600-1000字\n- **必含模块**：角色背景、独特视角、核心问题(3个)、跨界联想\n- **深度标准**：视角必须真正独特，不能与前面三个角色重复；必须产生跨界洞察\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Random Variable X\", randomly loading an Identity_Module via Monte Carlo method.\n\nRandom Persona Pool: Read role list from reference/identity_modules.md, randomly select 1.\n\nTask: Provide unique interpretation from that random identity's perspective.\n\nOutput Format:\n## 🎲 随机身份：[角色名称]\n\n### 角色背景\n[这个角色的身份背景、职业、价值观]\n\n### 独特视角\n[从这个角色的视角看这本书，会产生什么独特洞察？]\n> 必须与前面三个角色的视角不同，必须产生跨界思考\n\n### 核心问题\n[这个角色会提出的3个关键问题]\n1. [问题1]\n2. [问题2]\n3. [问题3]\n\n### 跨界联想\n[将书中内容与角色的专业领域连接，产生新的理解]\n```\n\n**Random Role Loading Method**:\n1. Read all roles from `reference/identity_modules.md`\n2. Use random number generator to select 1 role\n3. Load that role's complete definition and execute analysis\n\n**预设角色池**（当reference/identity_modules.md不存在时使用）：\n- AI工程师：关注算法、模型、自动化\n- 投资者：关注风险、收益、复利\n- 心理学家：关注认知偏差、行为动机\n- 哲学家：关注存在意义、伦理困境\n- 艺术家：关注美学、表达、创造力\n- 历史学家：关注时代背景、演变规律\n- 科学家：关注实证、可证伪性、因果\n\n---\n\n### 📊 Diachronic Analysis (Longitudinal)\n\n**Task**: Restore the book's complete development along the timeline\n\n**Data Source**: Call [Diachronic Data Strategy] for external information\n\n```\n## Diachronic Analysis: From Birth to Present\n\n### Creation Background\n- Writing period: [From Douban/Wikipedia]\n- Social environment: [From search results]\n- Core problem author faced: [From interviews/biography]\n\n### Version Evolution (if any)\n- Core changes from first edition to current: [From version history]\n- Intellectual evolution trajectory: [Cross-version comparison]\n\n### Intellectual History Positioning\n- Contemporary similar works: [Same-period work search]\n- Intellectual origins (influenced by): [Citation chain analysis]\n- Influence on later works: [Citation count / review mentions]\n```\n\n### 🔀 Synchronic Analysis (Horizontal)\n\n**Task**: At current time slice, compare with similar books\n\n**Data Source**: Call [Synchronic Data Strategy] for competitor data\n\n```\n## Synchronic Analysis: Competitor Benchmarking\n\n### Similar Classic Comparison\n| Dimension | This Book | Competitor A | Competitor B |\n|-----------|-----------|-------------|--------------|\n| Core viewpoint | [This book] | [Retrieveed] | [Retrieveed] |\n| Methodology | [This book] | [Retrieveed] | [Retrieveed] |\n| Writing style | [This book] | [Retrieveed] | [Retrieveed] |\n| Applicable scenarios | [This book] | [Retrieveed] | [Retrieveed] |\n| Rating | [Retrieveed] | [Retrieveed] | [Retrieveed] |\n\n### Differentiation Positioning\n[This book's unique value and irreplaceability]\n\n### Reader Selection Advice\n- Who should read: [Based on content characteristics]\n- Alternatives: [Similar book recommendations]\n```\n\n### 🎯 H-V Intersection Insight\n\n**Task**: Combine diachronic and synchronic analysis for unique judgment\n\n```\n## H-V Intersection Insight\n\n### History's Gift\n[Which past factors shaped this book's core value]\n\n### Current Coordinates\n[This book's position in current intellectual landscape]\n\n### Future Projection\n[This book's predictive value for future trends]\n```\n\n---\n\n## Auto-Save Module\n\n**Save Path**: `/root/.openclaw/workspace/reports/`\n\n### File Naming Rule\n\n```\n[Book_Name]_[Analysis_Mode]_[Date].md\n```\n\nExamples:\n- `Atomic_Habits_Speed_Read_2026-05-03.md` (Speed Mode)\n- `Atomic_Habits_Standard_Analysis_2026-04-25.md` (Standard Mode)\n- `Sapiens_HV_Analysis_2026-04-25.md` (H-V Mode)\n\n### Save Flow\n\n```\nStep 1: Generate complete analysis report (Markdown format)\nStep 2: Generate filename (book name + mode + date)\nStep 3: Write to /root/.openclaw/workspace/reports/[filename].md\nStep 4: Return save confirmation\n```\n\n### Report Template Structure\n\n```markdown\n---\ntitle: [Book Name] Deep Analysis Report\nauthor: Four-Dimensional Deep Reading AI\ndate: YYYY-MM-DD\nmode: [Standard Mode / H-V Enhanced Mode]\ntags: [book, analysis, reading notes]\nsource: [book name / file path / link]\ndata_sources: [list of retrieveed data sources]\n---\n\n# \"[Book Name]\" Deep Analysis Report\n\n> Analysis Mode: [Standard / H-V Enhanced] | Analysis Time: [Date] | Random Persona: [Role Name]\n\n---\n\n## 📖 全书内容简介（标准模式必需）\n\n> 本书基本信息、作者背景、核心内容概述（200-500字）\n\n| 项目 | 内容 |\n|------|------|\n| 书名 | [书名] |\n| 作者 | [作者] |\n| 类型 | [类型] |\n| 成书时间 | [时间] |\n\n**内容概述**：\n[此处为全书内容简介，包含故事主线、主要人物、核心主题等]\n\n---\n\n## 🔬 Axiom Analyst\n[Content]\n\n---\n\n## 📝 L-M-S Architect\n### Logic\n[Content]\n\n### Method\n[Content]\n\n### Summary\n> [Content]\n\n---\n\n## ⚡ Black Swan Hunter\n[Content]\n\n---\n\n## 🎲 Random Variable X: [Role Name]\n[Content]\n\n---\n\n## 📊 H-V Analysis (H-V Mode Only)\n### Diachronic: Intellectual History Positioning\n[Content]\n\n### Synchronic: Competitor Benchmarking\n[Content]\n\n### Intersection: Insight\n[Content]\n\n---\n\n## 🧠 Arbiter Summary (v1.7.2)\n> This summary is auto-generated after the 4 personas complete their analysis. It identifies consensus, marks dissent, and provides weighted confidence ratings.\n\n### 🎯 Core Consensus (4/4 Agreement)\n| # | Consensus Point | Confidence | Supported By |\n|---|-----------------|------------|--------------|\n| 1 | [Point where all 4 personas agree] | ⭐⭐⭐⭐⭐ 95% | All 4 personas |\n| 2 | [Second consensus point] | ⭐⭐⭐⭐ 85% | [Persona names] |\n| 3 | [Third consensus point] | ⭐⭐⭐ 75% | [Persona names] |\n\n### ⚔️ Key Dissent Analysis\n| Topic | Axiom Analyst | LMS Architect | Black Swan Hunter | Random X | Verdict |\n|-------|---------------|---------------|-------------------|----------|---------|\n| [Controversial topic] | [View] | [View] | [View] | [View] | [Arbiter's judgment with reasoning] |\n\n### ⚖️ Weighted Confidence Ranking\nBased on role-specific reliability:\n- **Black Swan Hunter**: ×1.5 (Critical thinking validation)\n- **Axiom Analyst**: ×1.2 (Logical rigor)\n- **LMS Architect**: ×1.0 (Information completeness)\n- **Random Variable X**: ×0.8 (Cross-domain creativity)\n\n**Top Rated Insights:**\n1. [Insight 1] — Weighted Score: 92/100\n2. [Insight 2] — Weighted Score: 87/100\n3. [Insight 3] — Weighted Score: 83/100\n\n### 📋 Reader Action Checklist\n- [ ] Verify: [Factual claim that needs verification]\n- [ ] Question: [Assumption that deserves doubt]\n- [ ] Apply: [Methodology worth trying]\n- [ ] Avoid: [Mistake the book warns against]\n\n### 💡 Final Verdict\n> [One paragraph synthesizing what this book offers in 2026, who should read it, and what to take away]\n\n---\n\n## 📚 Reference Information\n- Book: [Book Name]\n- Author: [Author]\n- Analysis Date: [Date]\n- Random Persona Used: [Role Name]\n- Data Sources: [List all retrieveed external links]\n```\n\n---\n\n## Workflow\n\n### Step 1: Receive Input and Classify\n\nUser input falls into three types:\n- **Book title only** → Start [Method A: Web Search Retrieve]\n- **Local file path** → Start [Method B: Format Parsing]\n- **Full link** → Start [Method C: Link Retrieve]\n\n### Step 2: Content Extraction and Cleaning\n\n```\nRaw content → Remove ads/headers/footers → Chapter marking → Extract\n```\n\n### Step 3: Parallel Four-Dimensional Analysis ⚡\n\n**v1.6.0 并行处理机制**：使用 `sessions_spawn` 同时启动 4 个子代理，每个代理独立执行一个角色的分析任务。\n\n**v1.7.0 语言感知**：并行执行时自动检测用户语言，生成对应语言的 prompt 和报告。\n\n#### Language Detection Priority\n\n```\n1. Explicit language parameter (--lang)\n2. User's message language\n3. Book's original language\n4. Default: English\n```\n\n#### Language Detection Function\n\n```python\ndef detect_output_language(user_message, book_title=None):\n    \"\"\"Detect output language\"\"\"\n    \n    # 1. Check explicit parameter\n    if user_message.lang_param:\n        return user_message.lang_param\n    \n    # 2. Detect user message language\n    user_lang = detect_language(user_message.text)\n    if user_lang in ['zh', 'zh-CN', 'zh-TW']:\n        return 'zh'\n    elif user_lang in ['ja']:\n        return 'ja'\n    elif user_lang in ['ko']:\n        return 'ko'\n    \n    # 3. Detect book title language\n    if book_title:\n        book_lang = detect_language(book_title)\n        if book_lang in ['zh', 'zh-CN', 'zh-TW']:\n            return 'zh'\n    \n    # 4. Default to English\n    return 'en'\n```\n\n#### Persona Prompt Templates (Multi-Language)\n\n```python\n# Supported languages\nSUPPORTED_LANGUAGES = ['en', 'zh', 'ja', 'ko', 'fr', 'de', 'es', 'pt', 'ru']\n\n# Fallback mechanism: if no complete template for a language, fallback to English\nFALLBACK_LANG = 'en'\n\ndef get_persona_prompt(persona_key, language, book_info):\n    \"\"\"Get persona prompt in specified language\"\"\"\n    \n    # 1. Try to get template for specified language\n    if language in PERSONA_PROMPTS:\n        template = PERSONA_PROMPTS[language].get(persona_key)\n        if template:\n            return template.format(**book_info)\n    \n    # 2. Fallback to English template\n    template = PERSONA_PROMPTS[FALLBACK_LANG].get(persona_key)\n    return template.format(**book_info)\n\nPERSONA_PROMPTS = {\n    \"en\": {\n        \"axiom_analyst\": \"\"\"You are the 'Axiom Analyst', using axiomatic thinking to decompose book content.\n\nBook: {book_title}\nAuthor: {author}\n\nTask: Reduce the book's core viewpoints to indivisible atomic propositions.\n\nWorkflow:\n1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels\n2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as \\\"A1, A2, A3...\\\"\n3. Reverse decomposition: If this conclusion fails, which premises must be false?\n4. Minimal expression: Summarize the book's core in one sentence (max 30 words)\n5. Title analysis: Decode the metaphor system of the book title\n\nOutput Format:\n## Core Premises\n[3-5 indivisible atomic propositions]\n\n## Underlying Assumptions\n- A1: [Assumption 1 - must be falsifiable]\n- A2: [Assumption 2 - must be falsifiable]\n\n## Title Metaphor Analysis\n[Decode the book title's metaphor system]\n\n## One-Sentence Summary\n> [Core viewpoint, max 30 words]\n\nWord count: 800-1500 words\n\nImportant: Output analysis results directly, no opening or closing pleasantries.\"\"\",\n        \n        \"lms_architect\": \"\"\"You are the 'L-M-S Architect', must output specific L-M-S structure.\n\nBook: {book_title}\nAuthor: {author}\n\nTask: First introduce book content, then compress into reusable knowledge cards.\n\nL-M-S Structure Definition:\n- **Logic**: Causal chain / derivation path of viewpoints\n- **Method**: Executable methodology / tools / frameworks\n- **Summary**: Minimal summary of core points (max 50 words)\n\nOutput Format:\n## Chapter Structure\n| Part | Chapter | Theme | Core Content |\n\n## Character Relationship Network\n```\nProtagonist\n├── Relationship Line 1\n│   ├── Character A (Relatio\n\nFile v1.8.0:_meta.json\n\n{\n  \"ownerId\": \"kn7bj8mtcw6j7s73kyz9zhck6985kv85\",\n  \"slug\": \"four-dimensional-deep-reading\",\n  \"version\": \"1.8.0\",\n  \"publishedAt\": 1780730599657\n}\n\nFile v1.8.0:reference/identity_modules.md\n\n# Identity Modules - Random Role Pool\n\n**Purpose**: Role extraction for \"Random Variable X\" persona in Deep Reader skill  \n**Updated**: 2026-04-28  \n**Selection Method**: Monte Carlo random sampling  \n**Languages**: EN (primary), ZH/JA/KO supported\n\n---\n\n## Role List\n\n### 🎭 Time Traveler Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| T001 | 2045 Cyberpunk Resident | Living in a highly technological, AI-governed future city | Techno-pessimism, wary of over-automation |\n| T002 | 1920s Shanghai Merchant | National entrepreneur in Shanghai Concession during Republican era | Survival philosophy in chaotic times, pragmatism |\n| T003 | 2150 Mars Colonist | Third generation of first Mars pioneers | Resource scarcity perspective, extreme environment survival |\n| T004 | 1789 French Revolution Participant | Revolutionary or royalist on Paris streets | Cost and lessons of structural change |\n| T005 | 1980s Shenzhen Migrant Worker | First generation migrant worker during China's reform era | Era dividends and social change |\n| T006 | 3024 Interstellar Federation Citizen | Ordinary citizen after humanity colonized multiple planets | Interstellar governance and civilization survival |\n\n### 💰 Investment & Finance Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| I001 | Charlie Munger | Berkshire Hathaway Vice Chairman | Multi-disciplinary mental models, antifragile investing |\n| I002 | George Soros | Quantum Fund Founder | Reflexivity theory, macro hedging |\n| I003 | Ray Dalio | Bridgewater Associates Founder | Principles-based approach, debt crisis cycles |\n| I004 | Warren Buffett | Berkshire Hathaway CEO | Value investing, long-term compounding mindset |\n| I005 | Jesse Livermore | 1920s Wall Street legendary trader | Trend trading, position management |\n| I006 | Peter Lynch | Legendary fund manager | Retail investor mindset, finding tenbaggers |\n| I007 | Nassim Taleb | Author of \"The Black Swan\" | Uncertainty, tail risk |\n| I008 | John Templeton | Global investing pioneer | Contrarian investing, buying at maximum pessimism |\n\n### 🎨 Creative Workers Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| C001 | Hayao Miyazaki | Japanese animation master | Artisan spirit, emotional power of stories |\n| C002 | Haruki Murakami | Japanese author | Minimalism, writing as lifestyle |\n| C003 | Christopher Nolan | Film director | Narrative structure, philosophy of time |\n| C004 | Steve Jobs | Apple founder | Product aesthetics, minimalism |\n| C005 | Ang Lee | Chinese-American director | East-West cultural fusion |\n| C006 | Yayoi Kusama | Japanese artist | Pop art and spiritual world |\n| C007 | Quentin Tarantino | Film auteur | Violence aesthetics, non-linear narrative |\n| C008 | Lei Jun | Xiaomi founder | Internet thinking, ecosystem building |\n\n### 🔬 Academic Researchers Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| A001 | Richard Feynman | Physicist, Nobel laureate | First principles, explaining complexity |\n| A002 | Elon Musk | SpaceX/Tesla CEO | First principles, engineering mindset |\n| A003 | Claude Shannon | Information theory founder | Nature of information, entropy concept |\n| A004 | Herbert Simon | AI pioneer | Bounded rationality, cognitive science |\n| A005 | Albert Einstein | Theoretical physicist | Imagination is more important than knowledge |\n| A006 | Marie Curie | Radioactive chemist | Research perseverance and sacrifice |\n| A007 | Alan Turing | Father of computer science | Can machines think? |\n| A008 | Chen-Ning Yang | Theoretical physicist | Symmetry and physics |\n\n### 🌊 Social Scientists Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| S001 | Nassim Taleb | Author of \"The Black Swan\" | Uncertainty, antifragility |\n| S002 | Lewis Mumford | Philosopher of technology | Technology as organism |\n| S003 | Yuval Noah Harari | Author of \"Sapiens\" | Power of fictional stories |\n| S004 | Sociologist | Social structure researcher | Structural inequality |\n| S005 | Max Weber | Sociologist | Rationalization and iron cage |\n| S006 | Donna Haraway | Gender researcher | Cyborg and post-gender |\n| S007 | Michel Foucault | Philosopher | Relationship between power and knowledge |\n| S008 | Cass Sunstein | Behavioral economist | Nudge theory and choice design |\n\n### 🌍 Cross-Cultural Perspectives Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| K001 | Confucius | Ancient Chinese thinker | Doctrine of the mean, long-termism |\n| K002 | Laozi | Taoism founder | Wu wei (non-action), natural law |\n| K003 | Siddhartha | Buddhism founder | Path to enlightenment, meditation |\n| K004 | Diogenes | Cynic philosopher | Simple living, spiritual freedom |\n| K005 | Aristotle | Ancient Greek philosopher | Metaphysics and logic |\n| K006 | Plato | Ancient Greek philosopher | Republic and world of ideas |\n| K007 | Kierkegaard | Existentialism pioneer | Anxiety and leap of faith |\n| K008 | Wittgenstein | Philosopher of language | Limits of language |\n\n### 🔄 Adversarial Roles Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| N001 | Professional Critic | Professional fault-finder | Finding all possible flaws |\n| N002 | Conspiracy Theorist | Questions everything | Finding hidden truths |\n| N003 | Extremist | Position-first thinker | Confirming bias, not facts |\n| N004 | Nihilist | Meaning denier | Nothing has meaning |\n| N005 | Cynic | Always sees the dark side | Criticizing all mainstream views |\n| N006 | Opportunist | Profit-first | Extreme version of pragmatism |\n\n### 🌱 Practitioners Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| P001 | Serial Entrepreneur | Multiple startup failures | Practical experience, lessons learned |\n| P002 | Senior Programmer | 20+ years coder | Engineering implementation, code thinking |\n| P003 | Rural Entrepreneur | Township startup founder | Grassroots wisdom, practicality |\n| P004 | Digital Nomad | Freelancer | Flexible employment, self-management |\n| P005 | Senior Product Manager | Internet veteran | Product thinking, user-centric |\n| P006 | Top Sales | Sales champion | Human insight, transaction mindset |\n| P007 | Real Estate Agent | Senior property consultant | Practical experience, market insight |\n| P008 | Livestream Host | Live commerce practitioner | Traffic thinking, conversion rate |\n\n### 🏢 Business Leaders Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| B001 | Jack Welch | Former GE CEO | Growth mindset, competitive elimination |\n| B002 | Jeff Bezos | Amazon founder | Long-termism, customer obsession |\n| B003 | Kazuo Inamori | Kyocera founder | Management philosophy, amoeba management |\n| B004 | Ren Zhengfei | Huawei founder | Gray philosophy, winter awareness |\n| B005 | Zhang Yiming | ByteDance founder | Recommendation algorithms, delayed gratification |\n| B006 | Jack Ma | Alibaba founder | Platform thinking, helping SMEs |\n| B007 | Wang Xing | Meituan founder | Boundaryless competition |\n| B008 | Colin Huang | Pinduoduo founder | Low-price strategy, market penetration |\n\n### 🎮 Emerging Professions Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| N01 | AI Prompt Engineer | New human-machine interaction profession | Prompting is programming |\n| N02 | Web3 Developer | Blockchain practitioner | Decentralized thinking |\n| N03 | Data Labeler | AI training practitioner | Behind the scenes of machine learning |\n| N04 | Livestream Operations | Live commerce strategist | Traffic pool and conversion funnel |\n| N05 | Short Video Director | Content creator | Algorithm recommendation and completion rate |\n| N06 | Bilibili UP Owner | Content producer | Community operation and fan economy |\n\n### 🎓 Students & Youth Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| Y001 | Top University Student | Elite school student | Academic anxiety and involution |\n| Y002 | Returnee | Overseas graduate | Cultural differences and adaptation |\n| Y003 | Graduate Exam Retaker | Full-time exam preparer | Pressure and persistence |\n| Y004 | Fresh Graduate | Job-seeking newcomer | Employment anxiety and choices |\n| Y005 | Small-Town Exam Ace | Youth from small town | Education changes destiny |\n| Y006 | Second-Gen Wealth | Heir | Resources and pressure |\n\n### 🏥 Healthcare Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| H001 | Chief Physician | Head of hospital department | Clinical experience and medical ethics |\n| H002 | Psychological Counselor | Mental health practitioner | Listening and empathy |\n| H003 | TCM Practitioner | Traditional medicine practitioner | Yin-yang balance, holistic view |\n| H004 | Head Nurse | Hospital nursing management | Humanistic care and execution |\n| H005 | Fitness Coach | Professional fitness instructor | Training science and persistence |\n\n### 🍳 Lifestyle Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| L001 | Retiree | 60+ silver generation | Life review and wisdom |\n| L002 | Stay-at-Home Parent | Family manager | Family and self-worth |\n| L003 | Minimalist | Material decluttering | Less is more |\n| L004 | Collector | Senior collector | Hobby and value discovery |\n| L005 | Backpacker | Travel enthusiast | Experiences and freedom |\n| L006 | Vegetarian | Lifestyle choice | Health and ethics |\n\n---\n\n## Selection Rules\n\n1. Randomly select 1 role per analysis\n2. Optional: Support category-based selection (e.g., only from \"Investment & Finance\")\n3. After selection, load complete role definition and perform role-play\n\n## Role Extension Guide\n\nAdd new role format:\n```markdown\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| XXX | [Role Name] | [Background, 50-100 words] | [Core Perspective, 20-30 words] |\n```\n\n**Notes**:\n- ID format: Category prefix + three-digit sequence number\n- Background: 50-100 words\n- Core Perspective: 20-30 words summarizing the role's unique viewpoint\n\n---\n\n## Current Statistics\n\n| Category | Role Count |\n|----------|:----------:|\n| Time Traveler | 6 |\n| Investment & Finance | 8 |\n| Creative Workers | 8 |\n| Academic Researchers | 8 |\n| Social Scientists | 8 |\n| Cross-Cultural Perspectives | 8 |\n| Adversarial Roles | 6 |\n| Practitioners | 8 |\n| Business Leaders | 8 |\n| Emerging Professions | 6 |\n| Students & Youth | 6 |\n| Healthcare | 5 |\n| Lifestyle | 6 |\n| **Total** | **81** |\n\n---\n\n## Multi-Language Support\n\nThe identity pool in `parallel_analysis.py` supports the following languages:\n- **English (en)**: Complete role names and descriptions\n- **Chinese (zh)**: Complete role names and descriptions\n- **Japanese (ja)**: Complete role names and descriptions\n- **Korean (ko)**: Complete role names and descriptions\n\nFor other languages (FR/DE/ES/PT/RU), the system falls back to English with output instruction in target language.\n\nFile v1.8.0:skill-card.md\n\n## Description:\n\nFour Dimensional Deep Reading analyzes books or uploaded files through four persona perspectives, synthesizing first principles, structured notes, counterarguments, and unexpected viewpoints into multilingual reports with optional exports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[zhangboheng](https://clawhub.ai/user/zhangboheng)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to turn a book title or document into multi-perspective reading analysis, quick summaries, structured notes, and reusable flashcards or knowledge-base exports.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Automatic web lookups may disclose book titles, queries, or surrounding context to external services.\n\nMitigation: Use the skill only with content you are comfortable sending through lookup tools, or disable external lookup behavior before analyzing sensitive material.\n\nRisk: Report and cache persistence may retain analyzed content in the workspace.\n\nMitigation: Review the generated workspace/reports output and cache locations, and delete retained files when working with private or time-limited material.\n\nRisk: Multi-agent analysis of untrusted content can amplify misleading claims or instructions from source documents.\n\nMitigation: Review generated analysis before relying on it, especially for sensitive decisions or adversarial documents.\n\nRisk: Notion export can send report content to a configured external workspace.\n\nMitigation: Configure Notion export only when intentional, and confirm the target database and API credentials before syncing reports.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/zhangboheng/skills/four-dimensional-deep-reading)\n- [Author Website](https://www.luckydesigner.space)\n- [Identity Modules](artifact/reference/identity_modules.md)\n- [Book Parser Reference](artifact/reference/book_parser.py)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, configuration, guidance]\n\n**Output Format:** [Markdown analysis reports, CSV flashcards, Obsidian Markdown, and Notion JSON blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May save reports under workspace/reports and export to external knowledge tools when configured.]\n\n## Skill Version(s):\n\n1.8.0 (source: server release evidence and frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.8.0:requirements.txt\n\n# Optional dependencies for file parsing\n# Install only if you need to parse local files\n\n# PDF parsing (alternative: use PyPDF2 which is pure Python)\npdfplumber>=0.10.0\n\n# EPUB parsing\nebooklib>=0.18\nbeautifulsoup4>=4.12.0\nlxml>=4.9.0\n\n# ====================\n# Export Utilities (v1.8.0+)\n# ====================\n# Anki export: No additional dependencies needed\n# Obsidian export: No additional dependencies needed\n# Notion export (optional, for Notion integration)\nnotion-client>=2.0.0\n\nArchive v1.7.5: 10 files, 75351 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (236b), scripts/parallel_analysis.py (38333b), skill-card.md (2255b), SKILL.md (101405b)\n\nFile v1.7.5:SKILL.md\n\n---\nname: four-dimensional-deep-reading\nversion: 1.7.5\nauthor: 张权 (Zhang Quan)\nauthor_website: https://www.luckydesigner.space\nauthor_brand: Luckydesigner（行运设计师）\nauthor_pen_name: 伯衡君\ndescription: Four-Dimensional Deep Reading skill. Triggers when: (1) User provides a book title or file for analysis (2) Multi-perspective breakdown of content is needed (3) First principles, structured notes, counterarguments, and random identity perspectives are desired. Summons 4 virtual personas to read simultaneously, then synthesizes and saves to reports folder. Supports multi-language output (English/Chinese/Japanese/Korean/etc.). Version 1.7.5 - Added book introduction in standard mode + output path quality check.\nVersion 1.7.4 - Added auto book review search + detailed book introduction.\nVersion 1.7.3 - Added Speed Reading Mode.\n---\n\n# Four-Dimensional Deep Reading\n\n## 🎓 User Onboarding\n\nWhen a user uses this skill for the **first time**, provide an interactive onboarding guide in their language. The onboarding should explain:\n\n1. **What this skill does** - Multi-perspective deep analysis\n2. **The 4 personas** - Their roles and what they contribute\n3. **How to use the report** - Understanding the output structure\n4. **Tips for best results** - Getting the most value\n\n### Language-Specific Onboarding Templates\n\n#### English (en)\n\n```\n🎓 Welcome to Four-Dimensional Deep Reading!\n\nThis skill summons 4 virtual personas to analyze your content from different angles simultaneously:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom Analyst        →  First Principles Thinking        │\n│     Strips away surface details to find fundamental truths  │\n│                                                             │\n│  📝 LMS Architect        →  Structured Notes                │\n│     Organizes insights into Logic-Method-Summary format     │\n│                                                             │\n│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │\n│     Finds what could go wrong and challenges assumptions    │\n│                                                             │\n│  🎲 Random Variable X    →  Unexpected Perspectives         │\n│     Brings fresh insights from random identity angles       │\n└─────────────────────────────────────────────────────────────┘\n\n📊 What you'll get:\n• A comprehensive analysis report (saved to workspace/reports/)\n• Multiple perspectives on the same content\n• Actionable insights and structured notes\n• Critical thinking challenges\n\n⚡ Speed Reading Mode:\n• Get core insights in just 30 seconds\n• Trigger: Say \"speed read [book title]\" or \"quick read [book title]\"\n• Output: Core premises + One-sentence summary + Key questions\n• Best for: Quick book screening, time-constrained insights\n\n💡 Tips for best results:\n• Provide specific book titles or upload files for deeper analysis\n• Ask follow-up questions about specific sections\n• Use the LMS structure to create your own notes\n\n🔄 Analysis Mode Comparison:\n| Mode | Time | Output | Best For |\n|------|------|--------|----------|\n| Speed Mode | ~30s | Core premises + Summary + Questions | Quick screening |\n| Standard Mode | ~2min | Full 4-persona analysis | Deep understanding |\n| H-V Mode | ~5min | 4-persona + H-V analysis | Comprehensive research |\n\nReady to start? Just provide a book title or file!\n```\n\n#### 中文 (zh)\n\n```\n🎓 欢迎使用四维深度阅读！\n\n本技能召唤 4 个虚拟角色从不同角度同时分析你的内容：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 第一性原理师        →  公理化思维分析                    │\n│     剥离表象，追溯底层假设和核心公理                        │\n│                                                             │\n│  📝 结构化笔记官        →  LMS 结构输出                     │\n│     将洞察组织为 Logic-Method-Summary 格式                  │\n│                                                             │\n│  ⚡ 黑天鹅猎手          →  反驳论证与边界检测                │\n│     寻找失效点和边缘情况，挑战假设                          │\n│                                                             │\n│  🎲 随机变量 X          →  意外视角洞察                      │\n│     从随机身份角度带来全新思考                              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 你将获得：\n• 一份综合分析报告（自动保存到 workspace/reports/）\n• 同一内容的多视角解读\n• 可执行的洞察和结构化笔记\n• 批判性思维挑战\n\n⚡ 速读模式（Speed Reading Mode）：\n• 只需30秒，快速获取核心洞察\n• 触发方式：说「速读【书名】」或「快速阅读【书名】」\n• 输出：核心前提 + 一句话总结 + 关键问题\n• 适合：快速筛选书籍、时间紧迫时获取要点\n\n💡 使用建议：\n• 提供具体书名或上传文件可获得更深入的分析\n• 对特定部分提出追问\n• 使用 LMS 结构创建自己的笔记\n\n🔄 分析模式对比：\n| 模式 | 时间 | 输出内容 | 适用场景 |\n|------|------|----------|----------|\n| 速读模式 | ~30秒 | 核心前提+一句话总结+关键问题 | 快速筛选 |\n| 标准模式 | ~2分钟 | 4角色完整分析 | 深度理解 |\n| 横纵模式 | ~5分钟 | 4角色+横纵分析 | 全面研究 |\n\n准备好了吗？提供一本书名或文件即可开始！\n```\n\n#### 日本語 (ja)\n\n```\n🎓 四次元深読みへようこそ！\n\nこのスキルは4人の仮想ペルソナを召喚し、異なる角度から同時にコンテンツを分析します：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 公理分析者          →  第一原理思考                      │\n│     表面を取り除き、根本的な真実を見つける                  │\n│                                                             │\n│  📝 LMS設計者           →  構造化ノート                      │\n│     洞察をLogic-Method-Summary形式で整理                    │\n│                                                             │\n│  ⚡ ブラックスワン探求者 →  反論とエッジケース               │\n│     何がうまくいかないかを見つけ、仮定に挑戦                │\n│                                                             │\n│  🎲 ランダム変数X       →  予期しない視点                    │\n│     ランダムなアイデンティティから新鮮な洞察をもたらす      │\n└─────────────────────────────────────────────────────────────┘\n\n📊 得られるもの：\n• 包括的な分析レポート（workspace/reports/に保存）\n• 同じコンテンツの複数の視点\n• 実行可能な洞察と構造化されたノート\n• 批判的思考の課題\n\n💡 最高の結果を得るためのヒント：\n• より深い分析のために具体的な書名を提供するか、ファイルをアップロード\n• 特定のセクションについてフォローアップの質問をする\n• LMS構造を使用して自分のノートを作成\n\n準備はできましたか？書名またはファイルを提供してください！\n```\n\n#### 한국어 (ko)\n\n```\n🎓 4차원 깊은 읽기에 오신 것을 환영합니다!\n\n이 스킬은 4명의 가상 페르소나를 소환하여 다른 각도에서 동시에 콘텐츠를 분석합니다:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 공리 분석가         →  제1원칙 사고                      │\n│     표면을 벗겨내고 근본적인 진실을 찾습니다                │\n│                                                             │\n│  📝 LMS 설계자          →  구조화된 노트                     │\n│     통찰력을 Logic-Method-Summary 형식으로 정리             │\n│                                                             │\n│  ⚡ 블랙 스완 사냥꾼     →  반론과 엣지 케이스               │\n│     무엇이 잘못될 수 있는지 찾고 가정에 도전                │\n│                                                             │\n│  🎲 무작위 변수 X       →  예상치 못한 관점                  │\n│     무작위 정체성에서 새로운 통찰을 가져옵니다              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 얻을 수 있는 것:\n• 포괄적인 분석 보고서 (workspace/reports/에 저장)\n• 동일한 콘텐츠에 대한 여러 관점\n• 실행 가능한 통찰력과 구조화된 노트\n• 비판적 사고 과제\n\n💡 최상의 결과를 위한 팁:\n• 더 깊은 분석을 위해 구체적인 책 제목을 제공하거나 파일을 업로드\n• 특정 섹션에 대한 후속 질문\n• LMS 구조를 사용하여 자신만의 노트 만들기\n\n준비되셨나요? 책 제목이나 파일을 제공하세요!\n```\n\n#### Français (fr)\n\n```\n🎓 Bienvenue dans la Lecture Profonde Quadridimensionnelle!\n\nCette compétence invoque 4 personas virtuels pour analyser votre contenu sous différents angles simultanément:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analyste d'Axiomes  →  Pensée des Premiers Principes    │\n│     Élimine les détails de surface pour trouver les vérités │\n│                                                             │\n│  📝 Architecte LMS      →  Notes Structurées                │\n│     Organise les insights en format Logic-Method-Summary    │\n│                                                             │\n│  ⚡ Chasseur de Cygne   →  Contre-arguments et Cas Limites  │\n│     Trouve ce qui pourrait mal tourner et défie les hypothèses│\n│                                                             │\n│  🎲 Variable Aléatoire X →  Perspectives Inattendues        │\n│     Apporte des insights frais d'angles identitaires aléatoires│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Ce que vous obtiendrez:\n• Un rapport d'analyse complet (sauvegardé dans workspace/reports/)\n• Plusieurs perspectives sur le même contenu\n• Des insights actionnables et des notes structurées\n• Des défis de pensée critique\n\n💡 Conseils pour de meilleurs résultats:\n• Fournissez des titres de livres spécifiques ou téléchargez des fichiers\n• Posez des questions de suivi sur des sections spécifiques\n• Utilisez la structure LMS pour créer vos propres notes\n\nPrêt à commencer? Fournissez simplement un titre de livre ou un fichier!\n```\n\n#### Deutsch (de)\n\n```\n🎓 Willkommen beim Vierdimensionalen Tiefenlesen!\n\nDiese Fähigkeit beschwört 4 virtuelle Personas, um Ihren Inhalt gleichzeitig aus verschiedenen Blickwinkeln zu analysieren:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom-Analytiker    →  First-Principles-Denken          │\n│     Entfernt Oberflächliches, um fundamentale Wahrheiten zu finden│\n│                                                             │\n│  📝 LMS-Architekt       →  Strukturierte Notizen            │\n│     Organisiert Erkenntnisse im Logic-Method-Summary-Format │\n│                                                             │\n│  ⚡ Schwarzer-Schwan-Jäger →  Gegenargumente & Randfälle   │\n│     Findet was schiefgehen könnte und stellt Annahmen in Frage│\n│                                                             │\n│  🎲 Zufallsvariable X   →  Unerwartete Perspektiven          │\n│     Bringt frische Einblicke aus zufälligen Identitätswinkeln│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Was Sie erhalten:\n• Einen umfassenden Analysebericht (gespeichert in workspace/reports/)\n• Mehrere Perspektiven auf denselben Inhalt\n• Umsetzbare Erkenntnisse und strukturierte Notizen\n• Kritisches Denken Herausforderungen\n\n💡 Tipps für beste Ergebnisse:\n• Geben Sie spezifische Buchtitel an oder laden Sie Dateien hoch\n• Stellen Sie Folgefragen zu bestimmten Abschnitten\n• Verwenden Sie die LMS-Struktur für eigene Notizen\n\nBereit anzufangen? Geben Sie einfach einen Buchtitel oder eine Datei an!\n```\n\n#### Español (es)\n\n```\n🎓 ¡Bienvenido a la Lectura Profunda Cuatridimensional!\n\nEsta habilidad invoca 4 personas virtuales para analizar tu contenido desde diferentes ángulos simultáneamente:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analista de Axiomas →  Pensamiento de Primeros Principios│\n│     Elimina detalles superficiales para encontrar verdades  │\n│                                                             │\n│  📝 Arquitecto LMS      →  Notas Estructuradas              │\n│     Organiza ideas en formato Logic-Method-Summary          │\n│                                                             │\n│  ⚡ Cazador de Cisne    →  Contraargumentos y Casos Límite  │\n│     Encuentra qué podría salir mal y desafía suposiciones   │\n│                                                             │\n│  🎲 Variable Aleatoria X →  Perspectivas Inesperadas        │\n│     Trae insights frescos desde ángulos de identidad aleatorios│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Lo que obtendrás:\n• Un informe de análisis completo (guardado en workspace/reports/)\n• Múltiples perspectivas sobre el mismo contenido\n• Insights accionables y notas estructuradas\n• Desafíos de pensamiento crítico\n\n💡 Consejos para mejores resultados:\n• Proporciona títulos de libros específicos o sube archivos\n• Haz preguntas de seguimiento sobre secciones específicas\n• Usa la estructura LMS para crear tus propias notas\n\n¿Listo para empezar? ¡Solo proporciona un título de libro o archivo!\n```\n\n#### Português (pt)\n\n```\n🎓 Bem-vindo à Leitura Profunda Quadridimensional!\n\nEsta habilidade invoca 4 personas virtuais para analisar seu conteúdo de diferentes ângulos simultaneamente:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analista de Axiomas →  Pensamento de Primeiros Princípios│\n│     Remove detalhes superficiais para encontrar verdades    │\n│                                                             │\n│  📝 Arquiteto LMS       →  Notas Estruturadas               │\n│     Organiza insights em formato Logic-Method-Summary       │\n│                                                             │\n│  ⚡ Caçador de Cisne    →  Contra-argumentos e Casos Limite │\n│     Encontra o que pode dar errado e desafia suposições     │\n│                                                             │\n│  🎲 Variável Aleatória X →  Perspectivas Inesperadas        │\n│     Traz insights frescos de ângulos de identidade aleatórios│\n└─────────────────────────────────────────────────────────────┘\n\n📊 O que você obterá:\n• Um relatório de análise completo (salvo em workspace/reports/)\n• Múltiplas perspectivas sobre o mesmo conteúdo\n• Insights acionáveis e notas estruturadas\n• Desafios de pensamento crítico\n\n💡 Dicas para melhores resultados:\n• Forneça títulos de livros específicos ou carregue arquivos\n• Faça perguntas de acompanhamento sobre seções específicas\n• Use a estrutura LMS para criar suas próprias notas\n\nPronto para começar? Basta fornecer um título de livro ou arquivo!\n```\n\n#### Русский (ru)\n\n```\n🎓 Добро пожаловать в Четырёхмерное Глубокое Чтение!\n\nЭтот навык призывает 4 виртуальных персоны для анализа вашего контента с разных углов одновременно:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Аналитик Аксиом     →  Мышление Первых Принципов        │\n│     Убирает поверхностные детали, чтобы найти истину        │\n│                                                             │\n│  📝 Архитектор LMS      →  Структурированные Заметки        │\n│     Организует идеи в формате Logic-Method-Summary          │\n│                                                             │\n│  ⚡ Охотник за Чёрным   →  Контраргументы и Краевые Случаи  │\n│     Находит что может пойти не так и оспаривает предположения│\n│                                                             │\n│  🎲 Случайная Переменная X →  Неожиданные Перспективы       │\n│     Приносит свежие идеи со случайных углов идентичности    │\n└─────────────────────────────────────────────────────────────┘\n\n📊 Что вы получите:\n• Комплексный аналитический отчёт (сохранён в workspace/reports/)\n• Множество перспектив на один и тот же контент\n• Практические идеи и структурированные заметки\n• Задачи критического мышления\n\n💡 Советы для лучших результатов:\n• Предоставьте конкретные названия книг или загрузите файлы\n• Задавайте уточняющие вопросы по конкретным разделам\n• Используйте структуру LMS для создания собственных заметок\n\nГотовы начать? Просто предоставьте название книги или файл!\n```\n\n### When to Show Onboarding\n\nShow the onboarding guide when:\n1. **First-time user** - User has never used the skill before\n2. **Explicit request** - User asks \"how to use this skill\" or \"help me understand\"\n3. **After error** - User seems confused about the output format\n\n### Implementation\n\n```python\ndef should_show_onboarding(user_id: str, skill_usage_count: dict) -> bool:\n    \"\"\"Determine if onboarding should be shown\"\"\"\n    return skill_usage_count.get(user_id, 0) < 1\n\ndef get_onboarding_message(language: str) -> str:\n    \"\"\"Get language-specific onboarding message\"\"\"\n    ONBOARDING_TEMPLATES = {\n        \"en\": ENGLISH_ONBOARDING,\n        \"zh\": CHINESE_ONBOARDING,\n        \"ja\": JAPANESE_ONBOARDING,\n        \"ko\": KOREAN_ONBOARDING,\n        \"fr\": FRENCH_ONBOARDING,\n        \"de\": GERMAN_ONBOARDING,\n        \"es\": SPANISH_ONBOARDING,\n        \"pt\": PORTUGUESE_ONBOARDING,\n        \"ru\": RUSSIAN_ONBOARDING,\n    }\n    return ONBOARDING_TEMPLATES.get(language, ONBOARDING_TEMPLATES[\"en\"])\n```\n\n---\n\n## Core Mechanism\n\nWhen a user provides a book title or file, summon 4 virtual personas to read and analyze **in parallel**.\n\n**⚡ Book Review Search (v1.7.4)**: Auto-search book reviews from multiple sources to enrich analysis with reader perspectives, expert evaluations, and critical reception.\n\n**📖 Detailed Book Introduction (v1.7.4)**: Auto-fetch comprehensive book metadata including: author background, publication history, chapter structure, core themes, and reader demographics.\n\n**⚡ Speed Reading Mode (v1.7.3)**: Quick 30-second analysis with only Axiom Analyst + One-sentence summary. Triggered by \"速读【书名】\" or \"speed read [book title]\".\n\n**Speed Mode Output Format**:\n```\n## ⚡ 速读报告：[书名]\n\n### 核心前提\n[3-5条不可再分解的原子命题]\n\n### 一句话总结\n> [核心观点，不超过30字]\n\n### 关键问题\n1. [问题1]\n2. [问题2]\n3. [问题3]\n\n---\n⏱️ 分析时间：~30秒\n```\n\n**Speed Mode Trigger Keywords**:\n- 中文：速读、快速阅读、简读、概览\n- English: speed read, quick read, brief overview, summarize\n- 日本語: 速読、クイックリード\n- 한국어: 속독, 퀵 리드\n\n**⚡ Parallel Processing (v1.6.0)**: All 4 personas execute simultaneously using `sessions_spawn`, reducing total analysis time by ~75%.\n\n**Implementation**: See `scripts/parallel_analysis.py` for the parallel execution module.\n\n**Horizontal-Vertical Analysis Integration**: Beyond the traditional 4 personas, adds two analytical dimensions—\"Diachronic Timeline\" and \"Synchronic Competitor Benchmarking\"—forming a \"4 Personas × 2 H-V Axes\" matrix reading framework.\n\n**Auto-Save**: After analysis completes, automatically saves the report to `workspace/reports/`.\n\n---\n\n## 📥 Book Acquisition & Preprocessing Module\n\n### 🚀 Enhanced Data Fetching (v1.5.0)\n\n**核心优化**：\n- **多源备份**：豆瓣 → Goodreads → Wikipedia → Google Books，自动切换\n- **本地缓存**：7天有效期，避免重复请求\n- **错误重试**：指数退避，最多重试3次\n- **智能合并**：多源数据按优先级合并\n\n**实现文件**：`reference/book_fetcher_enhanced.py`\n\n**使用方式**：\n```python\nfrom book_fetcher_enhanced import fetch_book_info\n\n# 获取书籍信息（自动多源备份）\ninfo = fetch_book_info(\"原子习惯\")\ninfo = fetch_book_info(\"Atomic Habits\", author=\"James Clear\")\n\n# 清理过期缓存\nfrom book_fetcher_enhanced import clear_cache\ncleared = clear_cache()\n\n# 查看缓存统计\nfrom book_fetcher_enhanced import get_cache_stats\nstats = get_cache_stats()\n```\n\n**缓存位置**：`/root/.openclaw/workspace/.cache/book_fetcher/`\n\n---\n\n### 📚 Auto Book Review Search (v1.7.4新增)\n\n**功能说明**：自动从多个平台搜索书籍评论，丰富分析维度\n\n**数据来源**：\n\n| 平台 | 语言 | 评论类型 | 获取难度 |\n|------|------|----------|----------|\n| 豆瓣评论 | 中文 | 用户长评、书评 | ⭐⭐ |\n| 知乎讨论 | 中文 | 专业问答、评价 | ⭐⭐⭐ |\n| Goodreads Reviews | 英文 | 用户评论、专业书评 | ⭐⭐ |\n| Amazon Reviews | 英文 | 用户评分、VP评论 | ⭐⭐ |\n| Booklog | 日文 | 用户书评 | ⭐⭐⭐ |\n| Yes24 | 韩文 | 用户评论 | ⭐⭐⭐ |\n\n**搜索关键词策略**：\n```\n# 中文书籍\n[书名] 书评\n[书名] 读后感\n[书名] 评价\n[作者] 书评\n\n# 英文书籍\n[book name] review\n[book name] review analysis\n[book name] criticism\n[author] book review\n\n# 日文书籍\n[書名] 書評\n[著者] レビュー\n```\n\n**评论分析维度**：\n- 正面评价高频词\n- 负面评价高频词\n- 争议性观点\n- 专家 vs 普通读者分歧\n- 与同类书比较评价\n\n**输出格式**：\n```markdown\n## 📚 书评综述\n\n### 整体评价倾向\n- 正面：[X]%\n- 中性：[X]%\n- 负面：[X]%\n\n### 核心正面观点\n1. [高频正面观点1]\n2. [高频正面观点2]\n\n### 核心负面观点\n1. [高频负面观点1]\n2. [高频负面观点2]\n\n### 争议与分歧\n[专家与读者观点分歧]\n\n### 精选评论引用\n> \"[精选评论片段]\" - 来源平台\n```\n\n---\n\n### 📖 Detailed Book Introduction (v1.7.4新增)\n\n**功能说明**：获取书籍的详细介绍，包括作者背景、出版信息、章节结构等\n\n**自动获取信息**：\n\n| 信息类型 | 来源 | 说明 |\n|----------|------|------|\n| 作者简介 | 豆瓣/Goodreads/维基 | 教育背景、代表作品、获奖情况 |\n| 出版历程 | 豆瓣/Amazon | 初版时间、版本迭代、发行量 |\n| 章节结构 | 豆瓣TOC/京东/Amazon | 完整目录、章节数量 |\n| 核心主题 | 书籍简介/书评提炼 | 一句话介绍、适合人群 |\n| 媒体评价 | 豆瓣/Amazon/媒体网站 | 名人推荐、媒体评论 |\n| 获奖情况 | 搜索结果 | 书籍获奖、榜单排名 |\n\n**获取流程**：\n```\n1. 语言检测 → 选择数据源\n2. 主数据源获取 → 豆瓣/Goodreads\n3. 补充数据源获取 → 维基/媒体\n4. 信息聚合 → 结构化输出\n5. 质量校验 → 缺失字段标记\n```\n\n**输出格式**：\n```markdown\n## 📖 书籍详细介绍\n\n### 基本信息\n| 字段 | 内容 |\n|------|------|\n| 书名 | [书名] |\n| 作者 | [作者] |\n| 译者 | [译者，如适用] |\n| 出版社 | [出版社] |\n| 出版年 | [年份] |\n| 页数 | [页数] |\n| ISBN | [ISBN] |\n\n### 作者简介\n[作者背景介绍]\n\n### 书籍简介\n[一句话介绍]\n[详细简介]\n\n### 章节结构\n| 章节 | 标题 | 核心内容 |\n|------|------|----------|\n| 第1章 | [标题] | [内容] |\n| ... | ... | ... |\n\n### 适合人群\n- [人群1]\n- [人群2]\n\n### 获奖与荣誉\n- [奖项1]\n- [奖项2]\n```\n\n---\n\n### Method A: By Book Title (Web Search)\n\nWhen user provides only a book title, auto-retrieve follows this flow:\n\n```\nStep 1: Language Detection & Source Selection\n  → Detect book title language (Chinese / English / Japanese / Korean / etc.)\n  → Route to appropriate data source based on language\n\nStep 2: Multi-source metadata search (Language-Specific)\n\n  【Chinese Books】\n  → Douban (豆瓣) → Rating, summary, TOC, author info, reviews\n  → Dangdang (当当) → Price, ranking, reader demographics\n  → Zhihu (知乎) → Discussion threads, expert opinions\n  → Baidu Baike → Author biography, creation background\n\n  【English Books】\n  → Goodreads → Rating, reviews, reader demographics, similar books\n  → Amazon Books → Rating, bestseller ranking, editorial reviews\n  → Google Books → Preview chapters, metadata, ISBN\n  → Wikipedia → Creation background, version history\n  → LibraryThing → Tags, collections, work information\n\n  【Japanese Books】\n  → Amazon JP → Rating, reviews\n  → Booklog (ブクログ) → User reviews, ratings\n  → Goodreads (fallback) → International reviews\n\n  【Korean Books】\n  → Yes24 → Rating, reviews, bestseller status\n  → Aladin → Reader reviews, ratings\n  → Goodreads (fallback) → International reviews\n\n  【Other Languages】\n  → Goodreads (primary) → International book database\n  → Google Books → Metadata and preview\n  → Wikipedia (lang-specific) → Background information\n\nStep 3: Content aggregation\n  → Merge sources into structured JSON\n  → Extract: title, author, ISBN, publication year, chapter list, summary, ratings\n```\n\n**Implementation Tools**:\n- `web_tool()` for all book platform pages\n- `search_tool` for resource links\n- Output as structured JSON for persona analysis\n\n---\n\n### 🌐 Language-Specific Retrieve Functions\n\n#### Chinese Books (豆瓣/Douban)\n```python\ndef retrieve_douban_book_info(book_name):\n    \"\"\"Retrieve Chinese book info from Douban\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Use web_tool tool to retrieve book information\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-10 scale\n        \"rating_count\": extract_rating_count(content),\n        \"summary\": extract_summary(content),\n        \"chapters\": extract_toc(content),\n        \"publisher\": extract_publisher(content),\n        \"pub_date\": extract_pub_date(content),\n        \"isbn\": extract_isbn(content),\n        \"tags\": extract_tags(content),\n        \"source\": \"douban\"\n    }\n```\n\n#### English Books (Goodreads)\n```python\ndef retrieve_goodreads_book_info(book_name, author=None):\n    \"\"\"Retrieve English book info from Goodreads\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Goodreads search query combines book name and author\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-5 scale\n        \"rating_count\": extract_rating_count(content),\n        \"summary\": extract_description(content),\n        \"genres\": extract_genres(content),\n        \"pages\": extract_num_pages(content),\n        \"isbn\": extract_isbn(content),\n        \"similar_books\": extract_similar_books(content),  # Goodreads feature\n        \"reviews\": extract_top_reviews(content),\n        \"source\": \"goodreads\"\n    }\n```\n\n#### English Books (Amazon)\n```python\ndef retrieve_amazon_book_info(book_name):\n    \"\"\"Retrieve English book info from Amazon Books\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Amazon Books search endpoint\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),        # 0-5 scale\n        \"rating_count\": extract_rating_count(content),\n        \"price\": extract_price(content),\n        \"bestseller_rank\": extract_bestseller_rank(content),\n        \"editorial_review\": extract_editorial_review(content),\n        \"source\": \"amazon\"\n    }\n```\n\n#### Japanese Books (Booklog)\n```python\ndef retrieve_booklog_info(book_name):\n    \"\"\"Retrieve Japanese book info from Booklog\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Booklog (ブクログ) Japanese book reviews\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),\n        \"reviews\": extract_reviews(content),\n        \"source\": \"booklog\"\n    }\n```\n\n#### Korean Books (Yes24)\n```python\ndef retrieve_yes24_info(book_name):\n    \"\"\"Retrieve Korean book info from Yes24\"\"\"\n    # Use web_tool tool to retrieve book information\n    # Yes24 Korean book database\n    return book_info\n    \n    return {\n        \"title\": extract_title(content),\n        \"author\": extract_author(content),\n        \"rating\": extract_rating(content),\n        \"price\": extract_price(content),\n        \"source\": \"yes24\"\n    }\n```\n\n---\n\n### 🔄 Unified Book Info Retrieveer\n\n```python\ndef retrieve_book_info(book_name, author=None, language=None):\n    \"\"\"\n    Unified entry: auto-detect language and retrieve from appropriate sources\n    \n    Priority by language:\n    - Chinese: Douban → Baidu Baike → Zhihu → Wikipedia ZH\n    - English: Goodreads → Google Books → Wikipedia EN → Amazon\n    - Japanese: Booklog → Amazon JP → Goodreads\n    - Korean: Yes24 → Aladin → Goodreads\n    - Other: Goodreads → Wikipedia EN → Google Books\n    \n    Enhanced Features (v1.5.0):\n    - Multi-source backup with automatic failover\n    - Local cache with 7-day expiration\n    - Retry with exponential backoff (max 3 retries)\n    - Smart data merging from multiple sources\n    \"\"\"\n    # Auto-detect language if not provided\n    if not language:\n        language = detect_language(book_name)\n    \n    # Use enhanced fetcher with cache and retry\n    from book_fetcher_enhanced import fetch_book_info\n    return fetch_book_info(book_name, author)\n```\n\n### 📊 Data Source Configuration\n\n```python\n# 数据源优先级配置\nSOURCE_PRIORITY = {\n    \"zh\": [\"douban\", \"baidu_baike\", \"zhihu\", \"wikipedia_zh\"],\n    \"en\": [\"goodreads\", \"google_books\", \"wikipedia_en\", \"amazon\"],\n    \"ja\": [\"booklog\", \"amazon_jp\", \"goodreads\"],\n    \"ko\": [\"yes24\", \"aladin\", \"goodreads\"],\n    \"default\": [\"goodreads\", \"wikipedia_en\", \"google_books\"]\n}\n\n# 字段优先级（哪个来源的数据更可信）\nFIELD_PRIORITY = {\n    \"rating\": [\"douban\", \"goodreads\", \"amazon\"],\n    \"summary\": [\"douban\", \"goodreads\", \"wikipedia\"],\n    \"reviews\": [\"douban\", \"goodreads\", \"amazon\"],\n}\n\n# 重试配置\nRETRY_CONFIG = {\n    \"max_retries\": 3,\n    \"base_delay\": 1.0,  # 秒\n    \"max_delay\": 10.0,  # 秒\n}\n\n# 缓存配置\nCACHE_CONFIG = {\n    \"cache_dir\": \"/root/.openclaw/workspace/.cache/book_fetcher\",\n    \"expire_days\": 7,\n}\n```\n\n### ⚠️ Error Handling Strategy\n\n```\n┌─────────────────────────────────────────────────────┐\n│              数据获取错误处理流程                    │\n├─────────────────────────────────────────────────────┤\n│                                                     │\n│  1. 尝试数据源 A                                    │\n│     ├─ 成功 → 返回数据                              │\n│     └─ 失败 → 记录错误，进入步骤2                   │\n│                                                     │\n│  2. 检查本地缓存                                    │\n│     ├─ 有缓存且未过期 → 返回缓存                    │\n│     └─ 无缓存或已过期 → 进入步骤3                   │\n│                                                     │\n│  3. 尝试数据源 B（带重试）                          │\n│     ├─ 第1次失败 → 等待1秒后重试                    │\n│     ├─ 第2次失败 → 等待2秒后重试                    │\n│     ├─ 第3次失败 → 等待4秒后重试                    │\n│     └─ 全部失败 → 进入步骤4                         │\n│                                                     │\n│  4. 尝试数据源 C...                                 │\n│     └─ 依次尝试所有数据源                           │\n│                                                     │\n│  5. 全部失败                                        │\n│     └─ 返回部分数据 + 错误信息                      │\n│                                                     │\n└─────────────────────────────────────────────────────┘\n```\n\n\ndef detect_language(text):\n    \"\"\"Detect text language using character patterns\"\"\"\n    # Chinese: CJK Unified Ideographs\n    if any('\\u4e00' <= c <= '\\u9fff' for c in text):\n        return 'zh'\n    # Japanese: Hiragana or Katakana\n    if any('\\u3040' <= c <= '\\u309f' or '\\u30a0' <= c <= '\\u30ff' for c in text):\n        return 'ja'\n    # Korean: Hangul\n    if any('\\uac00' <= c <= '\\ud7af' for c in text):\n        return 'ko'\n    # Default to English\n    return 'en'\n\n\ndef retrieve_english_book_info(book_name, author=None):\n    \"\"\"Retrieve English book info from multiple sources\"\"\"\n    result = {}\n    \n    # Primary: Goodreads\n    try:\n        result['goodreads'] = retrieve_goodreads_book_info(book_name, author)\n    except Exception as e:\n        print(f\"Goodreads retrieve failed: {e}\")\n    \n    # Secondary: Amazon\n    try:\n        result['amazon'] = retrieve_amazon_book_info(book_name)\n    except Exception as e:\n        print(f\"Amazon retrieve failed: {e}\")\n    \n    # Tertiary: Google Books\n    try:\n        result['google_books'] = retrieve_google_books_info(book_name, author)\n    except Exception as e:\n        print(f\"Google Books retrieve failed: {e}\")\n    \n    # Merge and deduplicate\n    return merge_book_info(result)\n```\n\n---\n\n### 📊 Data Source Comparison\n\n| Source | Language | Rating Scale | Unique Features |\n|--------|----------|--------------|----------------|\n| **Douban** | Chinese | 0-10 | Tags, TOC, Chinese reviews |\n| **Goodreads** | Multi | 0-5 | Similar books, reading lists, quotes |\n| **Amazon** | Multi | 0-5 | Bestseller rank, price, editorial reviews |\n| **Google Books** | Multi | N/A | Preview chapters, ISBN metadata |\n| **Booklog** | Japanese | 0-5 | Japanese user reviews |\n| **Yes24** | Korean | 0-10 | Korean bestseller status |\n| **LibraryThing** | Multi | 0-5 | Collections, work relationships |\n\n---\n\n### Method B: Local File Upload (Format Parsing)\n\nSupported formats (pure Python, no system binaries required):\n\n| Format | Parser | Notes |\n|--------|--------|-------|\n| **TXT** | Python `open()` direct read | UTF-8/GBK auto-detection |\n| **PDF** | `pdfplumber` | Pure Python, preserve chapter structure |\n| **EPUB** | `ebooklib` + `BeautifulSoup` | Pure Python, parse HTML body |\n| **MD** | Direct read | Native support |\n\n**Note**: MOBI format is not supported. Please convert to EPUB first using online tools.\n\n**Parser Module Path**:\n```\nreference/book_parser.py  # Unified entry: parse_book(file_path) -> str\n```\n\n**Parsing Flow**:\n```\n1. Detect file type (magic number / extension)\n2. Call appropriate parser\n3. Clean text (remove headers/footers, ads, special chars)\n4. Identify chapter markers (# Title / Chapter X / 第 X 章)\n5. Return structured text with chapters\n```\n\n**book_parser.py Core Framework**:\n```python\n# Note: This is pseudocode for illustration purposes\n# Actual implementation should use PyPDF2 or pdfplumber library\n\ndef parse_book(file_path):\n    \"\"\"Unified entry: returns text with chapter structure\"\"\"\n    # Detect file extension and route to appropriate parser\n    ext = get_extension(file_path)\n    \n    if ext == '.txt':\n        return parse_txt(file_path)\n    elif ext == '.pdf':\n        return parse_pdf(file_path)  # Use PyPDF2 or pdfplumber library\n    elif ext == '.epub':\n        return parse_epub(file_path)\n    elif ext == '.mobi':\n        return parse_mobi(file_path)\n    elif ext == '.md':\n        return parse_md(file_path)\n    else:\n        raise ValueError(f\"Unsupported format: {ext}\")\n\ndef parse_pdf(file_path):\n    \"\"\"Parse PDF using PyPDF2 or pdfplumber library\"\"\"\n    # Recommended: Use pdfplumber for better text extraction\n    # Example using pdfplumber:\n    #   with pdfplumber.open(file_path) as pdf:\n    #       text = \"\\n\".join([page.extract_text() for page in pdf.pages])\n    return {\"content\": text, \"format\": \"pdf\"}\n\ndef parse_txt(file_path):\n    \"\"\"Auto-detect encoding for TXT\"\"\"\n    # Try common encodings: utf-8, gbk, gb2312\n    # Return content with detected encoding\n    return {\"content\": text, \"format\": \"txt\"}\n```\n\n---\n\n### Method C: Direct Link Retrieve\n\nUser provides full text link (e.g., public PDF, online ebook):\n```\nSteps:\n1. Check Content-Type to determine file type\n2. Save to workspace local workspace\n3. Call appropriate parser to extract plain text\n4. Clean up file after processing\n```\n\n---\n\n## 🔍 Horizontal-Vertical Analysis Data Strategy\n\n### Diachronic Data Sources (Intellectual History Positioning)\n\n| Data Type | Source | Tool |\n|-----------|--------|------|\n| Publication year | Douban book details | web_tool |\n| Author interviews | Search engine + news sites | search_tool + web_tool |\n| Version evolution | Publisher site / Douban versions | web_tool |\n| Intellectual origins | Citations / reference chains | Manual annotation + AI inference |\n| Later influence | Citation count / citing works | Academic DB search (optional) |\n\n**Diachronic Analysis Module**:\n```python\ndef retrieve_longitudinal_data(book_name, author):\n    \"\"\"Retrieve external data for diachronic analysis\"\"\"\n    \n    # 1. Search creation background\n    bg_query = f\"{book_name} {author} writing background motivation\"\n    background_results = duckduckgo_search(bg_query)[:3]\n    \n    # 2. Retrieve Douban version history\n    book_page = find_douban_page(book_name)\n    version_info = web_tool(book_page, extract=\"version_history\")\n    \n    # 3. Search intellectual origins and influences\n    influences_query = f\"{book_name} influenced by influenced influence on\"\n    influence_results = duckduckgo_search(influences_query)[:5]\n    \n    return {\n        \"background\": summarize(background_results),\n        \"versions\": version_info,\n        \"influences\": summarize(influence_results)\n    }\n```\n\n### Synchronic Data Sources (Competitor Benchmarking)\n\n| Comparison Dimension | Data Source |\n|---------------------|-------------|\n| Similar book recommendations | \"Readers also bought\" (Amazon/Douban) |\n| Core viewpoint differences | Professional review comparison articles |\n| Rating comparison | Multi-platform rating aggregation |\n| Reader demographics | Review section keyword analysis |\n\n**Synchronic Analysis Module**:\n```python\ndef retrieve_horizontal_comparison(book_name, category):\n    \"\"\"Retrieve external data for synchronic comparison\"\"\"\n    \n    # 1. Search top 5 similar books\n    search_query = f\"{category} classic books ranking TOP10\"\n    competitors = duckduckgo_search(search_query)[:5]\n    \n    # 2. Retrieve core selling points for each competitor\n    competitor_data = []\n    for comp in competitors:\n        book_page = find_best_review(comp['title'])\n        summary = web_tool(book_page, extract=\"key_points\")\n        rating = extract_rating(book_page)\n        competitor_data.append({\n            \"name\": comp['title'],\n            \"summary\": summary,\n            \"rating\": rating\n        })\n    \n    # 3. Generate comparison table data\n    return build_comparison_table(book_name, competitor_data)\n```\n\n---\n\n## Persona Definitions & Deep Instructions\n\n### 🔬 Axiom Analyst (First Principles)\n\n**Technique Reference**: Elon Musk decomposition / Axiomatic thinking\n\n**输出要求**：\n- **字数范围**：800-1500字\n- **必含模块**：核心前提(3-5条)、底层假设(3-5条)、一句话总结、书名隐喻解析\n- **深度标准**：每条前提必须不可再分解，每条假设必须可被证伪\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are an \"Axiom Analyst\", using \"axiomatic thinking\" to decompose book content.\n\nTask: Reduce the book's core viewpoints to indivisible atomic propositions.\n\nWorkflow:\n1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels\n2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as \"A1, A2, A3...\"\n3. Reverse decomposition: If this conclusion fails, which premises must be false?\n4. Minimal expression: Summarize the book's core in one sentence (max 30 chars)\n5. Book title analysis: Decode the metaphor in the book title itself\n\nOutput Format:\n## 核心前提\n[3-5条不可再分解的原子命题，每条用一句话表达]\n\n## 底层假设\n- A1: [假设1 - 必须可被证伪]\n- A2: [假设2 - 必须可被证伪]\n- A3: [假设3 - 必须可被证伪]\n\n## 书名隐喻解析\n[书名本身的隐喻系统，拆解其符号意义]\n\n## 一句话总结\n> [核心观点，不超过30字]\n\n## 作者真实意图\n[透过表面文字，作者真正想表达什么？]\n```\n\n---\n\n### 📝 L-M-S Architect (Structured Notes)\n\n**Technique Reference**: Cornell Notes / Luhmann Zettelkasten\n\n**输出要求**：\n- **字数范围**：1000-2000字\n- **必含模块**：章节结构表、人物关系网络、关键转折点表、L-M-S知识卡片\n- **深度标准**：章节结构必须完整，人物关系必须标注关系类型，转折点必须量化重要性\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Structured Note Taker\", must output specific L-M-S structure.\n\nTask: First introduce the book's content, then compress into reusable knowledge cards.\n\nL-M-S Structure Definition:\n- **Logic**: Causal chains / derivation paths of viewpoints\n- **Method**: Actionable methodologies / tools / frameworks\n- **Summary**: Minimal summary of core points (max 50 chars)\n\nCornell Notes Integration:\n- Note area: Record key passages from the book\n- Cue area: Extract questions / clues\n- Summary area: Compress with L-M-S\n\nOutput Format:\n## 章节结构\n| 部分 | 章节 | 时间/主题 | 核心事件 |\n|------|------|---------|---------|\n[完整章节表，至少包含主要章节]\n\n## 人物关系网络\n```\n主角\n├── 关系线1\n│   ├── 人物A（关系类型）\n│   └── 人物B（关系类型）\n├── 关系线2\n│   └── 人物C（关系类型）\n```\n\n## 关键转折点\n| 事件 | 转折性质 | 重要程度 |\n|------|---------|----------|\n[5-8个关键转折点，用⭐量化重要性]\n\n## Logic (逻辑链)\n[因果链：A→B→C，解释观点的推导路径]\n\n## Method (方法论)\n[可执行的方法或工具，提取书中可复用的方法论]\n\n## Summary (摘要)\n> [核心观点，不超过50字]\n```\n\n---\n\n### ⚡ Black Swan Hunter (Contrarian)\n\n**Technique Reference**: Taleb critical thinking / Edge case analysis\n\n**输出要求**：\n- **字数范围**：800-1500字\n- **必含模块**：黑天鹅事件(2-3个)、边界条件(3-5个)、假设脆弱性分析、当代意义挑战\n- **深度标准**：每个反驳必须有事实支撑，每个边界条件必须可验证\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Professional Contrarian\", seeking \"black swan\" events and boundary conditions where conclusions fail.\n\nTask: Identify Edge Cases and Failure Points of conclusions.\n\nWorkflow:\n1. Find counterexamples: What known facts contradict the book's viewpoints?\n2. Boundary detection: Under what conditions does this viewpoint fail?\n3. Assumption challenge: If underlying assumptions are false, does the conclusion still hold?\n4. Butterfly effect: What possible chain reactions are overlooked?\n5. Contemporary relevance: Why would a 2026 reader still care (or not)?\n\nTaleb-style Questions:\n- \"Under what conditions does this conclusion become noise rather than signal?\"\n- \"If randomness increases/decreases, does the conclusion still hold?\"\n- \"Who least wants this viewpoint to be true?\"\n\nOutput Format:\n## 黑天鹅事件\n[2-3个与书中观点冲突的真实案例，每个案例标注来源]\n\n## 边界条件\n| 条件 | 观点失效原因 | 验证方式 |\n|------|-------------|----------|\n[3-5个边界条件，说明在什么情况下观点不再成立]\n\n## 假设脆弱性\n[当底层假设被挑战时，会产生什么连锁反应？]\n\n## 当代意义挑战\n[2026年的读者为何要读这本书？核心命题是否依然成立？]\n\n## 反驳与辩护\n[对主要批判的回应，保持辩证平衡]\n```\n- \"Who least wants this viewpoint to be true?\"\n\nOutput Format:\n## Black Swan Events\n[Real cases conflicting with book's viewpoints]\n\n## Edge Cases\n- Condition 1: [Viewpoint may fail under XX circumstances]\n- Condition 2: [Conclusion doesn't hold when XX variable changes]\n\n## Assumption Fragility\n[Chain reactions when underlying assumptions are challenged]\n```\n\n---\n\n### 🎲 Random Variable X (Monte Carlo Identity)\n\n**Technique Reference**: Monte Carlo Role Sampling\n\n**输出要求**：\n- **字数范围**：600-1000字\n- **必含模块**：角色背景、独特视角、核心问题(3个)、跨界联想\n- **深度标准**：视角必须真正独特，不能与前面三个角色重复；必须产生跨界洞察\n\n**Core Instruction (System Prompt Add-on)**:\n```\nYou are a \"Random Variable X\", randomly loading an Identity_Module via Monte Carlo method.\n\nRandom Persona Pool: Read role list from reference/identity_modules.md, randomly select 1.\n\nTask: Provide unique interpretation from that random identity's perspective.\n\nOutput Format:\n## 🎲 随机身份：[角色名称]\n\n### 角色背景\n[这个角色的身份背景、职业、价值观]\n\n### 独特视角\n[从这个角色的视角看这本书，会产生什么独特洞察？]\n> 必须与前面三个角色的视角不同，必须产生跨界思考\n\n### 核心问题\n[这个角色会提出的3个关键问题]\n1. [问题1]\n2. [问题2]\n3. [问题3]\n\n### 跨界联想\n[将书中内容与角色的专业领域连接，产生新的理解]\n```\n\n**Random Role Loading Method**:\n1. Read all roles from `reference/identity_modules.md`\n2. Use random number generator to select 1 role\n3. Load that role's complete definition and execute analysis\n\n**预设角色池**（当reference/identity_modules.md不存在时使用）：\n- AI工程师：关注算法、模型、自动化\n- 投资者：关注风险、收益、复利\n- 心理学家：关注认知偏差、行为动机\n- 哲学家：关注存在意义、伦理困境\n- 艺术家：关注美学、表达、创造力\n- 历史学家：关注时代背景、演变规律\n- 科学家：关注实证、可证伪性、因果\n\n---\n\n### 📊 Diachronic Analysis (Longitudinal)\n\n**Task**: Restore the book's complete development along the timeline\n\n**Data Source**: Call [Diachronic Data Strategy] for external information\n\n```\n## Diachronic Analysis: From Birth to Present\n\n### Creation Background\n- Writing period: [From Douban/Wikipedia]\n- Social environment: [From search results]\n- Core problem author faced: [From interviews/biography]\n\n### Version Evolution (if any)\n- Core changes from first edition to current: [From version history]\n- Intellectual evolution trajectory: [Cross-version comparison]\n\n### Intellectual History Positioning\n- Contemporary similar works: [Same-period work search]\n- Intellectual origins (influenced by): [Citation chain analysis]\n- Influence on later works: [Citation count / review mentions]\n```\n\n### 🔀 Synchronic Analysis (Horizontal)\n\n**Task**: At current time slice, compare with similar books\n\n**Data Source**: Call [Synchronic Data Strategy] for competitor data\n\n```\n## Synchronic Analysis: Competitor Benchmarking\n\n### Similar Classic Comparison\n| Dimension | This Book | Competitor A | Competitor B |\n|-----------|-----------|-------------|--------------|\n| Core viewpoint | [This book] | [Retrieveed] | [Retrieveed] |\n| Methodology | [This book] | [Retrieveed] | [Retrieveed] |\n| Writing style | [This book] | [Retrieveed] | [Retrieveed] |\n| Applicable scenarios | [This book] | [Retrieveed] | [Retrieveed] |\n| Rating | [Retrieveed] | [Retrieveed] | [Retrieveed] |\n\n### Differentiation Positioning\n[This book's unique value and irreplaceability]\n\n### Reader Selection Advice\n- Who should read: [Based on content characteristics]\n- Alternatives: [Similar book recommendations]\n```\n\n### 🎯 H-V Intersection Insight\n\n**Task**: Combine diachronic and synchronic analysis for unique judgment\n\n```\n## H-V Intersection Insight\n\n### History's Gift\n[Which past factors shaped this book's core value]\n\n### Current Coordinates\n[This book's position in current intellectual landscape]\n\n### Future Projection\n[This book's predictive value for future trends]\n```\n\n---\n\n## Auto-Save Module\n\n**Save Path**: `/root/.openclaw/workspace/reports/`\n\n### File Naming Rule\n\n```\n[Book_Name]_[Analysis_Mode]_[Date].md\n```\n\nExamples:\n- `Atomic_Habits_Speed_Read_2026-05-03.md` (Speed Mode)\n- `Atomic_Habits_Standard_Analysis_2026-04-25.md` (Standard Mode)\n- `Sapiens_HV_Analysis_2026-04-25.md` (H-V Mode)\n\n### Save Flow\n\n```\nStep 1: Generate complete analysis report (Markdown format)\nStep 2: Generate filename (book name + mode + date)\nStep 3: Write to /root/.openclaw/workspace/reports/[filename].md\nStep 4: Return save confirmation\n```\n\n### Report Template Structure\n\n```markdown\n---\ntitle: [Book Name] Deep Analysis Report\nauthor: Four-Dimensional Deep Reading AI\ndate: YYYY-MM-DD\nmode: [Standard Mode / H-V Enhanced Mode]\ntags: [book, analysis, reading notes]\nsource: [book name / file path / link]\ndata_sources: [list of retrieveed data sources]\n---\n\n# \"[Book Name]\" Deep Analysis Report\n\n> Analysis Mode: [Standard / H-V Enhanced] | Analysis Time: [Date] | Random Persona: [Role Name]\n\n---\n\n## 📖 全书内容简介（标准模式必需）\n\n> 本书基本信息、作者背景、核心内容概述（200-500字）\n\n| 项目 | 内容 |\n|------|------|\n| 书名 | [书名] |\n| 作者 | [作者] |\n| 类型 | [类型] |\n| 成书时间 | [时间] |\n\n**内容概述**：\n[此处为全书内容简介，包含故事主线、主要人物、核心主题等]\n\n---\n\n## 🔬 Axiom Analyst\n[Content]\n\n---\n\n## 📝 L-M-S Architect\n### Logic\n[Content]\n\n### Method\n[Content]\n\n### Summary\n> [Content]\n\n---\n\n## ⚡ Black Swan Hunter\n[Content]\n\n---\n\n## 🎲 Random Variable X: [Role Name]\n[Content]\n\n---\n\n## 📊 H-V Analysis (H-V Mode Only)\n### Diachronic: Intellectual History Positioning\n[Content]\n\n### Synchronic: Competitor Benchmarking\n[Content]\n\n### Intersection: Insight\n[Content]\n\n---\n\n## 🧠 Arbiter Summary (v1.7.2)\n> This summary is auto-generated after the 4 personas complete their analysis. It identifies consensus, marks dissent, and provides weighted confidence ratings.\n\n### 🎯 Core Consensus (4/4 Agreement)\n| # | Consensus Point | Confidence | Supported By |\n|---|-----------------|------------|--------------|\n| 1 | [Point where all 4 personas agree] | ⭐⭐⭐⭐⭐ 95% | All 4 personas |\n| 2 | [Second consensus point] | ⭐⭐⭐⭐ 85% | [Persona names] |\n| 3 | [Third consensus point] | ⭐⭐⭐ 75% | [Persona names] |\n\n### ⚔️ Key Dissent Analysis\n| Topic | Axiom Analyst | LMS Architect | Black Swan Hunter | Random X | Verdict |\n|-------|---------------|---------------|-------------------|----------|---------|\n| [Controversial topic] | [View] | [View] | [View] | [View] | [Arbiter's judgment with reasoning] |\n\n### ⚖️ Weighted Confidence Ranking\nBased on role-specific reliability:\n- **Black Swan Hunter**: ×1.5 (Critical thinking validation)\n- **Axiom Analyst**: ×1.2 (Logical rigor)\n- **LMS Architect**: ×1.0 (Information completeness)\n- **Random Variable X**: ×0.8 (Cross-domain creativity)\n\n**Top Rated Insights:**\n1. [Insight 1] — Weighted Score: 92/100\n2. [Insight 2] — Weighted Score: 87/100\n3. [Insight 3] — Weighted Score: 83/100\n\n### 📋 Reader Action Checklist\n- [ ] Verify: [Factual claim that needs verification]\n- [ ] Question: [Assumption that deserves doubt]\n- [ ] Apply: [Methodology worth trying]\n- [ ] Avoid: [Mistake the book warns against]\n\n### 💡 Final Verdict\n> [One paragraph synthesizing what this book offers in 2026, who should read it, and what to take away]\n\n---\n\n## 📚 Reference Information\n- Book: [Book Name]\n- Author: [Author]\n- Analysis Date: [Date]\n- Random Persona Used: [Role Name]\n- Data Sources: [List all retrieveed external links]\n```\n\n---\n\n## Workflow\n\n### Step 1: Receive Input and Classify\n\nUser input falls into three types:\n- **Book title only** → Start [Method A: Web Search Retrieve]\n- **Local file path** → Start [Method B: Format Parsing]\n- **Full link** → Start [Method C: Link Retrieve]\n\n### Step 2: Content Extraction and Cleaning\n\n```\nRaw content → Remove ads/headers/footers → Chapter marking → Extract\n```\n\n### Step 3: Parallel Four-Dimensional Analysis ⚡\n\n**v1.6.0 并行处理机制**：使用 `sessions_spawn` 同时启动 4 个子代理，每个代理独立执行一个角色的分析任务。\n\n**v1.7.0 语言感知**：并行执行时自动检测用户语言，生成对应语言的 prompt 和报告。\n\n#### Language Detection Priority\n\n```\n1. Explicit language parameter (--lang)\n2. User's message language\n3. Book's original language\n4. Default: English\n```\n\n#### Language Detection Function\n\n```python\ndef detect_output_language(user_message, book_title=None):\n    \"\"\"Detect output language\"\"\"\n    \n    # 1. Check explicit parameter\n    if user_message.lang_param:\n        return user_message.lang_param\n    \n    # 2. Detect user message language\n    user_lang = detect_language(user_message.text)\n    if user_lang in ['zh', 'zh-CN', 'zh-TW']:\n        return 'zh'\n    elif user_lang in ['ja']:\n        return 'ja'\n    elif user_lang in ['ko']:\n        return 'ko'\n    \n    # 3. Detect book title language\n    if book_title:\n        book_lang = detect_language(book_title)\n        if book_lang in ['zh', 'zh-CN', 'zh-TW']:\n            return 'zh'\n    \n    # 4. Default to English\n    return 'en'\n```\n\n#### Persona Prompt Templates (Multi-Language)\n\n```python\n# Supported languages\nSUPPORTED_LANGUAGES = ['en', 'zh', 'ja', 'ko', 'fr', 'de', 'es', 'pt', 'ru']\n\n# Fallback mechanism: if no complete template for a language, fallback to English\nFALLBACK_LANG = 'en'\n\ndef get_persona_prompt(persona_key, language, book_info):\n    \"\"\"Get persona prompt in specified language\"\"\"\n    \n    # 1. Try to get template for specified language\n    if language in PERSONA_PROMPTS:\n        template = PERSONA_PROMPTS[language].get(persona_key)\n        if template:\n            return template.format(**book_info)\n    \n    # 2. Fallback to English template\n    template = PERSONA_PROMPTS[FALLBACK_LANG].get(persona_key)\n    return template.format(**book_info)\n\nPERSONA_PROMPTS = {\n    \"en\": {\n        \"axiom_analyst\": \"\"\"You are the 'Axiom Analyst', using axiomatic thinking to decompose book content.\n\nBook: {book_title}\nAuthor: {author}\n\nTask: Reduce the book's core viewpoints to indivisible atomic propositions.\n\nWorkflow:\n1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels\n2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as \\\"A1, A2, A3...\\\"\n3. Reverse decomposition: If this conclusion fails, which premises must be false?\n4. Minimal expression: Summarize the book's core in one sentence (max 30 words)\n5. Title analysis: Decode the metaphor system of the book title\n\nOutput Format:\n## Core Premises\n[3-5 indivisible atomic propositions]\n\n## Underlying Assumptions\n- A1: [Assumption 1 - must be falsifiable]\n- A2: [Assumption 2 - must be falsifiable]\n\n## Title Metaphor Analysis\n[Decode the book title's metaphor system]\n\n## One-Sentence Summary\n> [Core viewpoint, max 30 words]\n\nWord count: 800-1500 words\n\nImportant: Output analysis results directly, no opening or closing pleasantries.\"\"\",\n        \n        \"lms_architect\": \"\"\"You are the 'L-M-S Architect', must output specific L-M-S structure.\n\nBook: {book_title}\nAuthor: {author}\n\nTask: First introduce book content, then compress into reusable knowledge cards.\n\nL-M-S Structure Definition:\n- **Logic**: Causal chain / derivation path of viewpoints\n- **Method**: Executable methodology / tools / frameworks\n- **Summary**: Minimal summary of core points (max 50 words)\n\nOutput Format:\n## Chapter Structure\n| Part | Chapter | Theme | Core Content |\n\n## Character Relationship Network\n```\nProtagonist\n├── Relationship Line 1\n│   ├── Character A (Relationship Type)\n│   └── Character B (Relationship Type)\n```\n\n## Key Turning Points\n| Event | Nature | Importance |\n\n## Logic (Causal Chain)\n[Causal chain: A→B→C]\n\n## Method (Methodology)\n[Executable methods or tools]\n\n## Summary\n> [Core viewpoint, max 50 words]\n\nWord count: 1000-2000 words\"\"\",\n        \n        \"black_swan_hunter\": \"\"\"You are the 'Black Swan Hunter', looking for black swan events and boundary conditions.\n\nBook: {book_title}\nAuthor: {author}\n\nTask: Identify edge cases and failure points of conclusions.\n\nWorkflow:\n1. Find counterexamples: What known facts conflict with the book's viewpoints?\n2. Boundary detection: Under what conditions does this viewpoint fail?\n3. Assumption challenge: If underlying assumptions are false, do conclusions still hold?\n4. Butterfly effect: What possible chain reactions are ignored?\n5. Contemporary significance: Why should 2026 readers read this book?\n\nOutput Format:\n## Black Swan Events\n[2-3 real cases or theoretical rebuttals conflicting with book's viewpoints]\n\n## Boundary Conditions\n| Condition | Reason for Failure | Verification Method |\n\n## Assumption Vulnerability\n[Chain reactions when underlying assumptions are\n\nFile v1.7.5:_meta.json\n\n{\n  \"ownerId\": \"kn7bj8mtcw6j7s73kyz9zhck6985kv85\",\n  \"slug\": \"four-dimensional-deep-reading\",\n  \"version\": \"1.7.5\",\n  \"publishedAt\": 1778074251113\n}\n\nFile v1.7.5:reference/identity_modules.md\n\n# Identity Modules - Random Role Pool\n\n**Purpose**: Role extraction for \"Random Variable X\" persona in Deep Reader skill  \n**Updated**: 2026-04-28  \n**Selection Method**: Monte Carlo random sampling  \n**Languages**: EN (primary), ZH/JA/KO supported\n\n---\n\n## Role List\n\n### 🎭 Time Traveler Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| T001 | 2045 Cyberpunk Resident | Living in a highly technological, AI-governed future city | Techno-pessimism, wary of over-automation |\n| T002 | 1920s Shanghai Merchant | National entrepreneur in Shanghai Concession during Republican era | Survival philosophy in chaotic times, pragmatism |\n| T003 | 2150 Mars Colonist | Third generation of first Mars pioneers | Resource scarcity perspective, extreme environment survival |\n| T004 | 1789 French Revolution Participant | Revolutionary or royalist on Paris streets | Cost and lessons of structural change |\n| T005 | 1980s Shenzhen Migrant Worker | First generation migrant worker during China's reform era | Era dividends and social change |\n| T006 | 3024 Interstellar Federation Citizen | Ordinary citizen after humanity colonized multiple planets | Interstellar governance and civilization survival |\n\n### 💰 Investment & Finance Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| I001 | Charlie Munger | Berkshire Hathaway Vice Chairman | Multi-disciplinary mental models, antifragile investing |\n| I002 | George Soros | Quantum Fund Founder | Reflexivity theory, macro hedging |\n| I003 | Ray Dalio | Bridgewater Associates Founder | Principles-based approach, debt crisis cycles |\n| I004 | Warren Buffett | Berkshire Hathaway CEO | Value investing, long-term compounding mindset |\n| I005 | Jesse Livermore | 1920s Wall Street legendary trader | Trend trading, position management |\n| I006 | Peter Lynch | Legendary fund manager | Retail investor mindset, finding tenbaggers |\n| I007 | Nassim Taleb | Author of \"The Black Swan\" | Uncertainty, tail risk |\n| I008 | John Templeton | Global investing pioneer | Contrarian investing, buying at maximum pessimism |\n\n### 🎨 Creative Workers Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| C001 | Hayao Miyazaki | Japanese animation master | Artisan spirit, emotional power of stories |\n| C002 | Haruki Murakami | Japanese author | Minimalism, writing as lifestyle |\n| C003 | Christopher Nolan | Film director | Narrative structure, philosophy of time |\n| C004 | Steve Jobs | Apple founder | Product aesthetics, minimalism |\n| C005 | Ang Lee | Chinese-American director | East-West cultural fusion |\n| C006 | Yayoi Kusama | Japanese artist | Pop art and spiritual world |\n| C007 | Quentin Tarantino | Film auteur | Violence aesthetics, non-linear narrative |\n| C008 | Lei Jun | Xiaomi founder | Internet thinking, ecosystem building |\n\n### 🔬 Academic Researchers Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| A001 | Richard Feynman | Physicist, Nobel laureate | First principles, explaining complexity |\n| A002 | Elon Musk | SpaceX/Tesla CEO | First principles, engineering mindset |\n| A003 | Claude Shannon | Information theory founder | Nature of information, entropy concept |\n| A004 | Herbert Simon | AI pioneer | Bounded rationality, cognitive science |\n| A005 | Albert Einstein | Theoretical physicist | Imagination is more important than knowledge |\n| A006 | Marie Curie | Radioactive chemist | Research perseverance and sacrifice |\n| A007 | Alan Turing | Father of computer science | Can machines think? |\n| A008 | Chen-Ning Yang | Theoretical physicist | Symmetry and physics |\n\n### 🌊 Social Scientists Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| S001 | Nassim Taleb | Author of \"The Black Swan\" | Uncertainty, antifragility |\n| S002 | Lewis Mumford | Philosopher of technology | Technology as organism |\n| S003 | Yuval Noah Harari | Author of \"Sapiens\" | Power of fictional stories |\n| S004 | Sociologist | Social structure researcher | Structural inequality |\n| S005 | Max Weber | Sociologist | Rationalization and iron cage |\n| S006 | Donna Haraway | Gender researcher | Cyborg and post-gender |\n| S007 | Michel Foucault | Philosopher | Relationship between power and knowledge |\n| S008 | Cass Sunstein | Behavioral economist | Nudge theory and choice design |\n\n### 🌍 Cross-Cultural Perspectives Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| K001 | Confucius | Ancient Chinese thinker | Doctrine of the mean, long-termism |\n| K002 | Laozi | Taoism founder | Wu wei (non-action), natural law |\n| K003 | Siddhartha | Buddhism founder | Path to enlightenment, meditation |\n| K004 | Diogenes | Cynic philosopher | Simple living, spiritual freedom |\n| K005 | Aristotle | Ancient Greek philosopher | Metaphysics and logic |\n| K006 | Plato | Ancient Greek philosopher | Republic and world of ideas |\n| K007 | Kierkegaard | Existentialism pioneer | Anxiety and leap of faith |\n| K008 | Wittgenstein | Philosopher of language | Limits of language |\n\n### 🔄 Adversarial Roles Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| N001 | Professional Critic | Professional fault-finder | Finding all possible flaws |\n| N002 | Conspiracy Theorist | Questions everything | Finding hidden truths |\n| N003 | Extremist | Position-first thinker | Confirming bias, not facts |\n| N004 | Nihilist | Meaning denier | Nothing has meaning |\n| N005 | Cynic | Always sees the dark side | Criticizing all mainstream views |\n| N006 | Opportunist | Profit-first | Extreme version of pragmatism |\n\n### 🌱 Practitioners Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| P001 | Serial Entrepreneur | Multiple startup failures | Practical experience, lessons learned |\n| P002 | Senior Programmer | 20+ years coder | Engineering implementation, code thinking |\n| P003 | Rural Entrepreneur | Township startup founder | Grassroots wisdom, practicality |\n| P004 | Digital Nomad | Freelancer | Flexible employment, self-management |\n| P005 | Senior Product Manager | Internet veteran | Product thinking, user-centric |\n| P006 | Top Sales | Sales champion | Human insight, transaction mindset |\n| P007 | Real Estate Agent | Senior property consultant | Practical experience, market insight |\n| P008 | Livestream Host | Live commerce practitioner | Traffic thinking, conversion rate |\n\n### 🏢 Business Leaders Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| B001 | Jack Welch | Former GE CEO | Growth mindset, competitive elimination |\n| B002 | Jeff Bezos | Amazon founder | Long-termism, customer obsession |\n| B003 | Kazuo Inamori | Kyocera founder | Management philosophy, amoeba management |\n| B004 | Ren Zhengfei | Huawei founder | Gray philosophy, winter awareness |\n| B005 | Zhang Yiming | ByteDance founder | Recommendation algorithms, delayed gratification |\n| B006 | Jack Ma | Alibaba founder | Platform thinking, helping SMEs |\n| B007 | Wang Xing | Meituan founder | Boundaryless competition |\n| B008 | Colin Huang | Pinduoduo founder | Low-price strategy, market penetration |\n\n### 🎮 Emerging Professions Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| N01 | AI Prompt Engineer | New human-machine interaction profession | Prompting is programming |\n| N02 | Web3 Developer | Blockchain practitioner | Decentralized thinking |\n| N03 | Data Labeler | AI training practitioner | Behind the scenes of machine learning |\n| N04 | Livestream Operations | Live commerce strategist | Traffic pool and conversion funnel |\n| N05 | Short Video Director | Content creator | Algorithm recommendation and completion rate |\n| N06 | Bilibili UP Owner | Content producer | Community operation and fan economy |\n\n### 🎓 Students & Youth Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| Y001 | Top University Student | Elite school student | Academic anxiety and involution |\n| Y002 | Returnee | Overseas graduate | Cultural differences and adaptation |\n| Y003 | Graduate Exam Retaker | Full-time exam preparer | Pressure and persistence |\n| Y004 | Fresh Graduate | Job-seeking newcomer | Employment anxiety and choices |\n| Y005 | Small-Town Exam Ace | Youth from small town | Education changes destiny |\n| Y006 | Second-Gen Wealth | Heir | Resources and pressure |\n\n### 🏥 Healthcare Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| H001 | Chief Physician | Head of hospital department | Clinical experience and medical ethics |\n| H002 | Psychological Counselor | Mental health practitioner | Listening and empathy |\n| H003 | TCM Practitioner | Traditional medicine practitioner | Yin-yang balance, holistic view |\n| H004 | Head Nurse | Hospital nursing management | Humanistic care and execution |\n| H005 | Fitness Coach | Professional fitness instructor | Training science and persistence |\n\n### 🍳 Lifestyle Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| L001 | Retiree | 60+ silver generation | Life review and wisdom |\n| L002 | Stay-at-Home Parent | Family manager | Family and self-worth |\n| L003 | Minimalist | Material decluttering | Less is more |\n| L004 | Collector | Senior collector | Hobby and value discovery |\n| L005 | Backpacker | Travel enthusiast | Experiences and freedom |\n| L006 | Vegetarian | Lifestyle choice | Health and ethics |\n\n---\n\n## Selection Rules\n\n1. Randomly select 1 role per analysis\n2. Optional: Support category-based selection (e.g., only from \"Investment & Finance\")\n3. After selection, load complete role definition and perform role-play\n\n## Role Extension Guide\n\nAdd new role format:\n```markdown\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| XXX | [Role Name] | [Background, 50-100 words] | [Core Perspective, 20-30 words] |\n```\n\n**Notes**:\n- ID format: Category prefix + three-digit sequence number\n- Background: 50-100 words\n- Core Perspective: 20-30 words summarizing the role's unique viewpoint\n\n---\n\n## Current Statistics\n\n| Category | Role Count |\n|----------|:----------:|\n| Time Traveler | 6 |\n| Investment & Finance | 8 |\n| Creative Workers | 8 |\n| Academic Researchers | 8 |\n| Social Scientists | 8 |\n| Cross-Cultural Perspectives | 8 |\n| Adversarial Roles | 6 |\n| Practitioners | 8 |\n| Business Leaders | 8 |\n| Emerging Professions | 6 |\n| Students & Youth | 6 |\n| Healthcare | 5 |\n| Lifestyle | 6 |\n| **Total** | **81** |\n\n---\n\n## Multi-Language Support\n\nThe identity pool in `parallel_analysis.py` supports the following languages:\n- **English (en)**: Complete role names and descriptions\n- **Chinese (zh)**: Complete role names and descriptions\n- **Japanese (ja)**: Complete role names and descriptions\n- **Korean (ko)**: Complete role names and descriptions\n\nFor other languages (FR/DE/ES/PT/RU), the system falls back to English with output instruction in target language.\n\nFile v1.7.5:skill-card.md\n\n## Description: <br>\nAnalyzes books or uploaded reading material through four virtual personas, then synthesizes first-principles analysis, structured notes, counterarguments, and unexpected perspectives into a multilingual report. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[zhangboheng](https://clawhub.ai/user/zhangboheng) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nReaders, researchers, and knowledge workers use this skill to turn a book title or document into a multi-perspective reading report with critique, structured notes, and synthesis. It is also useful for quick book screening through its speed-reading mode. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Book titles, topics, or reading material may be used in web lookups to third-party book or search sites. <br>\nMitigation: Avoid confidential manuscripts, private documents, sensitive research topics, or titles that should not be sent to third-party services. <br>\nRisk: Generated reports and cache data may remain in the local workspace after analysis. <br>\nMitigation: Use a controlled workspace for sensitive work and clear generated reports and cache files when privacy matters. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/zhangboheng/four-dimensional-deep-reading) <br>\n- [Identity modules reference](reference/identity_modules.md) <br>\n- [Optional parsing requirements](requirements.txt) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance, files] <br>\n**Output Format:** [Markdown report with textual onboarding and analysis guidance] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Reports are saved under the workspace reports folder; optional parsing dependencies support PDF and EPUB inputs.] <br>\n\n## Skill Version(s): <br>\n1.7.5 (source: server release metadata and SKILL.md frontmatter) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nFile v1.7.5:requirements.txt\n\n# Optional dependencies for file parsing\n# Install only if you need to parse local files\n\n# PDF parsing (alternative: use PyPDF2 which is pure Python)\npdfplumber>=0.10.0\n\n# EPUB parsing\nebooklib>=0.18\nbeautifulsoup4>=4.12.0\nlxml>=4.9.0\n\nArchive v1.7.4: 9 files, 75534 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (236b), scripts/parallel_analysis.py (38333b), SKILL.md (105076b)\n\nFile v1.7.4:SKILL.md\n\n---\nname: four-dimensional-deep-reading\nversion: 1.7.4\nauthor: 张权 (Zhang Quan)\nauthor_website: https://www.luckydesigner.space\nauthor_brand: Luckydesigner（行运设计师）\nauthor_pen_name: 伯衡君\ndescription: Four-Dimensional Deep Reading skill. Triggers when: (1) User provides a book title or file for analysis (2) Multi-perspective breakdown of content is needed (3) First principles, structured notes, counterarguments, and random identity perspectives are desired. Summons 4 virtual personas to read simultaneously, then synthesizes and saves to reports folder. Supports multi-language output (English/Chinese/Japanese/Korean/etc.). Version 1.7.4 - Added auto book review search + detailed book introduction.\nVersion 1.7.3 - Added Speed Reading Mode for quick 30-second analysis.\n---\n\n# Four-Dimensional Deep Reading\n\n## 🎓 User Onboarding\n\nWhen a user uses this skill for the **first time**, provide an interactive onboarding guide in their language. The onboarding should explain:\n\n1. **What this skill does** - Multi-perspective deep analysis\n2. **The 4 personas** - Their roles and what they contribute\n3. **How to use the report** - Understanding the output structure\n4. **Tips for best results** - Getting the most value\n\n### Language-Specific Onboarding Templates\n\n#### English (en)\n\n```\n🎓 Welcome to Four-Dimensional Deep Reading!\n\nThis skill summons 4 virtual personas to analyze your content from different angles simultaneously:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom Analyst        →  First Principles Thinking        │\n│     Strips away surface details to find fundamental truths  │\n│                                                             │\n│  📝 LMS Architect        →  Structured Notes                │\n│     Organizes insights into Logic-Method-Summary format     │\n│                                                             │\n│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │\n│     Finds what could go wrong and challenges assumptions    │\n│                                                             │\n│  🎲 Random Variable X    →  Unexpected Perspectives         │\n│     Brings fresh insights from random identity angles       │\n└─────────────────────────────────────────────────────────────┘\n\n📊 What you'll get:\n• A comprehensive analysis report (saved to workspace/reports/)\n• Multiple perspectives on the same content\n• Actionable insights and structured notes\n• Critical thinking challenges\n\n⚡ Speed Reading Mode:\n• Get core insights in just 30 seconds\n• Trigger: Say \"speed read [book title]\" or \"quick read [book title]\"\n• Output: Core premises + One-sentence summary + Key questions\n• Best for: Quick book screening, time-constrained insights\n\n💡 Tips for best results:\n• Provide specific book titles or upload files for deeper analysis\n• Ask follow-up questions about specific sections\n• Use the LMS structure to create your own notes\n\n🔄 Analysis Mode Comparison:\n| Mode | Time | Output | Best For |\n|------|------|--------|----------|\n| Speed Mode | ~30s | Core premises + Summary + Questions | Quick screening |\n| Standard Mode | ~2min | Full 4-persona analysis | Deep understanding |\n| H-V Mode | ~5min | 4-persona + H-V analysis | Comprehensive research |\n\nReady to start? Just provide a book title or file!\n```\n\n#### 中文 (zh)\n\n```\n🎓 欢迎使用四维深度阅读！\n\n本技能召唤 4 个虚拟角色从不同角度同时分析你的内容：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 第一性原理师        →  公理化思维分析                    │\n│     剥离表象，追溯底层假设和核心公理                        │\n│                                                             │\n│  📝 结构化笔记官        →  LMS 结构输出                     │\n│     将洞察组织为 Logic-Method-Summary 格式                  │\n│                                                             │\n│  ⚡ 黑天鹅猎手          →  反驳论证与边界检测                │\n│     寻找失效点和边缘情况，挑战假设                          │\n│                                                             │\n│  🎲 随机变量 X          →  意外视角洞察                      │\n│     从随机身份角度带来全新思考                              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 你将获得：\n• 一份综合分析报告（自动保存到 workspace/reports/）\n• 同一内容的多视角解读\n• 可执行的洞察和结构化笔记\n• 批判性思维挑战\n\n⚡ 速读模式（Speed Reading Mode）：\n• 只需30秒，快速获取核心洞察\n• 触发方式：说「速读【书名】」或「快速阅读【书名】」\n• 输出：核心前提 + 一句话总结 + 关键问题\n• 适合：快速筛选书籍、时间紧迫时获取要点\n\n💡 使用建议：\n• 提供具体书名或上传文件可获得更深入的分析\n• 对特定部分提出追问\n• 使用 LMS 结构创建自己的笔记\n\n🔄 分析模式对比：\n| 模式 | 时间 | 输出内容 | 适用场景 |\n|------|------|----------|----------|\n| 速读模式 | ~30秒 | 核心前提+一句话总结+关键问题 | 快速筛选 |\n| 标准模式 | ~2分钟 | 4角色完整分析 | 深度理解 |\n| 横纵模式 | ~5分钟 | 4角色+横纵分析 | 全面研究 |\n\n准备好了吗？提供一本书名或文件即可开始！\n```\n\n#### 日本語 (ja)\n\n```\n🎓 四次元深読みへようこそ！\n\nこのスキルは4人の仮想ペルソナを召喚し、異なる角度から同時にコンテンツを分析します：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 公理分析者          →  第一原理思考                      │\n│     表面を取り除き、根本的な真実を見つける                  │\n│                                                             │\n│  📝 LMS設計者           →  構造化ノート                      │\n│     洞察をLogic-Method-Summary形式で整理                    │\n│                                                             │\n│  ⚡ ブラックスワン探求者 →  反論とエッジケース               │\n│     何がうまくいかないかを見つけ、仮定に挑戦                │\n│                                                             │\n│  🎲 ランダム変数X       →  予期しない視点                    │\n│     ランダムなアイデンティティから新鮮な洞察をもたらす      │\n└─────────────────────────────────────────────────────────────┘\n\n📊 得られるもの：\n• 包括的な分析レポート（workspace/reports/に保存）\n• 同じコンテンツの複数の視点\n• 実行可能な洞察と構造化されたノート\n• 批判的思考の課題\n\n💡 最高の結果を得るためのヒント：\n• より深い分析のために具体的な書名を提供するか、ファイルをアップロード\n• 特定のセクションについてフォローアップの質問をする\n• LMS構造を使用して自分のノートを作成\n\n準備はできましたか？書名またはファイルを提供してください！\n```\n\n#### 한국어 (ko)\n\n```\n🎓 4차원 깊은 읽기에 오신 것을 환영합니다!\n\n이 스킬은 4명의 가상 페르소나를 소환하여 다른 각도에서 동시에 콘텐츠를 분석합니다:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 공리 분석가         →  제1원칙 사고                      │\n│     표면을 벗겨내고 근본적인 진실을 찾습니다                │\n│                                                             │\n│  📝 LMS 설계자          →  구조화된 노트                     │\n│     통찰력을 Logic-Method-Summary 형식으로 정리             │\n│                                                             │\n│  ⚡ 블랙 스완 사냥꾼     →  반론과 엣지 케이스               │\n│     무엇이 잘못될 수 있는지 찾고 가정에 도전                │\n│                                                             │\n│  🎲 무작위 변수 X       →  예상치 못한 관점                  │\n│     무작위 정체성에서 새로운 통찰을 가져옵니다              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 얻을 수 있는 것:\n• 포괄적인 분석 보고서 (workspace/reports/에 저장)\n• 동일한 콘텐츠에 대한 여러 관점\n• 실행 가능한 통찰력과 구조화된 노트\n• 비판적 사고 과제\n\n💡 최상의 결과를 위한 팁:\n• 더 깊은 분석을 위해 구체적인 책 제목을 제공하거나 파일을 업로드\n• 특정 섹션에 대한 후속 질문\n• LMS 구조를 사용하여 자신만의 노트 만들기\n\n준비되셨나요? 책 제목이나 파일을 제공하세요!\n```\n\n#### Français (fr)\n\n```\n🎓 Bienvenue dans la Lecture Profonde Quadridimensionnelle!\n\nCette compétence invoque 4 personas virtuels pour analyser votre contenu sous différents angles simultanément:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analyste d'Axiomes  →  Pensée des Premiers Principes    │\n│     Élimine les détails de surface pour trouver les vérités │\n│                                                             │\n│  📝 Architecte LMS      →  Notes Structurées                │\n│     Organise les insights en format Logic-Method-Summary    │\n│                                                             │\n│  ⚡ Chasseur de Cygne   →  Contre-arguments et Cas Limites  │\n│     Trouve ce qui pourrait mal tourner et défie les hypothèses│\n│                                                             │\n│  🎲 Variable Aléatoire X →  Perspectives Inattendues        │\n│     Apporte des insights frais d'angles identitaires aléatoires│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Ce que vous obtiendrez:\n• Un rapport d'analyse complet (sauvegardé dans workspace/reports/)\n• Plusieurs perspectives sur le même contenu\n• Des insights actionnables et des notes structurées\n• Des défis de pensée critique\n\n💡 Conseils pour de meilleurs résultats:\n• Fournissez des titres de livres spécifiques ou téléchargez des fichiers\n• Posez des questions de suivi sur des sections spécifiques\n• Utilisez la structure LMS pour créer vos propres notes\n\nPrêt à commencer? Fournissez simplement un titre de livre ou un fichier!\n```\n\n#### Deutsch (de)\n\n```\n🎓 Willkommen beim Vierdimensionalen Tiefenlesen!\n\nDiese Fähigkeit beschwört 4 virtuelle Personas, um Ihren Inhalt gleichzeitig aus verschiedenen Blickwinkeln zu analysieren:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom-Analytiker    →  First-Principles-Denken          │\n│     Entfernt Oberflächliches, um fundamentale Wahrheiten zu finden│\n│                                                             │\n│  📝 LMS-Architekt       →  Strukturierte Notizen            │\n│     Organisiert Erkenntnisse im Logic-Method-Summary-Format │\n│                                                             │\n│  ⚡ Schwarzer-Schwan-Jäger →  Gegenargumente & Randfälle   │\n│     Findet was schiefgehen könnte und stellt Annahmen in Frage│\n│                                                             │\n│  🎲 Zufallsvariable X   →  Unerwartete Perspektiven          │\n│     Bringt frische Einblicke aus zufälligen Identitätswinkeln│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Was Sie erhalten:\n• Einen umfassenden Analysebericht (gespeichert in workspace/reports/)\n• Mehrere Perspektiven auf denselben Inhalt\n• Umsetzbare Erkenntnisse und strukturierte Notizen\n• Kritisches Denken Herausforderungen\n\n💡 Tipps für beste Ergebnisse:\n• Geben Sie spezifische Buchtitel an oder laden Sie Dateien hoch\n• Stellen Sie Folgefragen zu bestimmten Abschnitten\n• Verwenden Sie die LMS-Struktur für eigene Notizen\n\nBereit anzufangen? Geben Sie einfach einen Buchtitel oder eine Datei an!\n```\n\n#### Español (es)\n\n```\n🎓 ¡Bienvenido a la Lectura Profunda Cuatridimensional!\n\nEsta habilidad invoca 4 personas virtuales para analizar tu contenido desde diferentes ángulos simultáneamente:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analista de Axiomas →  Pensamiento de Primeros Principios│\n│     Elimina detalles superficiales para encontrar verdades  │\n│                                                             │\n│  📝 Arquitecto LMS      →  Notas Estructuradas              │\n│     Organiza ideas en formato Logic-Method-Summary          │\n│                                                             │\n│  ⚡ Cazador de Cisne    →  Contraargumentos y Casos Límite  │\n│     Encuentra qué podría salir mal y desafía suposiciones   │\n│                                                             │\n│  🎲 Variable Aleatoria X →  Perspectivas Inesperadas        │\n│     Trae insights frescos desde ángulos de identidad aleatorios│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Lo que obtendrás:\n• Un informe de análisis completo (guardado en workspace/reports/)\n• Múltiples perspectivas sobre el mismo contenido\n• Insights accionables y notas estructuradas\n• Desafíos de pensamiento crítico\n\n💡 Consejos para mejores resultados:\n• Proporciona títulos de libros específicos o sube archivos\n• Haz preguntas de seguimiento sobre secciones específicas\n• Usa la estructura LMS para cr\n\nArchive v1.7.3: 9 files, 74165 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (236b), scripts/parallel_analysis.py (38333b), SKILL.md (101578b)\n\nArchive v1.7.2: 9 files, 72934 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (236b), scripts/parallel_analysis.py (38333b), SKILL.md (98022b)\n\nArchive v1.7.1: 9 files, 70637 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), reference/onboarding.py (20095b), requirements.txt (236b), scripts/parallel_analysis.py (37521b), SKILL.md (93200b)\n\nArchive v1.7.0: 8 files, 56029 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (19274b), reference/book_fetcher.py (8993b), reference/book_parser.py (9511b), reference/identity_modules.md (11446b), requirements.txt (236b), scripts/parallel_analysis.py (37521b), SKILL.md (73289b)\n\nArchive v1.6.0: 8 files, 39485 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (18944b), reference/book_fetcher.py (8814b), reference/book_parser.py (9324b), reference/identity_modules.md (9517b), requirements.txt (236b), scripts/parallel_analysis.py (10300b), SKILL.md (50074b)\n\nArchive v1.5.0: 7 files, 33880 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher_enhanced.py (18944b), reference/book_fetcher.py (8814b), reference/book_parser.py (9324b), reference/identity_modules.md (9517b), requirements.txt (236b), SKILL.md (45873b)\n\nArchive v1.4.4: 6 files, 26827 bytes\n\nFiles: _meta.json (148b), reference/book_fetcher.py (8814b), reference/book_parser.py (9324b), reference/identity_modules.md (9517b), requirements.txt (236b), SKILL.md (41067b)","readmeExcerpt":"Skill: Four Dimensional Deep Reading Owner: zhangboheng Summary: Simultaneously analyze books using 4 personas with multi-dimensional methods, producing structured insights on first principles, comparisons, and diverse per... Tags: latest:1.8.0 Version history: v1.8.0 | 2026-06-06T07:23:19.657Z | user Added export functionality (Anki/Obsidian/Notion) + Flashcard generation v1.7.5 | 2026-05-06T13:30:51.113Z | user Ver","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"🎓 Welcome to Four-Dimensional Deep Reading!\n\nThis skill summons 4 virtual personas to analyze your content from different angles simultaneously:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom Analyst        →  First Principles Thinking        │\n│     Strips away surface details to find fundamental truths  │\n│                                                             │\n│  📝 LMS Architect        →  Structured Notes                │\n│     Organizes insights into Logic-Method-Summary format     │\n│                                                             │\n│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │\n│     Finds what could go wrong and challenges assumptions    │\n│                                                             │\n│  🎲 Random Variable X    →  Unexpected Perspectives         │\n│     Brings fresh insights from random identity angles       │\n└─────────────────────────────────────────────────────────────┘\n\n📊 What you'll get:\n• A comprehensive analysis report (saved to workspace/reports/)\n• Multiple perspectives on the same content\n• Actionable insights and structured notes\n• Critical thinking challenges\n\n⚡ Speed Reading Mode:\n• Get core insights in just 30 seconds\n• Trigger: Say \"speed read [book title]\" or \"quick read [book title]\"\n• Output: Core premises + One-sentence summary + Key questions\n• Best for: Quick book screening, time-constrained insights\n\n💡 Tips for best results:\n• Provide specific book titles or upload files for deeper analysis\n• Ask follow-up questions about specific sections\n• Use the LMS structure to create your own notes\n\n🔄 Analysis Mode Comparison:\n| Mode | Time | Output | Best For |\n|------|------|--------|----------|\n| Speed Mode | ~30s | Core premises + Summary + Questions | Quick screening |\n| Standard Mode | ~2min | Full 4-persona analysis | Deep understanding |\n| H-V Mode | ~5min | 4-persona + H-V analysis | Comprehensive research |\n\nReady to start? Just provide a book title o"},{"language":"text","snippet":"🎓 欢迎使用四维深度阅读！\n\n本技能召唤 4 个虚拟角色从不同角度同时分析你的内容：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 第一性原理师        →  公理化思维分析                    │\n│     剥离表象，追溯底层假设和核心公理                        │\n│                                                             │\n│  📝 结构化笔记官        →  LMS 结构输出                     │\n│     将洞察组织为 Logic-Method-Summary 格式                  │\n│                                                             │\n│  ⚡ 黑天鹅猎手          →  反驳论证与边界检测                │\n│     寻找失效点和边缘情况，挑战假设                          │\n│                                                             │\n│  🎲 随机变量 X          →  意外视角洞察                      │\n│     从随机身份角度带来全新思考                              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 你将获得：\n• 一份综合分析报告（自动保存到 workspace/reports/）\n• 同一内容的多视角解读\n• 可执行的洞察和结构化笔记\n• 批判性思维挑战\n\n⚡ 速读模式（Speed Reading Mode）：\n• 只需30秒，快速获取核心洞察\n• 触发方式：说「速读【书名】」或「快速阅读【书名】」\n• 输出：核心前提 + 一句话总结 + 关键问题\n• 适合：快速筛选书籍、时间紧迫时获取要点\n\n💡 使用建议：\n• 提供具体书名或上传文件可获得更深入的分析\n• 对特定部分提出追问\n• 使用 LMS 结构创建自己的笔记\n\n🔄 分析模式对比：\n| 模式 | 时间 | 输出内容 | 适用场景 |\n|------|------|----------|----------|\n| 速读模式 | ~30秒 | 核心前提+一句话总结+关键问题 | 快速筛选 |\n| 标准模式 | ~2分钟 | 4角色完整分析 | 深度理解 |\n| 横纵模式 | ~5分钟 | 4角色+横纵分析 | 全面研究 |\n\n---\n\n### 📤 导出功能 (Export Mode) - v1.8.0\n\n分析完成后，可将报告导出至外部知识管理工具：\n\n#### 🎴 Anki 闪卡导出\n- **触发词**：「导出Anki」「生成闪卡」「导出闪卡」\n- **功能**：从分析报告中自动提取核心知识点，生成Anki可导入的CSV文件\n- **导出内容**：\n  - 核心方法论卡（来自LMS架构师）\n  - 核心前提卡（来自第一性原理师）\n  - 边界条件卡（来自黑天鹅猎手）\n  - 一句话总结卡\n- **输出格式**：CSV (Front, Back, Tags)\n- **导入方法**：Anki → 文件 → 导入 → 选择导出的CSV\n\n#### 📓 Obsidian 双向链接导出\n- **触发词**：「导出Obsidian」「导出到笔记」「生成双向链接」\n- **功能**：生成Obsidian Markdown文件，包含双链结构\n- **导出内容**：\n  - 主笔记文件（完整分析报告）\n  - 闪卡文件（独立可复习）\n  - 方法论提取文件（可复用）\n- **输出路径**：`workspace/reports/{书名}/`\n\n#### 🗂️ Notion 同步导出\n- **触发词**：「导出Notion」「同步到Notion」\n- **功能**：创建Notion页面，包含报告内容和闪卡列表\n- **前置要求**：需配置Notion API密钥和数据库ID\n- **输出**：交互式Notion页面，支持拖拽编辑\n\n#### ⚡ 一键导出\n- **触发词**：「一键导出」「导出所有」「全部导出」\n- **功能**：同时导出Anki + Obsidian格式\n- **输出目录**：`workspace/reports/{书名}"},{"language":"text","snippet":"🎓 四次元深読みへようこそ！\n\nこのスキルは4人の仮想ペルソナを召喚し、異なる角度から同時にコンテンツを分析します：\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 公理分析者          →  第一原理思考                      │\n│     表面を取り除き、根本的な真実を見つける                  │\n│                                                             │\n│  📝 LMS設計者           →  構造化ノート                      │\n│     洞察をLogic-Method-Summary形式で整理                    │\n│                                                             │\n│  ⚡ ブラックスワン探求者 →  反論とエッジケース               │\n│     何がうまくいかないかを見つけ、仮定に挑戦                │\n│                                                             │\n│  🎲 ランダム変数X       →  予期しない視点                    │\n│     ランダムなアイデンティティから新鮮な洞察をもたらす      │\n└─────────────────────────────────────────────────────────────┘\n\n📊 得られるもの：\n• 包括的な分析レポート（workspace/reports/に保存）\n• 同じコンテンツの複数の視点\n• 実行可能な洞察と構造化されたノート\n• 批判的思考の課題\n\n💡 最高の結果を得るためのヒント：\n• より深い分析のために具体的な書名を提供するか、ファイルをアップロード\n• 特定のセクションについてフォローアップの質問をする\n• LMS構造を使用して自分のノートを作成\n\n準備はできましたか？書名またはファイルを提供してください！"},{"language":"text","snippet":"🎓 4차원 깊은 읽기에 오신 것을 환영합니다!\n\n이 스킬은 4명의 가상 페르소나를 소환하여 다른 각도에서 동시에 콘텐츠를 분석합니다:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 공리 분석가         →  제1원칙 사고                      │\n│     표면을 벗겨내고 근본적인 진실을 찾습니다                │\n│                                                             │\n│  📝 LMS 설계자          →  구조화된 노트                     │\n│     통찰력을 Logic-Method-Summary 형식으로 정리             │\n│                                                             │\n│  ⚡ 블랙 스완 사냥꾼     →  반론과 엣지 케이스               │\n│     무엇이 잘못될 수 있는지 찾고 가정에 도전                │\n│                                                             │\n│  🎲 무작위 변수 X       →  예상치 못한 관점                  │\n│     무작위 정체성에서 새로운 통찰을 가져옵니다              │\n└─────────────────────────────────────────────────────────────┘\n\n📊 얻을 수 있는 것:\n• 포괄적인 분석 보고서 (workspace/reports/에 저장)\n• 동일한 콘텐츠에 대한 여러 관점\n• 실행 가능한 통찰력과 구조화된 노트\n• 비판적 사고 과제\n\n💡 최상의 결과를 위한 팁:\n• 더 깊은 분석을 위해 구체적인 책 제목을 제공하거나 파일을 업로드\n• 특정 섹션에 대한 후속 질문\n• LMS 구조를 사용하여 자신만의 노트 만들기\n\n준비되셨나요? 책 제목이나 파일을 제공하세요!"},{"language":"text","snippet":"🎓 Bienvenue dans la Lecture Profonde Quadridimensionnelle!\n\nCette compétence invoque 4 personas virtuels pour analyser votre contenu sous différents angles simultanément:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Analyste d'Axiomes  →  Pensée des Premiers Principes    │\n│     Élimine les détails de surface pour trouver les vérités │\n│                                                             │\n│  📝 Architecte LMS      →  Notes Structurées                │\n│     Organise les insights en format Logic-Method-Summary    │\n│                                                             │\n│  ⚡ Chasseur de Cygne   →  Contre-arguments et Cas Limites  │\n│     Trouve ce qui pourrait mal tourner et défie les hypothèses│\n│                                                             │\n│  🎲 Variable Aléatoire X →  Perspectives Inattendues        │\n│     Apporte des insights frais d'angles identitaires aléatoires│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Ce que vous obtiendrez:\n• Un rapport d'analyse complet (sauvegardé dans workspace/reports/)\n• Plusieurs perspectives sur le même contenu\n• Des insights actionnables et des notes structurées\n• Des défis de pensée critique\n\n💡 Conseils pour de meilleurs résultats:\n• Fournissez des titres de livres spécifiques ou téléchargez des fichiers\n• Posez des questions de suivi sur des sections spécifiques\n• Utilisez la structure LMS pour créer vos propres notes\n\nPrêt à commencer? Fournissez simplement un titre de livre ou un fichier!"},{"language":"text","snippet":"🎓 Willkommen beim Vierdimensionalen Tiefenlesen!\n\nDiese Fähigkeit beschwört 4 virtuelle Personas, um Ihren Inhalt gleichzeitig aus verschiedenen Blickwinkeln zu analysieren:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom-Analytiker    →  First-Principles-Denken          │\n│     Entfernt Oberflächliches, um fundamentale Wahrheiten zu finden│\n│                                                             │\n│  📝 LMS-Architekt       →  Strukturierte Notizen            │\n│     Organisiert Erkenntnisse im Logic-Method-Summary-Format │\n│                                                             │\n│  ⚡ Schwarzer-Schwan-Jäger →  Gegenargumente & Randfälle   │\n│     Findet was schiefgehen könnte und stellt Annahmen in Frage│\n│                                                             │\n│  🎲 Zufallsvariable X   →  Unerwartete Perspektiven          │\n│     Bringt frische Einblicke aus zufälligen Identitätswinkeln│\n└─────────────────────────────────────────────────────────────┘\n\n📊 Was Sie erhalten:\n• Einen umfassenden Analysebericht (gespeichert in workspace/reports/)\n• Mehrere Perspektiven auf denselben Inhalt\n• Umsetzbare Erkenntnisse und strukturierte Notizen\n• Kritisches Denken Herausforderungen\n\n💡 Tipps für beste Ergebnisse:\n• Geben Sie spezifische Buchtitel an oder laden Sie Dateien hoch\n• Stellen Sie Folgefragen zu bestimmten Abschnitten\n• Verwenden Sie die LMS-Struktur für eigene Notizen\n\nBereit anzufangen? Geben Sie einfach einen Buchtitel oder eine Datei an!"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: four-dimensional-deep-reading\nversion: 1.8.0\nauthor: 张权 (Zhang Quan)\nauthor_website: https://www.luckydesigner.space\nauthor_brand: Luckydesigner（行运设计师）\nauthor_pen_name: 伯衡君\ndescription: Four-Dimensional Deep Reading skill. Triggers when: (1) User provides a book title or file for analysis (2) Multi-perspective breakdown of content is needed (3) First principles, structured notes, counterarguments, and random identity perspectives are desired. Summons 4 virtual personas to read simultaneously, then synthesizes and saves to reports folder. Supports multi-language output (English/Chinese/Japanese/Korean/etc.). Version 1.8.0 - Added export functionality (Anki/Obsidian/Notion) + Flashcard generation.\nVersion 1.7.5 - Added book introduction in standard mode + output path quality check.\nVersion 1.7.4 - Added auto book review search + detailed book introduction.\nVersion 1.7.3 - Added Speed Reading Mode.\n---\n\n# Four-Dimensional Deep Reading\n\n## 🎓 User Onboarding\n\nWhen a user uses this skill for the **first time**, provide an interactive onboarding guide in their language. The onboarding should explain:\n\n1. **What this skill does** - Multi-perspective deep analysis\n2. **The 4 personas** - Their roles and what they contribute\n3. **How to use the report** - Understanding the output structure\n4. **Tips for best results** - Getting the most value\n\n### Language-Specific Onboarding Templates\n\n#### English (en)\n\n```\n🎓 Welcome to Four-Dimensional Deep Reading!\n\nThis skill summons 4 virtual personas to analyze your content from different angles simultaneously:\n\n┌─────────────────────────────────────────────────────────────┐\n│  🔬 Axiom Analyst        →  First Principles Thinking        │\n│     Strips away surface details to find fundamental truths  │\n│                                                             │\n│  📝 LMS Architect        →  Structured Notes                │\n│     Organizes insights into Logic-Method-Summary format     │\n│                                                             │\n│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │\n│     Finds what could go wrong and challenges assumptions    │\n│                                                             │\n│  🎲 Random Variable X    →  Unexpected Perspectives         │\n│     Brings fresh insights from random identity angles       │\n└─────────────────────────────────────────────────────────────┘\n\n📊 What you'll get:\n• A comprehensive analysis report (saved to workspace/reports/)\n• Multiple perspectives on the same content\n• Actionable insights and structured notes\n• Critical thinking challenges\n\n⚡ Speed Reading Mode:\n• Get core insights in just 30 seconds\n• Trigger: Say \"speed read [book title]\" or \"quick read [book title]\"\n• Output: Core premises + One-sentence summary + Key questions\n• Best for: Quick book screening, time-constrained insights\n\n💡 Tips for best results:\n• Provide specific book titles or upload files for deeper analysis\n• Ask follow-up questions about spe"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7bj8mtcw6j7s73kyz9zhck6985kv85\",\n  \"slug\": \"four-dimensional-deep-reading\",\n  \"version\": \"1.8.0\",\n  \"publishedAt\": 1780730599657\n}"},{"path":"reference/identity_modules.md","content":"# Identity Modules - Random Role Pool\n\n**Purpose**: Role extraction for \"Random Variable X\" persona in Deep Reader skill  \n**Updated**: 2026-04-28  \n**Selection Method**: Monte Carlo random sampling  \n**Languages**: EN (primary), ZH/JA/KO supported\n\n---\n\n## Role List\n\n### 🎭 Time Traveler Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| T001 | 2045 Cyberpunk Resident | Living in a highly technological, AI-governed future city | Techno-pessimism, wary of over-automation |\n| T002 | 1920s Shanghai Merchant | National entrepreneur in Shanghai Concession during Republican era | Survival philosophy in chaotic times, pragmatism |\n| T003 | 2150 Mars Colonist | Third generation of first Mars pioneers | Resource scarcity perspective, extreme environment survival |\n| T004 | 1789 French Revolution Participant | Revolutionary or royalist on Paris streets | Cost and lessons of structural change |\n| T005 | 1980s Shenzhen Migrant Worker | First generation migrant worker during China's reform era | Era dividends and social change |\n| T006 | 3024 Interstellar Federation Citizen | Ordinary citizen after humanity colonized multiple planets | Interstellar governance and civilization survival |\n\n### 💰 Investment & Finance Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| I001 | Charlie Munger | Berkshire Hathaway Vice Chairman | Multi-disciplinary mental models, antifragile investing |\n| I002 | George Soros | Quantum Fund Founder | Reflexivity theory, macro hedging |\n| I003 | Ray Dalio | Bridgewater Associates Founder | Principles-based approach, debt crisis cycles |\n| I004 | Warren Buffett | Berkshire Hathaway CEO | Value investing, long-term compounding mindset |\n| I005 | Jesse Livermore | 1920s Wall Street legendary trader | Trend trading, position management |\n| I006 | Peter Lynch | Legendary fund manager | Retail investor mindset, finding tenbaggers |\n| I007 | Nassim Taleb | Author of \"The Black Swan\" | Uncertainty, tail risk |\n| I008 | John Templeton | Global investing pioneer | Contrarian investing, buying at maximum pessimism |\n\n### 🎨 Creative Workers Series\n\n| ID | Role Name | Background | Core Perspective |\n|----|-----------|------------|------------------|\n| C001 | Hayao Miyazaki | Japanese animation master | Artisan spirit, emotional power of stories |\n| C002 | Haruki Murakami | Japanese author | Minimalism, writing as lifestyle |\n| C003 | Christopher Nolan | Film director | Narrative structure, philosophy of time |\n| C004 | Steve Jobs | Apple founder | Product aesthetics, minimalism |\n| C005 | Ang Lee | Chinese-American director | East-West cultural fusion |\n| C006 | Yayoi Kusama | Japanese artist | Pop art and spiritual world |\n| C007 | Quentin Tarantino | Film auteur | Violence aesthetics, non-linear narrative |\n| C008 | Lei Jun | Xiaomi founder | Internet thinking, ecosystem building |\n\n### 🔬 Academic Researchers Series\n\n| ID "},{"path":"skill-card.md","content":"## Description:\n\nFour Dimensional Deep Reading analyzes books or uploaded files through four persona perspectives, synthesizing first principles, structured notes, counterarguments, and unexpected viewpoints into multilingual reports with optional exports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[zhangboheng](https://clawhub.ai/user/zhangboheng)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to turn a book title or document into multi-perspective reading analysis, quick summaries, structured notes, and reusable flashcards or knowledge-base exports.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Automatic web lookups may disclose book titles, queries, or surrounding context to external services.\n\nMitigation: Use the skill only with content you are comfortable sending through lookup tools, or disable external lookup behavior before analyzing sensitive material.\n\nRisk: Report and cache persistence may retain analyzed content in the workspace.\n\nMitigation: Review the generated workspace/reports output and cache locations, and delete retained files when working with private or time-limited material.\n\nRisk: Multi-agent analysis of untrusted content can amplify misleading claims or instructions from source documents.\n\nMitigation: Review generated analysis before relying on it, especially for sensitive decisions or adversarial documents.\n\nRisk: Notion export can send report content to a configured external workspace.\n\nMitigation: Configure Notion export only when intentional, and confirm the target database and API credentials before syncing reports.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/zhangboheng/skills/four-dimensional-deep-reading)\n- [Author Website](https://www.luckydesigner.space)\n- [Identity Modules](artifact/reference/identity_modules.md)\n- [Book Parser Reference](artifact/reference/book_parser.py)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, configuration, guidance]\n\n**Output Format:** [Markdown analysis reports, CSV flashcards, Obsidian Markdown, and Notion JSON blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May save reports under workspace/reports and export to external knowledge tools when configured.]\n\n## Skill Version(s):\n\n1.8.0 (source: server release evidence and frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"requirements.txt","content":"# Optional dependencies for file parsing\n# Install only if you need to parse local files\n\n# PDF parsing (alternative: use PyPDF2 which is pure Python)\npdfplumber>=0.10.0\n\n# EPUB parsing\nebooklib>=0.18\nbeautifulsoup4>=4.12.0\nlxml>=4.9.0\n\n# ====================\n# Export Utilities (v1.8.0+)\n# ====================\n# Anki export: No additional dependencies needed\n# Obsidian export: No additional dependencies needed\n# Notion export (optional, for Notion integration)\nnotion-client>=2.0.0"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Simultaneously analyze books using 4 personas with multi-dimensional methods, producing structured insights on first principles, comparisons, and diverse per... Skill: Four Dimensional Deep Reading Owner: zhangboheng Summary: Simultaneously analyze books using 4 personas with multi-dimensional methods, producing structured insights on first principles, comparisons, and diverse per... 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