Four-Dimensional Deep Reading

🎓 User Onboarding

When a user uses this skill for the first time, provide an interactive onboarding guide in their language. The onboarding should explain:

1. What this skill does - Multi-perspective deep analysis 2. The 4 personas - Their roles and what they contribute 3. How to use the report - Understanding the output structure 4. Tips for best results - Getting the most value

Language-Specific Onboarding Templates

#### English (en)

🎓 Welcome to Four-Dimensional Deep Reading!
This skill summons 4 virtual personas to analyze your content from different angles simultaneously:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Axiom Analyst        →  First Principles Thinking        │
│     Strips away surface details to find fundamental truths  │
│                                                             │
│  📝 LMS Architect        →  Structured Notes                │
│     Organizes insights into Logic-Method-Summary format     │
│                                                             │
│  ⚡ Black Swan Hunter    →  Counterarguments & Edge Cases   │
│     Finds what could go wrong and challenges assumptions    │
│                                                             │
│  🎲 Random Variable X    →  Unexpected Perspectives         │
│     Brings fresh insights from random identity angles       │
└─────────────────────────────────────────────────────────────┘
📊 What you'll get:
• A comprehensive analysis report (saved to workspace/reports/)
• Multiple perspectives on the same content
• Actionable insights and structured notes
• Critical thinking challenges
⚡ Speed Reading Mode:
• Get core insights in just 30 seconds
• Trigger: Say "speed read [book title]" or "quick read [book title]"
• Output: Core premises + One-sentence summary + Key questions
• Best for: Quick book screening, time-constrained insights
💡 Tips for best results:
• Provide specific book titles or upload files for deeper analysis
• Ask follow-up questions about specific sections
• Use the LMS structure to create your own notes
🔄 Analysis Mode Comparison:
| Mode | Time | Output | Best For |
|------|------|--------|----------|
| Speed Mode | ~30s | Core premises + Summary + Questions | Quick screening |
| Standard Mode | ~2min | Full 4-persona analysis | Deep understanding |
| H-V Mode | ~5min | 4-persona + H-V analysis | Comprehensive research |
Ready to start? Just provide a book title or file!

#### 中文 (zh)

🎓 欢迎使用四维深度阅读！
本技能召唤 4 个虚拟角色从不同角度同时分析你的内容：
┌─────────────────────────────────────────────────────────────┐
│  🔬 第一性原理师        →  公理化思维分析                    │
│     剥离表象，追溯底层假设和核心公理                        │
│                                                             │
│  📝 结构化笔记官        →  LMS 结构输出                     │
│     将洞察组织为 Logic-Method-Summary 格式                  │
│                                                             │
│  ⚡ 黑天鹅猎手          →  反驳论证与边界检测                │
│     寻找失效点和边缘情况，挑战假设                          │
│                                                             │
│  🎲 随机变量 X          →  意外视角洞察                      │
│     从随机身份角度带来全新思考                              │
└─────────────────────────────────────────────────────────────┘
📊 你将获得：
• 一份综合分析报告（自动保存到 workspace/reports/）
• 同一内容的多视角解读
• 可执行的洞察和结构化笔记
• 批判性思维挑战
⚡ 速读模式（Speed Reading Mode）：
• 只需30秒，快速获取核心洞察
• 触发方式：说「速读【书名】」或「快速阅读【书名】」
• 输出：核心前提 + 一句话总结 + 关键问题
• 适合：快速筛选书籍、时间紧迫时获取要点
💡 使用建议：
• 提供具体书名或上传文件可获得更深入的分析
• 对特定部分提出追问
• 使用 LMS 结构创建自己的笔记
🔄 分析模式对比：
| 模式 | 时间 | 输出内容 | 适用场景 |
|------|------|----------|----------|
| 速读模式 | ~30秒 | 核心前提+一句话总结+关键问题 | 快速筛选 |
| 标准模式 | ~2分钟 | 4角色完整分析 | 深度理解 |
| 横纵模式 | ~5分钟 | 4角色+横纵分析 | 全面研究 |
准备好了吗？提供一本书名或文件即可开始！

#### 日本語 (ja)

🎓 四次元深読みへようこそ！
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：
┌─────────────────────────────────────────────────────────────┐
│  🔬 公理分析者          →  第一原理思考                      │
│     表面を取り除き、根本的な真実を見つける                  │
│                                                             │
│  📝 LMS設計者           →  構造化ノート                      │
│     洞察をLogic-Method-Summary形式で整理                    │
│                                                             │
│  ⚡ ブラックスワン探求者 →  反論とエッジケース               │
│     何がうまくいかないかを見つけ、仮定に挑戦                │
│                                                             │
│  🎲 ランダム変数X       →  予期しない視点                    │
│     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      │
└─────────────────────────────────────────────────────────────┘
📊 得られるもの：
• 包括的な分析レポート（workspace/reports/に保存）
• 同じコンテンツの複数の視点
• 実行可能な洞察と構造化されたノート
• 批判的思考の課題
💡 最高の結果を得るためのヒント：
• 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
• 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
• LMS構造を使用して自分のノートを作成
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！

#### 한국어 (ko)

🎓 4차원 깊은 읽기에 오신 것을 환영합니다!
이 스킬은 4명의 가상 페르소나를 소환하여 다른 각도에서 동시에 콘텐츠를 분석합니다:
┌─────────────────────────────────────────────────────────────┐
│  🔬 공리 분석가         →  제1원칙 사고                      │
│     표면을 벗겨내고 근본적인 진실을 찾습니다                │
│                                                             │
│  📝 LMS 설계자          →  구조화된 노트                     │
│     통찰력을 Logic-Method-Summary 형식으로 정리             │
│                                                             │
│  ⚡ 블랙 스완 사냥꾼     →  반론과 엣지 케이스               │
│     무엇이 잘못될 수 있는지 찾고 가정에 도전                │
│                                                             │
│  🎲 무작위 변수 X       →  예상치 못한 관점                  │
│     무작위 정체성에서 새로운 통찰을 가져옵니다              │
└─────────────────────────────────────────────────────────────┘
📊 얻을 수 있는 것:
• 포괄적인 분석 보고서 (workspace/reports/에 저장)
• 동일한 콘텐츠에 대한 여러 관점
• 실행 가능한 통찰력과 구조화된 노트
• 비판적 사고 과제
💡 최상의 결과를 위한 팁:
• 더 깊은 분석을 위해 구체적인 책 제목을 제공하거나 파일을 업로드
• 특정 섹션에 대한 후속 질문
• LMS 구조를 사용하여 자신만의 노트 만들기
준비되셨나요? 책 제목이나 파일을 제공하세요!

#### Français (fr)

🎓 Bienvenue dans la Lecture Profonde Quadridimensionnelle!
Cette compétence invoque 4 personas virtuels pour analyser votre contenu sous différents angles simultanément:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Analyste d'Axiomes  →  Pensée des Premiers Principes    │
│     Élimine les détails de surface pour trouver les vérités │
│                                                             │
│  📝 Architecte LMS      →  Notes Structurées                │
│     Organise les insights en format Logic-Method-Summary    │
│                                                             │
│  ⚡ Chasseur de Cygne   →  Contre-arguments et Cas Limites  │
│     Trouve ce qui pourrait mal tourner et défie les hypothèses│
│                                                             │
│  🎲 Variable Aléatoire X →  Perspectives Inattendues        │
│     Apporte des insights frais d'angles identitaires aléatoires│
└─────────────────────────────────────────────────────────────┘
📊 Ce que vous obtiendrez:
• Un rapport d'analyse complet (sauvegardé dans workspace/reports/)
• Plusieurs perspectives sur le même contenu
• Des insights actionnables et des notes structurées
• Des défis de pensée critique
💡 Conseils pour de meilleurs résultats:
• Fournissez des titres de livres spécifiques ou téléchargez des fichiers
• Posez des questions de suivi sur des sections spécifiques
• Utilisez la structure LMS pour créer vos propres notes
Prêt à commencer? Fournissez simplement un titre de livre ou un fichier!

#### Deutsch (de)

🎓 Willkommen beim Vierdimensionalen Tiefenlesen!
Diese Fähigkeit beschwört 4 virtuelle Personas, um Ihren Inhalt gleichzeitig aus verschiedenen Blickwinkeln zu analysieren:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Axiom-Analytiker    →  First-Principles-Denken          │
│     Entfernt Oberflächliches, um fundamentale Wahrheiten zu finden│
│                                                             │
│  📝 LMS-Architekt       →  Strukturierte Notizen            │
│     Organisiert Erkenntnisse im Logic-Method-Summary-Format │
│                                                             │
│  ⚡ Schwarzer-Schwan-Jäger →  Gegenargumente & Randfälle   │
│     Findet was schiefgehen könnte und stellt Annahmen in Frage│
│                                                             │
│  🎲 Zufallsvariable X   →  Unerwartete Perspektiven          │
│     Bringt frische Einblicke aus zufälligen Identitätswinkeln│
└─────────────────────────────────────────────────────────────┘
📊 Was Sie erhalten:
• Einen umfassenden Analysebericht (gespeichert in workspace/reports/)
• Mehrere Perspektiven auf denselben Inhalt
• Umsetzbare Erkenntnisse und strukturierte Notizen
• Kritisches Denken Herausforderungen
💡 Tipps für beste Ergebnisse:
• Geben Sie spezifische Buchtitel an oder laden Sie Dateien hoch
• Stellen Sie Folgefragen zu bestimmten Abschnitten
• Verwenden Sie die LMS-Struktur für eigene Notizen
Bereit anzufangen? Geben Sie einfach einen Buchtitel oder eine Datei an!

#### Español (es)

🎓 ¡Bienvenido a la Lectura Profunda Cuatridimensional!
Esta habilidad invoca 4 personas virtuales para analizar tu contenido desde diferentes ángulos simultáneamente:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Analista de Axiomas →  Pensamiento de Primeros Principios│
│     Elimina detalles superficiales para encontrar verdades  │
│                                                             │
│  📝 Arquitecto LMS      →  Notas Estructuradas              │
│     Organiza ideas en formato Logic-Method-Summary          │
│                                                             │
│  ⚡ Cazador de Cisne    →  Contraargumentos y Casos Límite  │
│     Encuentra qué podría salir mal y desafía suposiciones   │
│                                                             │
│  🎲 Variable Aleatoria X →  Perspectivas Inesperadas        │
│     Trae insights frescos desde ángulos de identidad aleatorios│
└─────────────────────────────────────────────────────────────┘
📊 Lo que obtendrás:
• Un informe de análisis completo (guardado en workspace/reports/)
• Múltiples perspectivas sobre el mismo contenido
• Insights accionables y notas estructuradas
• Desafíos de pensamiento crítico
💡 Consejos para mejores resultados:
• Proporciona títulos de libros específicos o sube archivos
• Haz preguntas de seguimiento sobre secciones específicas
• Usa la estructura LMS para crear tus propias notas
¿Listo para empezar? ¡Solo proporciona un título de libro o archivo!

#### Português (pt)

🎓 Bem-vindo à Leitura Profunda Quadridimensional!
Esta habilidade invoca 4 personas virtuais para analisar seu conteúdo de diferentes ângulos simultaneamente:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Analista de Axiomas →  Pensamento de Primeiros Princípios│
│     Remove detalhes superficiais para encontrar verdades    │
│                                                             │
│  📝 Arquiteto LMS       →  Notas Estruturadas               │
│     Organiza insights em formato Logic-Method-Summary       │
│                                                             │
│  ⚡ Caçador de Cisne    →  Contra-argumentos e Casos Limite │
│     Encontra o que pode dar errado e desafia suposições     │
│                                                             │
│  🎲 Variável Aleatória X →  Perspectivas Inesperadas        │
│     Traz insights frescos de ângulos de identidade aleatórios│
└─────────────────────────────────────────────────────────────┘
📊 O que você obterá:
• Um relatório de análise completo (salvo em workspace/reports/)
• Múltiplas perspectivas sobre o mesmo conteúdo
• Insights acionáveis e notas estruturadas
• Desafios de pensamento crítico
💡 Dicas para melhores resultados:
• Forneça títulos de livros específicos ou carregue arquivos
• Faça perguntas de acompanhamento sobre seções específicas
• Use a estrutura LMS para criar suas próprias notas
Pronto para começar? Basta fornecer um título de livro ou arquivo!

#### Русский (ru)

🎓 Добро пожаловать в Четырёхмерное Глубокое Чтение!
Этот навык призывает 4 виртуальных персоны для анализа вашего контента с разных углов одновременно:
┌─────────────────────────────────────────────────────────────┐
│  🔬 Аналитик Аксиом     →  Мышление Первых Принципов        │
│     Убирает поверхностные детали, чтобы найти истину        │
│                                                             │
│  📝 Архитектор LMS      →  Структурированные Заметки        │
│     Организует идеи в формате Logic-Method-Summary          │
│                                                             │
│  ⚡ Охотник за Чёрным   →  Контраргументы и Краевые Случаи  │
│     Находит что может пойти не так и оспаривает предположения│
│                                                             │
│  🎲 Случайная Переменная X →  Неожиданные Перспективы       │
│     Приносит свежие идеи со случайных углов идентичности    │
└─────────────────────────────────────────────────────────────┘
📊 Что вы получите:
• Комплексный аналитический отчёт (сохранён в workspace/reports/)
• Множество перспектив на один и тот же контент
• Практические идеи и структурированные заметки
• Задачи критического мышления
💡 Советы для лучших результатов:
• Предоставьте конкретные названия книг или загрузите файлы
• Задавайте уточняющие вопросы по конкретным разделам
• Используйте структуру LMS для создания собственных заметок
Готовы начать? Просто предоставьте название книги или файл!

When to Show Onboarding

Show the onboarding guide when: 1. First-time user - User has never used the skill before 2. Explicit request - User asks "how to use this skill" or "help me understand" 3. After error - User seems confused about the output format

Implementation

def should_show_onboarding(user_id: str, skill_usage_count: dict) -> bool:
    """Determine if onboarding should be shown"""
    return skill_usage_count.get(user_id, 0) < 1
def get_onboarding_message(language: str) -> str:
    """Get language-specific onboarding message"""
    ONBOARDING_TEMPLATES = {
        "en": ENGLISH_ONBOARDING,
        "zh": CHINESE_ONBOARDING,
        "ja": JAPANESE_ONBOARDING,
        "ko": KOREAN_ONBOARDING,
        "fr": FRENCH_ONBOARDING,
        "de": GERMAN_ONBOARDING,
        "es": SPANISH_ONBOARDING,
        "pt": PORTUGUESE_ONBOARDING,
        "ru": RUSSIAN_ONBOARDING,
    }
    return ONBOARDING_TEMPLATES.get(language, ONBOARDING_TEMPLATES["en"])

Core Mechanism

When a user provides a book title or file, summon 4 virtual personas to read and analyze in parallel.

⚡ Book Review Search (v1.7.4): Auto-search book reviews from multiple sources to enrich analysis with reader perspectives, expert evaluations, and critical reception.

📖 Detailed Book Introduction (v1.7.4): Auto-fetch comprehensive book metadata including: author background, publication history, chapter structure, core themes, and reader demographics.

⚡ 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]".

Speed Mode Output Format:

## ⚡ 速读报告：[书名]
核心前提
[3-5条不可再分解的原子命题]
一句话总结
> [核心观点，不超过30字]
关键问题
1. [问题1]
2. [问题2]
3. [问题3]

⏱️ 分析时间：~30秒

Speed Mode Trigger Keywords:

⚡ Parallel Processing (v1.6.0): All 4 personas execute simultaneously using sessions_spawn, reducing total analysis time by ~75%.

Implementation: See scripts/parallel_analysis.py for the parallel execution module.

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.

Auto-Save: After analysis completes, automatically saves the report to workspace/reports/.

📥 Book Acquisition & Preprocessing Module

🚀 Enhanced Data Fetching (v1.5.0)

核心优化：

实现文件：reference/book_fetcher_enhanced.py

使用方式：

from book_fetcher_enhanced import fetch_book_info
获取书籍信息（自动多源备份）
info = fetch_book_info("原子习惯")
info = fetch_book_info("Atomic Habits", author="James Clear")
清理过期缓存
from book_fetcher_enhanced import clear_cache
cleared = clear_cache()
查看缓存统计
from book_fetcher_enhanced import get_cache_stats
stats = get_cache_stats()

缓存位置：/root/.openclaw/workspace/.cache/book_fetcher/

📚 Auto Book Review Search (v1.7.4新增)

功能说明：自动从多个平台搜索书籍评论，丰富分析维度

数据来源：

| 平台 | 语言 | 评论类型 | 获取难度 | |------|------|----------|----------| | 豆瓣评论 | 中文 | 用户长评、书评 | ⭐⭐ | | 知乎讨论 | 中文 | 专业问答、评价 | ⭐⭐⭐ | | Goodreads Reviews | 英文 | 用户评论、专业书评 | ⭐⭐ | | Amazon Reviews | 英文 | 用户评分、VP评论 | ⭐⭐ | | Booklog | 日文 | 用户书评 | ⭐⭐⭐ | | Yes24 | 韩文 | 用户评论 | ⭐⭐⭐ |

搜索关键词策略：

# 中文书籍
[书名] 书评
[书名] 读后感
[书名] 评价
[作者] 书评
英文书籍
[book name] review
[book name] review analysis
[book name] criticism
[author] book review
日文书籍
[書名] 書評
[著者] レビュー

评论分析维度：

输出格式：

## 📚 书评综述
整体评价倾向
正面：[X]%
中性：[X]%
负面：[X]%
核心正面观点
1. [高频正面观点1]
2. [高频正面观点2]
核心负面观点
1. [高频负面观点1]
2. [高频负面观点2]
争议与分歧
[专家与读者观点分歧]
精选评论引用
> "[精选评论片段]" - 来源平台

📖 Detailed Book Introduction (v1.7.4新增)

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

自动获取信息：

| 信息类型 | 来源 | 说明 | |----------|------|------| | 作者简介 | 豆瓣/Goodreads/维基 | 教育背景、代表作品、获奖情况 | | 出版历程 | 豆瓣/Amazon | 初版时间、版本迭代、发行量 | | 章节结构 | 豆瓣TOC/京东/Amazon | 完整目录、章节数量 | | 核心主题 | 书籍简介/书评提炼 | 一句话介绍、适合人群 | | 媒体评价 | 豆瓣/Amazon/媒体网站 | 名人推荐、媒体评论 | | 获奖情况 | 搜索结果 | 书籍获奖、榜单排名 |

获取流程：

1. 语言检测 → 选择数据源
2. 主数据源获取 → 豆瓣/Goodreads
3. 补充数据源获取 → 维基/媒体
4. 信息聚合 → 结构化输出
5. 质量校验 → 缺失字段标记

输出格式：

## 📖 书籍详细介绍
基本信息
| 字段 | 内容 |
|------|------|
| 书名 | [书名] |
| 作者 | [作者] |
| 译者 | [译者，如适用] |
| 出版社 | [出版社] |
| 出版年 | [年份] |
| 页数 | [页数] |
| ISBN | [ISBN] |
作者简介
[作者背景介绍]
书籍简介
[一句话介绍]
[详细简介]
章节结构
| 章节 | 标题 | 核心内容 |
|------|------|----------|
| 第1章 | [标题] | [内容] |
| ... | ... | ... |
适合人群
[人群1]
[人群2]
获奖与荣誉
[奖项1]
[奖项2]

Method A: By Book Title (Web Search)

When user provides only a book title, auto-retrieve follows this flow:

Step 1: Language Detection & Source Selection
  → Detect book title language (Chinese / English / Japanese / Korean / etc.)
  → Route to appropriate data source based on language
Step 2: Multi-source metadata search (Language-Specific)
  【Chinese Books】
  → Douban (豆瓣) → Rating, summary, TOC, author info, reviews
  → Dangdang (当当) → Price, ranking, reader demographics
  → Zhihu (知乎) → Discussion threads, expert opinions
  → Baidu Baike → Author biography, creation background
  【English Books】
  → Goodreads → Rating, reviews, reader demographics, similar books
  → Amazon Books → Rating, bestseller ranking, editorial reviews
  → Google Books → Preview chapters, metadata, ISBN
  → Wikipedia → Creation background, version history
  → LibraryThing → Tags, collections, work information
  【Japanese Books】
  → Amazon JP → Rating, reviews
  → Booklog (ブクログ) → User reviews, ratings
  → Goodreads (fallback) → International reviews
  【Korean Books】
  → Yes24 → Rating, reviews, bestseller status
  → Aladin → Reader reviews, ratings
  → Goodreads (fallback) → International reviews
  【Other Languages】
  → Goodreads (primary) → International book database
  → Google Books → Metadata and preview
  → Wikipedia (lang-specific) → Background information
Step 3: Content aggregation
  → Merge sources into structured JSON
  → Extract: title, author, ISBN, publication year, chapter list, summary, ratings

Implementation Tools:

🌐 Language-Specific Retrieve Functions

#### Chinese Books (豆瓣/Douban)

def retrieve_douban_book_info(book_name):
    """Retrieve Chinese book info from Douban"""
    # Use web_tool tool to retrieve book information
    # Use web_tool tool to retrieve book information
    return book_info
    
    return {
        "title": extract_title(content),
        "author": extract_author(content),
        "rating": extract_rating(content),        # 0-10 scale
        "rating_count": extract_rating_count(content),
        "summary": extract_summary(content),
        "chapters": extract_toc(content),
        "publisher": extract_publisher(content),
        "pub_date": extract_pub_date(content),
        "isbn": extract_isbn(content),
        "tags": extract_tags(content),
        "source": "douban"
    }

#### English Books (Goodreads)

def retrieve_goodreads_book_info(book_name, author=None):
    """Retrieve English book info from Goodreads"""
    # Use web_tool tool to retrieve book information
    # Goodreads search query combines book name and author
    return book_info
    
    return {
        "title": extract_title(content),
        "author": extract_author(content),
        "rating": extract_rating(content),        # 0-5 scale
        "rating_count": extract_rating_count(content),
        "summary": extract_description(content),
        "genres": extract_genres(content),
        "pages": extract_num_pages(content),
        "isbn": extract_isbn(content),
        "similar_books": extract_similar_books(content),  # Goodreads feature
        "reviews": extract_top_reviews(content),
        "source": "goodreads"
    }

#### English Books (Amazon)

def retrieve_amazon_book_info(book_name):
    """Retrieve English book info from Amazon Books"""
    # Use web_tool tool to retrieve book information
    # Amazon Books search endpoint
    return book_info
    
    return {
        "title": extract_title(content),
        "author": extract_author(content),
        "rating": extract_rating(content),        # 0-5 scale
        "rating_count": extract_rating_count(content),
        "price": extract_price(content),
        "bestseller_rank": extract_bestseller_rank(content),
        "editorial_review": extract_editorial_review(content),
        "source": "amazon"
    }

#### Japanese Books (Booklog)

def retrieve_booklog_info(book_name):
    """Retrieve Japanese book info from Booklog"""
    # Use web_tool tool to retrieve book information
    # Booklog (ブクログ) Japanese book reviews
    return book_info
    
    return {
        "title": extract_title(content),
        "author": extract_author(content),
        "rating": extract_rating(content),
        "reviews": extract_reviews(content),
        "source": "booklog"
    }

#### Korean Books (Yes24)

def retrieve_yes24_info(book_name):
    """Retrieve Korean book info from Yes24"""
    # Use web_tool tool to retrieve book information
    # Yes24 Korean book database
    return book_info
    
    return {
        "title": extract_title(content),
        "author": extract_author(content),
        "rating": extract_rating(content),
        "price": extract_price(content),
        "source": "yes24"
    }

🔄 Unified Book Info Retrieveer

def retrieve_book_info(book_name, author=None, language=None):
    """
    Unified entry: auto-detect language and retrieve from appropriate sources
    
    Priority by language:
    - Chinese: Douban → Baidu Baike → Zhihu → Wikipedia ZH
    - English: Goodreads → Google Books → Wikipedia EN → Amazon
    - Japanese: Booklog → Amazon JP → Goodreads
    - Korean: Yes24 → Aladin → Goodreads
    - Other: Goodreads → Wikipedia EN → Google Books
    
    Enhanced Features (v1.5.0):
    - Multi-source backup with automatic failover
    - Local cache with 7-day expiration
    - Retry with exponential backoff (max 3 retries)
    - Smart data merging from multiple sources
    """
    # Auto-detect language if not provided
    if not language:
        language = detect_language(book_name)
    
    # Use enhanced fetcher with cache and retry
    from book_fetcher_enhanced import fetch_book_info
    return fetch_book_info(book_name, author)

📊 Data Source Configuration

# 数据源优先级配置
SOURCE_PRIORITY = {
    "zh": ["douban", "baidu_baike", "zhihu", "wikipedia_zh"],
    "en": ["goodreads", "google_books", "wikipedia_en", "amazon"],
    "ja": ["booklog", "amazon_jp", "goodreads"],
    "ko": ["yes24", "aladin", "goodreads"],
    "default": ["goodreads", "wikipedia_en", "google_books"]
}
字段优先级（哪个来源的数据更可信）
FIELD_PRIORITY = {
    "rating": ["douban", "goodreads", "amazon"],
    "summary": ["douban", "goodreads", "wikipedia"],
    "reviews": ["douban", "goodreads", "amazon"],
}
重试配置
RETRY_CONFIG = {
    "max_retries": 3,
    "base_delay": 1.0,  # 秒
    "max_delay": 10.0,  # 秒
}
缓存配置
CACHE_CONFIG = {
    "cache_dir": "/root/.openclaw/workspace/.cache/book_fetcher",
    "expire_days": 7,
}

⚠️ Error Handling Strategy

┌─────────────────────────────────────────────────────┐
│              数据获取错误处理流程                    │
├─────────────────────────────────────────────────────┤
│                                                     │
│  1. 尝试数据源 A                                    │
│     ├─ 成功 → 返回数据                              │
│     └─ 失败 → 记录错误，进入步骤2                   │
│                                                     │
│  2. 检查本地缓存                                    │
│     ├─ 有缓存且未过期 → 返回缓存                    │
│     └─ 无缓存或已过期 → 进入步骤3                   │
│                                                     │
│  3. 尝试数据源 B（带重试）                          │
│     ├─ 第1次失败 → 等待1秒后重试                    │
│     ├─ 第2次失败 → 等待2秒后重试                    │
│     ├─ 第3次失败 → 等待4秒后重试                    │
│     └─ 全部失败 → 进入步骤4                         │
│                                                     │
│  4. 尝试数据源 C...                                 │
│     └─ 依次尝试所有数据源                           │
│                                                     │
│  5. 全部失败                                        │
│     └─ 返回部分数据 + 错误信息                      │
│                                                     │
└─────────────────────────────────────────────────────┘

def detect_language(text): """Detect text language using character patterns""" # Chinese: CJK Unified Ideographs if any('\u4e00' <= c <= '\u9fff' for c in text): return 'zh' # Japanese: Hiragana or Katakana if any('\u3040' <= c <= '\u309f' or '\u30a0' <= c <= '\u30ff' for c in text): return 'ja' # Korean: Hangul if any('\uac00' <= c <= '\ud7af' for c in text): return 'ko' # Default to English return 'en'

def retrieve_english_book_info(book_name, author=None): """Retrieve English book info from multiple sources""" result = {} # Primary: Goodreads try: result['goodreads'] = retrieve_goodreads_book_info(book_name, author) except Exception as e: print(f"Goodreads retrieve failed: {e}") # Secondary: Amazon try: result['amazon'] = retrieve_amazon_book_info(book_name) except Exception as e: print(f"Amazon retrieve failed: {e}") # Tertiary: Google Books try: result['google_books'] = retrieve_google_books_info(book_name, author) except Exception as e: print(f"Google Books retrieve failed: {e}") # Merge and deduplicate return merge_book_info(result)

📊 Data Source Comparison
| Source | Language | Rating Scale | Unique Features |
|--------|----------|--------------|----------------|
| Douban | Chinese | 0-10 | Tags, TOC, Chinese reviews |
| Goodreads | Multi | 0-5 | Similar books, reading lists, quotes |
| Amazon | Multi | 0-5 | Bestseller rank, price, editorial reviews |
| Google Books | Multi | N/A | Preview chapters, ISBN metadata |
| Booklog | Japanese | 0-5 | Japanese user reviews |
| Yes24 | Korean | 0-10 | Korean bestseller status |
| LibraryThing | Multi | 0-5 | Collections, work relationships |
Method B: Local File Upload (Format Parsing)
Supported formats (pure Python, no system binaries required):
| Format | Parser | Notes |
|--------|--------|-------|
| TXT | Python open() direct read | UTF-8/GBK auto-detection |
| PDF | pdfplumber | Pure Python, preserve chapter structure |
| EPUB | ebooklib + BeautifulSoup | Pure Python, parse HTML body |
| MD | Direct read | Native support |
Note: MOBI format is not supported. Please convert to EPUB first using online tools.
Parser Module Path:

Parsing Flow:

book_parser.py Core Framework:

Note: This is pseudocode for illustration purposes

Actual implementation should use PyPDF2 or pdfplumber library

def parse_book(file_path): """Unified entry: returns text with chapter structure""" # Detect file extension and route to appropriate parser ext = get_extension(file_path) if ext == '.txt': return parse_txt(file_path) elif ext == '.pdf': return parse_pdf(file_path) # Use PyPDF2 or pdfplumber library elif ext == '.epub': return parse_epub(file_path) elif ext == '.mobi': return parse_mobi(file_path) elif ext == '.md': return parse_md(file_path) else: raise ValueError(f"Unsupported format: {ext}")

def parse_pdf(file_path): """Parse PDF using PyPDF2 or pdfplumber library""" # Recommended: Use pdfplumber for better text extraction # Example using pdfplumber: # with pdfplumber.open(file_path) as pdf: # text = "\n".join([page.extract_text() for page in pdf.pages]) return {"content": text, "format": "pdf"}

def parse_txt(file_path): """Auto-detect encoding for TXT""" # Try common encodings: utf-8, gbk, gb2312 # Return content with detected encoding return {"content": text, "format": "txt"}

Method C: Direct Link Retrieve
User provides full text link (e.g., public PDF, online ebook):

🔍 Horizontal-Vertical Analysis Data Strategy
Diachronic Data Sources (Intellectual History Positioning)
| Data Type | Source | Tool |
|-----------|--------|------|
| Publication year | Douban book details | web_tool |
| Author interviews | Search engine + news sites | search_tool + web_tool |
| Version evolution | Publisher site / Douban versions | web_tool |
| Intellectual origins | Citations / reference chains | Manual annotation + AI inference |
| Later influence | Citation count / citing works | Academic DB search (optional) |
Diachronic Analysis Module:

Synchronic Data Sources (Competitor Benchmarking)
| Comparison Dimension | Data Source |
|---------------------|-------------|
| Similar book recommendations | "Readers also bought" (Amazon/Douban) |
| Core viewpoint differences | Professional review comparison articles |
| Rating comparison | Multi-platform rating aggregation |
| Reader demographics | Review section keyword analysis |
Synchronic Analysis Module:

Persona Definitions & Deep Instructions
🔬 Axiom Analyst (First Principles)
Technique Reference: Elon Musk decomposition / Axiomatic thinking
输出要求：
字数范围：800-1500字
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
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
Core Instruction (System Prompt Add-on):

Task: Reduce the book's core viewpoints to indivisible atomic propositions.

Workflow: 1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels 2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as "A1, A2, A3..." 3. Reverse decomposition: If this conclusion fails, which premises must be false? 4. Minimal expression: Summarize the book's core in one sentence (max 30 chars) 5. Book title analysis: Decode the metaphor in the book title itself

Output Format:

核心前提

底层假设

书名隐喻解析

一句话总结

作者真实意图

📝 L-M-S Architect (Structured Notes)
Technique Reference: Cornell Notes / Luhmann Zettelkasten
输出要求：
字数范围：1000-2000字
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
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
Core Instruction (System Prompt Add-on):

Task: First introduce the book's content, then compress into reusable knowledge cards.

L-M-S Structure Definition:

Cornell Notes Integration:

Output Format:

章节结构

人物关系网络

主角
├── 关系线1
│   ├── 人物A（关系类型）
│   └── 人物B（关系类型）
├── 关系线2
│   └── 人物C（关系类型）

关键转折点

Logic (逻辑链)

Method (方法论)

Summary (摘要)

⚡ Black Swan Hunter (Contrarian)
Technique Reference: Taleb critical thinking / Edge case analysis
输出要求：
字数范围：800-1500字
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
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
Core Instruction (System Prompt Add-on):

Task: Identify Edge Cases and Failure Points of conclusions.

Workflow: 1. Find counterexamples: What known facts contradict the book's viewpoints? 2. Boundary detection: Under what conditions does this viewpoint fail? 3. Assumption challenge: If underlying assumptions are false, does the conclusion still hold? 4. Butterfly effect: What possible chain reactions are overlooked? 5. Contemporary relevance: Why would a 2026 reader still care (or not)?

Taleb-style Questions:

Output Format:

黑天鹅事件

边界条件

假设脆弱性

当代意义挑战

反驳与辩护

- "Who least wants this viewpoint to be true?"
Output Format:
Black Swan Events
[Real cases conflicting with book's viewpoints]
Edge Cases
Condition 1: [Viewpoint may fail under XX circumstances]
Condition 2: [Conclusion doesn't hold when XX variable changes]
Assumption Fragility
[Chain reactions when underlying assumptions are challenged]

🎲 Random Variable X (Monte Carlo Identity)

Technique Reference: Monte Carlo Role Sampling

输出要求：

Core Instruction (System Prompt Add-on):

You are a "Random Variable X", randomly loading an Identity_Module via Monte Carlo method.
Random Persona Pool: Read role list from reference/identity_modules.md, randomly select 1.
Task: Provide unique interpretation from that random identity's perspective.
Output Format:
🎲 随机身份：[角色名称]
角色背景
[这个角色的身份背景、职业、价值观]
独特视角
[从这个角色的视角看这本书，会产生什么独特洞察？]
> 必须与前面三个角色的视角不同，必须产生跨界思考
核心问题
[这个角色会提出的3个关键问题]
1. [问题1]
2. [问题2]
3. [问题3]
跨界联想
[将书中内容与角色的专业领域连接，产生新的理解]

Random Role Loading Method: 1. Read all roles from reference/identity_modules.md 2. Use random number generator to select 1 role 3. Load that role's complete definition and execute analysis

预设角色池（当reference/identity_modules.md不存在时使用）：

📊 Diachronic Analysis (Longitudinal)

Task: Restore the book's complete development along the timeline

Data Source: Call [Diachronic Data Strategy] for external information

## Diachronic Analysis: From Birth to Present
Creation Background
Writing period: [From Douban/Wikipedia]
Social environment: [From search results]
Core problem author faced: [From interviews/biography]
Version Evolution (if any)
Core changes from first edition to current: [From version history]
Intellectual evolution trajectory: [Cross-version comparison]
Intellectual History Positioning
Contemporary similar works: [Same-period work search]
Intellectual origins (influenced by): [Citation chain analysis]
Influence on later works: [Citation count / review mentions]

🔀 Synchronic Analysis (Horizontal)

Task: At current time slice, compare with similar books

Data Source: Call [Synchronic Data Strategy] for competitor data

## Synchronic Analysis: Competitor Benchmarking
Similar Classic Comparison
| Dimension | This Book | Competitor A | Competitor B |
|-----------|-----------|-------------|--------------|
| Core viewpoint | [This book] | [Retrieveed] | [Retrieveed] |
| Methodology | [This book] | [Retrieveed] | [Retrieveed] |
| Writing style | [This book] | [Retrieveed] | [Retrieveed] |
| Applicable scenarios | [This book] | [Retrieveed] | [Retrieveed] |
| Rating | [Retrieveed] | [Retrieveed] | [Retrieveed] |
Differentiation Positioning
[This book's unique value and irreplaceability]
Reader Selection Advice
Who should read: [Based on content characteristics]
Alternatives: [Similar book recommendations]

🎯 H-V Intersection Insight

Task: Combine diachronic and synchronic analysis for unique judgment

## H-V Intersection Insight
History's Gift
[Which past factors shaped this book's core value]
Current Coordinates
[This book's position in current intellectual landscape]
Future Projection
[This book's predictive value for future trends]

Auto-Save Module

Save Path: /root/.openclaw/workspace/reports/

File Naming Rule

[Book_Name]_[Analysis_Mode]_[Date].md

Examples:

Save Flow

Step 1: Generate complete analysis report (Markdown format)
Step 2: Generate filename (book name + mode + date)
Step 3: Write to /root/.openclaw/workspace/reports/[filename].md
Step 4: Return save confirmation

Report Template Structure

---
title: [Book Name] Deep Analysis Report
author: Four-Dimensional Deep Reading AI
date: YYYY-MM-DD
mode: [Standard Mode / H-V Enhanced Mode]
tags: [book, analysis, reading notes]
source: [book name / file path / link]
data_sources: [list of retrieveed data sources]
"[Book Name]" Deep Analysis Report
> Analysis Mode: [Standard / H-V Enhanced] | Analysis Time: [Date] | Random Persona: [Role Name]
📖 全书内容简介（标准模式必需）
> 本书基本信息、作者背景、核心内容概述（200-500字）
| 项目 | 内容 |
|------|------|
| 书名 | [书名] |
| 作者 | [作者] |
| 类型 | [类型] |
| 成书时间 | [时间] |
内容概述：
[此处为全书内容简介，包含故事主线、主要人物、核心主题等]
🔬 Axiom Analyst
[Content]
📝 L-M-S Architect
Logic
[Content]
Method
[Content]
Summary
> [Content]
⚡ Black Swan Hunter
[Content]
🎲 Random Variable X: [Role Name]
[Content]
📊 H-V Analysis (H-V Mode Only)
Diachronic: Intellectual History Positioning
[Content]
Synchronic: Competitor Benchmarking
[Content]
Intersection: Insight
[Content]
🧠 Arbiter Summary (v1.7.2)
> This summary is auto-generated after the 4 personas complete their analysis. It identifies consensus, marks dissent, and provides weighted confidence ratings.
🎯 Core Consensus (4/4 Agreement)
| # | Consensus Point | Confidence | Supported By |
|---|-----------------|------------|--------------|
| 1 | [Point where all 4 personas agree] | ⭐⭐⭐⭐⭐ 95% | All 4 personas |
| 2 | [Second consensus point] | ⭐⭐⭐⭐ 85% | [Persona names] |
| 3 | [Third consensus point] | ⭐⭐⭐ 75% | [Persona names] |
⚔️ Key Dissent Analysis
| Topic | Axiom Analyst | LMS Architect | Black Swan Hunter | Random X | Verdict |
|-------|---------------|---------------|-------------------|----------|---------|
| [Controversial topic] | [View] | [View] | [View] | [View] | [Arbiter's judgment with reasoning] |
⚖️ Weighted Confidence Ranking
Based on role-specific reliability:
Black Swan Hunter: ×1.5 (Critical thinking validation)
Axiom Analyst: ×1.2 (Logical rigor)
LMS Architect: ×1.0 (Information completeness)
Random Variable X: ×0.8 (Cross-domain creativity)
Top Rated Insights:
1. [Insight 1] — Weighted Score: 92/100
2. [Insight 2] — Weighted Score: 87/100
3. [Insight 3] — Weighted Score: 83/100
📋 Reader Action Checklist
[ ] Verify: [Factual claim that needs verification]
[ ] Question: [Assumption that deserves doubt]
[ ] Apply: [Methodology worth trying]
[ ] Avoid: [Mistake the book warns against]
💡 Final Verdict
> [One paragraph synthesizing what this book offers in 2026, who should read it, and what to take away]
📚 Reference Information
Book: [Book Name]
Author: [Author]
Analysis Date: [Date]
Random Persona Used: [Role Name]
Data Sources: [List all retrieveed external links]

Workflow

Step 1: Receive Input and Classify

User input falls into three types:

Step 2: Content Extraction and Cleaning

Raw content → Remove ads/headers/footers → Chapter marking → Extract

Step 3: Parallel Four-Dimensional Analysis ⚡

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。

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。

#### Language Detection Priority

1. Explicit language parameter (--lang)
2. User's message language
3. Book's original language
4. Default: English

#### Language Detection Function

def detect_output_language(user_message, book_title=None):
    """Detect output language"""
    
    # 1. Check explicit parameter
    if user_message.lang_param:
        return user_message.lang_param
    
    # 2. Detect user message language
    user_lang = detect_language(user_message.text)
    if user_lang in ['zh', 'zh-CN', 'zh-TW']:
        return 'zh'
    elif user_lang in ['ja']:
        return 'ja'
    elif user_lang in ['ko']:
        return 'ko'
    
    # 3. Detect book title language
    if book_title:
        book_lang = detect_language(book_title)
        if book_lang in ['zh', 'zh-CN', 'zh-TW']:
            return 'zh'
    
    # 4. Default to English
    return 'en'

#### Persona Prompt Templates (Multi-Language)

# Supported languages
SUPPORTED_LANGUAGES = ['en', 'zh', 'ja', 'ko', 'fr', 'de', 'es', 'pt', 'ru']
Fallback mechanism: if no complete template for a language, fallback to English
FALLBACK_LANG = 'en'
def get_persona_prompt(persona_key, language, book_info):
    """Get persona prompt in specified language"""
    
    # 1. Try to get template for specified language
    if language in PERSONA_PROMPTS:
        template = PERSONA_PROMPTS[language].get(persona_key)
        if template:
            return template.format(**book_info)
    
    # 2. Fallback to English template
    template = PERSONA_PROMPTS[FALLBACK_LANG].get(persona_key)
    return template.format(**book_info)
PERSONA_PROMPTS = {
    "en": {
        "axiom_analyst": """You are the 'Axiom Analyst', using axiomatic thinking to decompose book content.
Book: {book_title}
Author: {author}
Task: Reduce the book's core viewpoints to indivisible atomic propositions.
Workflow:
1. Strip appearances: Identify all packaging (stories, cases, metaphors), extract pure viewpoint kernels
2. Trace premises: Find underlying assumptions supporting core viewpoints, mark as \"A1, A2, A3...\"
3. Reverse decomposition: If this conclusion fails, which premises must be false?
4. Minimal expression: Summarize the book's core in one sentence (max 30 words)
5. Title analysis: Decode the metaphor system of the book title
Output Format:
Core Premises
[3-5 indivisible atomic propositions]
Underlying Assumptions
A1: [Assumption 1 - must be falsifiable]
A2: [Assumption 2 - must be falsifiable]
Title Metaphor Analysis
[Decode the book title's metaphor system]
One-Sentence Summary
> [Core viewpoint, max 30 words]
Word count: 800-1500 words
Important: Output analysis results directly, no opening or closing pleasantries.""",
        
        "lms_architect": """You are the 'L-M-S Architect', must output specific L-M-S structure.
Book: {book_title}
Author: {author}
Task: First introduce book content, then compress into reusable knowledge cards.
L-M-S Structure Definition:
Logic: Causal chain / derivation path of viewpoints
Method: Executable methodology / tools / frameworks
Summary: Minimal summary of core points (max 50 words)
Output Format:
Chapter Structure
| Part | Chapter | Theme | Core Content |
Character Relationship Network

Key Turning Points
| Event | Nature | Importance |
Logic (Causal Chain)
[Causal chain: A→B→C]
Method (Methodology)
[Executable methods or tools]
Summary
> [Core viewpoint, max 50 words]
Word count: 1000-2000 words""",
        
        "black_swan_hunter": """You are the 'Black Swan Hunter', looking for black swan events and boundary conditions.
Book: {book_title}
Author: {author}
Task: Identify edge cases and failure points of conclusions.
Workflow:
1. Find counterexamples: What known facts conflict with the book's viewpoints?
2. Boundary detection: Under what conditions does this viewpoint fail?
3. Assumption challenge: If underlying assumptions are false, do conclusions still hold?
4. Butterfly effect: What possible chain reactions are ignored?
5. Contemporary significance: Why should 2026 readers read this book?
Output Format:
Black Swan Events
[2-3 real cases or theoretical rebuttals conflicting with book's viewpoints]
Boundary Conditions
| Condition | Reason for Failure | Verification Method |
Assumption Vulnerability
[Chain reactions when underlying assumptions are challenged]
Contemporary Significance Challenge
[Why should 2026 readers read this book?]
Rebuttal and Defense
[Response to main criticisms, maintain dialectical balance]
Word count: 800-1500 words""",
        
        "random_variable_x": """You are 'Random Variable X', randomly loading an identity module.
Randomly selected identity: {random_role}
Focus: {role_focus}
Values: {role_values}
Book: {book_title}
Author: {author}
Task: Provide unique interpretation from the perspective of {random_role}.
Output Format:
🎲 Random Identity: {random_role}
Role Background
[Identity background, profession, values]
Unique Perspective
[What unique insights emerge when viewing the book from this perspective?]
> Must differ from other personas' perspectives, must produce cross-domain thinking
Core Questions
[3 key questions this role would ask]
1. [Question 1]
2. [Question 2]
3. [Question 3]
Cross-Domain Associations
[Connect the book with domain concepts, generate new understanding]
Word count: 600-1000 words"""
    },
    
    "zh": {
        "axiom_analyst": """你是「第一性原理师」，使用公理化思维分解书籍内容。
书籍：{book_title}
作者：{author}
任务：将书籍核心观点还原为不可再分的原子命题。
工作流程：
1. 剥离表象：识别所有包装（故事、案例、隐喻），提取纯观点内核
2. 追溯前提：找出支撑核心观点的底层假设，标记为\"A1, A2, A3...\"
3. 反向分解：如果这个结论失败，哪些前提必然为假？
4. 最小表达：用一句话总结本书核心（不超过30字）
5. 书名解析：解码书名本身的隐喻系统
输出格式：
核心前提
[3-5条不可再分解的原子命题]
底层假设
A1: [假设1 - 必须可被证伪]
A2: [假设2 - 必须可被证伪]
书名隐喻解析
[书名本身的隐喻系统]
一句话总结
> [核心观点，不超过30字]
字数要求：800-1500字
重要提醒：直接输出分析结果，不要有任何开场白或结尾客套话""",
        
        "lms_architect": """你是「结构化笔记官」，必须输出具体的 L-M-S 结构。
书籍：{book_title}
作者：{author}
任务：先介绍书籍内容，再压缩为可复用的知识卡片。
L-M-S 结构定义：
Logic：因果链 / 观点的推导路径
Method：可执行的方法论 / 工具 / 框架
Summary：核心点的最小摘要（不超过50字）
输出格式：
章节结构
| 部分 | 章节 | 主题 | 核心内容 |
人物关系网络

关键转折点
| 事件 | 转折性质 | 重要程度 |
Logic (逻辑链)
[因果链：A→B→C]
Method (方法论)
[可执行的方法或工具]
Summary (摘要)
> [核心观点，不超过50字]
字数要求：1000-2000字""",
        
        "black_swan_hunter": """你是「专业反驳者」，寻找「黑天鹅」事件和边界条件。
书籍：{book_title}
作者：{author}
任务：识别结论的边缘情况和失效点。
工作流程：
1. 找反例：有哪些已知事实与书中观点冲突？
2. 边界检测：在什么条件下，这个观点会失效？
3. 假设挑战：如果底层假设为假，结论是否仍然成立？
4. 蝴蝶效应：忽略了哪些可能的连锁反应？
5. 当代意义：2026年的读者为何要读这本书？
输出格式：
黑天鹅事件
[2-3个与书中观点冲突的真实案例或理论反驳]
边界条件
| 条件 | 观点失效原因 | 验证方式 |
假设脆弱性
[当底层假设被挑战时，会产生什么连锁反应？]
当代意义挑战
[2026年的读者为何要读这本书？]
反驳与辩护
[对主要批判的回应，保持辩证平衡]
字数要求：800-1500字""",
        
        "random_variable_x": """你是「随机变量 X」，随机加载一个身份模块。
本次随机选中的身份：{random_role}
关注点：{role_focus}
价值观：{role_values}
书籍：{book_title}
作者：{author}
任务：从{random_role}的视角提供独特解读。
输出格式：
🎲 随机身份：{random_role}
角色背景
[身份背景、职业、价值观]
独特视角
[从该视角看书会产生什么独特洞察？]
> 必须与其他角色的视角不同，必须产生跨界思考
核心问题
[该角色会提出的3个关键问题]
1. [问题1]
2. [问题2]
3. [问题3]
跨界联想
[将书籍与领域概念连接，产生新的理解]
字数要求：600-1000字"""
    },
    
    "ja": {
        "axiom_analyst": """あなたは「公理分析者」です。公理的思考を用いて書籍の内容を分解してください。
書籍：{book_title}
著者：{author}
タスク：書籍の核心的観点を分割不可能な原子命題に還元してください。
ワークフロー：
1. 表象を剥ぎ取る：すべての包装（物語、事例、隠喩）を識別し、純粋な観点の核を抽出
2. 前提を追跡する：核心的観点を支える基礎仮定を見つけ、「A1, A2, A3...」とマーク
3. 逆分解：この結論が失敗した場合、どの前提が偽でなければならないか？
4. 最小表現：本書の核心を一言で要約（30文字以内）
5. タイトル分析：書籍タイトルの隠喩システムを解読
出力形式：
核心前提
[3-5個の分割不可能な原子命題]
基礎仮定
A1: [仮定1 - 反証可能でなければならない]
A2: [仮定2 - 反証可能でなければならない]
タイトル隠喩分析
[書籍タイトルの隠喩システムを解読]
一言要約
> [核心的観点、30文字以内]
文字数：800-1500文字
重要：分析結果を直接出力し、冒頭や結びの挨拶は不要""",
        
        "lms_architect": """あなたは「構造化ノート作成者」です。具体的なL-M-S構造を出力してください。
書籍：{book_title}
著者：{author}
タスク：まず書籍の内容を紹介し、再利用可能な知識カードに圧縮してください。
L-M-S構造定義：
Logic：因果連鎖 / 観点の導出パス
Method：実行可能な方法論 / ツール / フレームワーク
Summary：核心の最小要約（50文字以内）
出力形式：
章構造
| 部分 | 章 | テーマ | 核心内容 |
人物関係ネットワーク

重要な転換点
| 出来事 | 転換の性質 | 重要度 |
Logic（論理連鎖）
[因果連鎖：A→B→C]
Method（方法論）
[実行可能な方法またはツール]
Summary（要約）
> [核心的観点、50文字以内]
文字数：1000-2000文字""",
        
        "black_swan_hunter": """あなたは「ブラックスワン探索者」です。ブラックスワン事象と境界条件を探してください。
書籍：{book_title}
著者：{author}
タスク：結論のエッジケースと失敗点を識別してください。
ワークフロー：
1. 反例を見つける：書籍の観点と矛盾する既知の事実は何か？
2. 境界検出：どのような条件下でこの観点は失敗するか？
3. 仮定挑戦：基礎仮定が偽の場合、結論は依然として成立するか？
4. バタフライ効果：どのような可能な連鎖反応が無視されているか？
5. 現代的意義：2026年の読者はなぜこの本を読むべきか？
出力形式：
ブラックスワン事象
[書籍の観点と矛盾する2-3個の実例または理論的反論]
境界条件
| 条件 | 失敗理由 | 検証方法 |
仮定の脆弱性
[基礎仮定が挑戦された時、どのような連鎖反応が生じるか？]
現代的意義の挑戦
[2026年の読者はなぜこの本を読むべきか？]
反論と弁護
[主な批判への対応、弁証法的バランスを維持]
文字数：800-1500文字""",
        
        "random_variable_x": """あなたは「確率変数X」です。ランダムにアイデンティティモジュールをロードします。
ランダムに選択されたアイデンティティ：{random_role}
焦点：{role_focus}
価値観：{role_values}
書籍：{book_title}
著者：{author}
タスク：{random_role}の視点から独自の解釈を提供してください。
出力形式：
🎲 ランダムアイデンティティ：{random_role}
役割の背景
[アイデンティティの背景、職業、価値観]
独自の視点
[この視点から書籍を見ると、どのような独自の洞察が生まれるか？]
> 他のペルソナの視点と異なり、クロスドメイン思考を生み出す必要がある
核心的質問
[この役割が提起する3つの重要な質問]
1. [質問1]
2. [質問2]
3. [質問3]
クロスドメイン連想
[書籍をドメイン概念と接続し、新しい理解を生み出す]
文字数：600-1000文字"""
    },
    
    "ko": {
        "axiom_analyst": """당신은 '공리 분석가'입니다. 공리적 사고를 사용하여 책 내용을 분해하세요.
책: {book_title}
저자: {author}
과제: 책의 핵심 관점을 더 이상 분해할 수 없는 원자 명제로 환원하세요.
워크플로:
1. 표면 벗기기: 모든 포장(이야기, 사례, 은유)을 식별하고 순수한 관점의 핵심 추출
2. 전제 추적: 핵심 관점을 지탱하는 기초 가정을 찾아 'A1, A2, A3...'로 표시
3. 역분해: 이 결론이 실패하면 어떤 전제가 거짓이어야 하는가?
4. 최소 표현: 책의 핵심을 한 문장으로 요약(30자 이내)
5. 제목 분석: 책 제목의 은유 시스템 해독
출력 형식:
핵심 전제
[3-5개의 분해 불가능한 원자 명제]
기초 가정
A1: [가정 1 - 반증 가능해야 함]
A2: [가정 2 - 반증 가능해야 함]
제목 은유 분석
[책 제목의 은유 시스템 해독]
한 문장 요약
> [핵심 관점, 30자 이내]
글자 수: 800-1500자
중요: 분석 결과를 직접 출력하고, 시작이나 끝의 인사말은 필요 없음""",
        
        "lms_architect": """당신은 '구조화 노트 작성자'입니다. 구체적인 L-M-S 구조를 출력하세요.
책: {book_title}
저자: {author}
과제: 먼저 책 내용을 소개하고, 재사용 가능한 지식 카드로 압축하세요.
L-M-S 구조 정의:
Logic: 인과 연쇄 / 관점의 도출 경로
Method: 실행 가능한 방법론 / 도구 / 프레임워크
Summary: 핵심의 최소 요약(50자 이내)
출력 형식:
장 구조
| 부분 | 장 | 주제 | 핵심 내용 |
인물 관계 네트워크

핵심 전환점
| 사건 | 전환 성격 | 중요도 |
Logic (논리 연쇄)
[인과 연쇄: A→B→C]
Method (방법론)
[실행 가능한 방법 또는 도구]
Summary (요약)
> [핵심 관점, 50자 이내]
글자 수: 1000-2000자""",
        
        "black_swan_hunter": """당신은 '블랙 스왐 탐색자'입니다. 블랙 스왐 사건과 경계 조건을 찾으세요.
책: {book_title}
저자: {author}
과제: 결론의 엣지 케이스와 실패 지점을 식별하세요.
워크플로:
1. 반례 찾기: 책의 관점과 충돌하는 알려진 사실은 무엇인가?
2. 경계 검출: 어떤 조건에서 이 관점이 실패하는가?
3. 가정 도전: 기초 가정이 거짓이면 결론이 여전히 성립하는가?
4. 나비 효과: 어떤 가능한 연쇄 반응이 무시되고 있는가?
5. 현대적 의미: 2026년 독자가 왜 이 책을 읽어야 하는가?
출력 형식:
블랙 스왐 사건
[책의 관점과 충돌하는 2-3개의 실제 사례 또는 이론적 반박]
경계 조건
| 조건 | 실패 이유 | 검증 방법 |
가정의 취약성
[기초 가정이 도전받을 때 어떤 연쇄 반응이 일어나는가?]
현대적 의미 도전
[2026년 독자가 왜 이 책을 읽어야 하는가?]
반박과 변호
[주요 비판에 대한 대응, 변증법적 균형 유지]
글자 수: 800-1500자""",
        
        "random_variable_x": """당신은 '확률 변수 X'입니다. 무작위로 정체성 모듈을 로드합니다.
무작위로 선택된 정체성: {random_role}
초점: {role_focus}
가치관: {role_values}
책: {book_title}
저자: {author}
과제: {random_role}의 관점에서 독특한 해석을 제공하세요.
출력 형식:
🎲 무작위 정체성: {random_role}
역할 배경
[정체성 배경, 직업, 가치관]
독특한 관점
[이 관점에서 책을 보면 어떤 독특한 통찰이 생기는가?]
> 다른 페르소나의 관점과 달라야 하며, 크로스 도메인 사고를 생성해야 함
핵심 질문
[이 역할이 제기할 3가지 핵심 질문]
1. [질문 1]
2. [질문 2]
3. [질문 3]
크로스 도메인 연상
[책을 도메인 개념과 연결하여 새로운 이해 생성]
글자 수: 600-1000자"""
    }
}
Additional languages (French, German, Spanish, Portuguese, Russian) use English template as base
But require output in corresponding language
ADDITIONAL_LANG_INSTRUCTIONS = {
    "fr": "Important: Output all analysis in French (Français).",
    "de": "Important: Output all analysis in German (Deutsch).",
    "es": "Important: Output all analysis in Spanish (Español).",
    "pt": "Important: Output all analysis in Portuguese (Português).",
    "ru": "Important: Output all analysis in Russian (Русский)."
}
def get_persona_prompt(persona_key, language, book_info):
    """
    Get persona prompt in specified language
    
    Supported languages:
    - en (English) - Complete template
    - zh (Chinese) - Complete template
    - ja (Japanese) - Complete template
    - ko (Korean) - Complete template
    - fr (French) - English template + French output instruction
    - de (German) - English template + German output instruction
    - es (Spanish) - English template + Spanish output instruction
    - pt (Portuguese) - English template + Portuguese output instruction
    - ru (Russian) - English template + Russian output instruction
    - Other languages - English template
    """
    
    # 1. Try to get complete template for specified language
    if language in PERSONA_PROMPTS:
        template = PERSONA_PROMPTS[language].get(persona_key)
        if template:
            return template.format(**book_info)
    
    # 2. For other supported languages, use English template + language instruction
    template = PERSONA_PROMPTS[FALLBACK_LANG].get(persona_key)
    base_prompt = template.format(**book_info)
    
    # Add language output instruction
    if language in ADDITIONAL_LANG_INSTRUCTIONS:
        base_prompt += f"\n\n{ADDITIONAL_LANG_INSTRUCTIONS[language]}"
    
    return base_prompt

#### Parallel Execution Code (with Language Detection)

# 1. Detect output language
output_lang = detect_output_language(user_message, book_title)
2. Get prompt template for the language
prompts = PERSONA_PROMPTS.get(output_lang, PERSONA_PROMPTS["en"])
3. Launch 4 persona analyses in parallel
personas = [
    {"key": "axiom_analyst", "name": "Axiom Analyst" if output_lang == "en" else "第一性原理师"},
    {"key": "lms_architect", "name": "L-M-S Architect" if output_lang == "en" else "结构化笔记官"},
    {"key": "black_swan_hunter", "name": "Black Swan Hunter" if output_lang == "en" else "黑天鹅猎手"},
    {"key": "random_variable_x", "name": "Random Variable X" if output_lang == "en" else "随机变量 X"}
]
4. Execute in parallel using sessions_spawn (with language parameter)
for persona in personas:
    prompt_template = prompts[persona["key"]]
    task_prompt = prompt_template.format(
        book_title=book_title,
        author=author,
        random_role=random_role,
        role_focus=role_focus,
        role_values=role_values
    )
    
    sessions_spawn(
        task=task_prompt,
        mode="run",
        runtime="subagent"
    )
5. Wait for all subagents to complete
sessions_yield(message=f"Waiting for all persona analyses to complete (Output language: {output_lang})")

#### OpenClaw Tool Call Example (English User)

# User message: "Please deep read The Three-Body Problem"
Detected language: en
1. Launch Axiom Analyst (English prompt)
sessions_spawn(
  task: "You are the 'Axiom Analyst', using axiomatic thinking to decompose book content. Book: The Three-Body Problem...",
  mode: "run",
  runtime: "subagent"
)
2. Launch L-M-S Architect (English prompt)
sessions_spawn(
  task: "You are the 'L-M-S Architect', must output specific L-M-S structure. Book: The Three-Body Problem...",
  mode: "run",
  runtime: "subagent"
)
3. Launch Black Swan Hunter (English prompt)
sessions_spawn(
  task: "You are the 'Black Swan Hunter', looking for black swan events and boundary conditions...",
  mode: "run",
  runtime: "subagent"
)
4. Launch Random Variable X (English prompt)
sessions_spawn(
  task: "You are 'Random Variable X', randomly loading an identity module...",
  mode: "run",
  runtime: "subagent"
)
5. Wait for all results
sessions_yield(message: "Waiting for all persona analyses to complete (Output language: en)")

#### OpenClaw Tool Call Example (Chinese User)

# User message: "深度阅读《三体》"
Detected language: zh
1. Launch 第一性原理师 (Chinese prompt)
sessions_spawn(
  task: "你是「第一性原理师」，使用公理化思维分解书籍内容。书籍：《三体》...",
  mode: "run",
  runtime: "subagent"
)
2. Launch 结构化笔记官 (Chinese prompt)
sessions_spawn(
  task: "你是「结构化笔记官」，必须输出具体的 L-M-S 结构...",
  mode: "run",
  runtime: "subagent"
)
3. Launch 黑天鹅猎手 (Chinese prompt)
sessions_spawn(
  task: "你是「专业反驳者」，寻找「黑天鹅」事件和边界条件...",
  mode: "run",
  runtime: "subagent"
)
4. Launch 随机变量 X (Chinese prompt)
sessions_spawn(
  task: "你是「随机变量 X」，随机加载一个身份模块...",
  mode: "run",
  runtime: "subagent"
)
5. Wait for all results
sessions_yield(message: "等待所有角色分析完成（输出语言：zh）")

#### 并行处理流程图

┌─────────────────────────────────────────────────────────────────┐
│                    Step 3: 并行四维分析                         │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌────────┐│
│   │ Axiom       │  │ L-M-S       │  │ Black Swan  │  │ Random ││
│   │ Analyst     │  │ Architect   │  │ Hunter      │  │ Var X  ││
│   └──────┬──────┘  └──────┬──────┘  └──────┬──────┘  └───┬────┘│
│          │                │                │             │      │
│          └────────────────┴────────────────┴─────────────┘      │
│                                    │                            │
│                                    ▼                            │
│                          ┌─────────────────┐                    │
│                          │  结果聚合       │                    │
│                          │  Synthesis      │                    │
│                          └─────────────────┘                    │
└─────────────────────────────────────────────────────────────────┘
时间对比：
顺序执行：~4T（每个角色耗时 T）
并行执行：~T（所有角色同时完成）
效率提升：约 75%

Step 4: H-V Analysis (Optional)

If user requests enhanced mode:

Step 5: Synthesis and Save

1. Aggregate all persona outputs 2. Generate complete Markdown report 3. Save to workspace/reports/ 4. Return report path to user

Quality Assessment Module

Analysis Quality Metrics

| Dimension | Metric | Weight | |-----------|--------|--------| | Depth | Atomic proposition count | 25% | | Structure | L-M-S completeness | 20% | | Criticality | Edge case count | 20% | | Diversity | Random persona insight uniqueness | 15% | | Context | H-V analysis coverage | 20% |

Quality Score Calculation

def calculate_quality_score(analysis_result):
    """Calculate overall quality score (0-100)"""
    
    scores = {
        "depth": count_atomic_propositions(analysis_result) * 5,  # max 25
        "structure": assess_lms_completeness(analysis_result) * 20,  # max 20
        "criticality": count_edge_cases(analysis_result) * 4,  # max 20
        "diversity": assess_insight_uniqueness(analysis_result) * 15,  # max 15
        "context": assess_hv_coverage(analysis_result) * 20  # max 20
    }
    
    return sum(scores.values())  # max 100

Quality Report Output

## 📊 Analysis Quality Report
| Dimension | Score | Max |
|-----------|-------|-----|
| Depth | 22 | 25 |
| Structure | 18 | 20 |
| Criticality | 16 | 20 |
| Diversity | 12 | 15 |
| Context | 18 | 20 |
| Total | 86 | 100 |
Quality Assessment
✅ Excellent depth analysis
✅ Well-structured notes
⚠️ Consider more edge cases
✅ Unique persona perspective
✅ Comprehensive H-V analysis

Usage Examples

Example 1: Book Title Only

User: Use deep reading to analyze "Atomic Habits"
→ Triggers Method A (web search)
→ Retrievees metadata from Douban, Google Books, Wikipedia
→ Runs 4-persona analysis
→ Saves report to reports/Atomic_Habits_Analysis_2026-04-26.md

Example 2: Local File

User: Deep read this file: /path/to/book.pdf
→ Triggers Method B (format parsing)
→ Parses PDF to text
→ Runs 4-persona analysis
→ Saves report

Example 3: Link

User: Analyze this book from a public link
→ Triggers Method C (Link retrieve)
→ Saves and parses
→ Runs 4-persona analysis
→ Saves report

🌐 Language Output Module

Supported Languages

| Language | Code | Native Name | |----------|------|-------------| | English | en | English | | Chinese (Simplified) | zh-CN | 简体中文 | | Chinese (Traditional) | zh-TW | 繁體中文 | | Japanese | ja | 日本語 | | Korean | ko | 한국어 | | French | fr | Français | | German | de | Deutsch | | Spanish | es | Español | | Portuguese | pt | Português | | Russian | ru | Русский |

Language Detection Logic

def detect_output_language(user_input, book_language=None):
    """Determine output language based on context"""
    
    # Priority order:
    # 1. Explicit language parameter
    # 2. User's message language
    # 3. Book's original language
    # 4. Default (English)
    
    if user_input.language_param:
        return user_input.language_param
    
    detected = detect_language(user_input.text)
    if detected in SUPPORTED_LANGUAGES:
        return detected
    
    if book_language:
        return book_language
    
    return 'en'  # Default

Language-Specific Output Templates

#### English Template

## 🔬 Axiom Analyst
Core Premises
[Atomic propositions in English]
Underlying Assumptions
A1: [Assumption 1]
A2: [Assumption 2]

#### Chinese Template (Simplified)

## 🔬 第一性原理师
核心前提
[原子命题，中文表达]
底层假设
A1: [假设 1]
A2: [假设 2]

#### Japanese Template

## 🔬 公理分析者
核心前提
[原子命題、日本語で]
基礎仮定
A1: [仮定 1]
A2: [仮定 2]

Persona Name Localization

| Persona | English | Chinese | Japanese | Korean | |---------|---------|---------|----------|--------| | Axiom Analyst | Axiom Analyst | 第一性原理师 | 公理分析者 | 공리 분석가 | | L-M-S Architect | L-M-S Architect | 结构化笔记官 | 構造化ノート作成者 | 구조화 노트 작성자 | | Black Swan Hunter | Black Swan Hunter | 黑天鹅猎手 | ブラックスワン探索者 | 블랙 스왐 탐색자 | | Random Variable X | Random Variable X | 随机变量 X | 確率変数 X | 확률 변수 X |

Section Header Localization

| Section | English | Chinese | Japanese | |---------|---------|---------|----------| | Core Premises | Core Premises | 核心前提 | 核心前提 | | Underlying Assumptions | Underlying Assumptions | 底层假设 | 基礎仮定 | | Logic | Logic | 逻辑 | 論理 | | Method | Method | 方法 | 方法 | | Summary | Summary | 摘要 | 要約 | | Black Swan Events | Black Swan Events | 黑天鹅事件 | ブラックスワン事件 | | Edge Cases | Edge Cases | 边界条件 | 境界条件 | | Diachronic Analysis | Diachronic Analysis | 纵向分析 | 縦断分析 | | Synchronic Analysis | Synchronic Analysis | 横向分析 | 横断分析 |

Usage Examples with Language

# English output (default)
User: Analyze "Atomic Habits" with deep reading
Chinese output
User: 用深度阅读分析《原子习惯》
User: Analyze "Atomic Habits" --lang zh-CN
Japanese output
User: 深読みで「アトミック・ハビッツ」を分析して
User: Analyze "Atomic Habits" --lang ja
Explicit language parameter
User: Analyze "三体" --lang en  # Chinese book, English output

Language-Aware Report Naming

def generate_report_filename(book_name, language, date):
    """Generate language-aware filename"""
    
    lang_suffix = {
        'en': '',
        'zh-CN': '_中文',
        'zh-TW': '_繁體',
        'ja': '_日本語',
        'ko': '_한국어'
    }.get(language, '')
    
    return f"{book_name}_Deep_Analysis{lang_suffix}_{date}.md"

Configuration

Environment Variables

DEEP_READER_REPORTS_DIR=/root/.openclaw/workspace/reports
DEEP_READER_TEMP_DIR=/tmp/deep-reader
DEEP_READER_DEFAULT_MODE=hv-enhanced  # standard | hv-enhanced
DEEP_READER_DEFAULT_LANG=auto  # auto | en | zh-CN | ja | ko | ...

Skill Parameters

| Parameter | Default | Description | |-----------|---------|-------------| | mode | hv-enhanced | Analysis mode: speed | standard | hv-enhanced | | lang | auto | Output language (auto-detect if auto) | | save_report | true | Auto-save report | | random_persona_category | all | Restrict random persona pool | | quality_threshold | 70 | Minimum quality score to pass |

Mode Comparison

| Mode | Time | Personas | Output | Best For | |------|------|----------|--------|----------| | speed | ~30s | Axiom Analyst only | Core premises + Summary + Questions | Quick screening | | standard | ~2min | All 4 personas | Full 4-persona analysis | Deep understanding | | hv-enhanced | ~5min | All 4 + H-V | 4-persona + Horizontal-Vertical | Comprehensive research |

Dependencies & Installation

Core Dependencies (Auto-installed with skill)

| Dependency | Purpose | Notes | |------------|---------|-------| | web_tool | Retrieve web content | Built-in OpenClaw tool | | search_tool | Search book resources | Built-in skill |

File Parsing Dependencies (Optional)

All file parsing uses pure Python libraries - no system binaries required.

| Format | Library | Install | Notes | |--------|---------|---------|-------| | TXT | None | - | ✅ Always available | | MD | None | - | ✅ Always available | | PDF | pdfplumber | pip install pdfplumber | Pure Python, no system deps | | EPUB | ebooklib, beautifulsoup4 | pip install ebooklib beautifulsoup4 | Pure Python | | MOBI | Convert to EPUB first | - | Use online converter |

Quick Install (for local file parsing)

pip install pdfplumber ebooklib beautifulsoup4 lxml

Or install from requirements.txt:

pip install -r requirements.txt

Dependency Check

python3 -c "import pdfplumber; print('PDF OK')"
python3 -c "import ebooklib; print('EPUB OK')"

Usage Examples

Example 1: Speed Reading Mode (Quick Analysis)

User: 速读《原子习惯》
User: Speed read "Atomic Habits"
User: 快速阅读《三体》
→ Triggers Speed Reading Mode
→ Uses only Axiom Analyst persona
→ Output: Core premises + One-sentence summary + Key questions
→ Time: ~30 seconds
→ Saves mini-report to reports/_Speed_Read_.md

Example 2: Book Title Only (Web Search)

User: Analyze "Atomic Habits" with deep reading
User: Use four-dimensional deep reading on "Tao Te Ching"
User: 用四维深度阅读分析《漫长的旅途》
→ Triggers Method A (web search)
→ Retrievees metadata from Douban, Google Books, Wikipedia
→ Runs 4-persona analysis
→ Saves report to reports/_Deep_Analysis_.md

Example 2: Local TXT File

User: Deep read this file: /path/to/book.txt
User: 分析这个文件：/home/user/我的书.txt
→ Triggers Method B (format parsing)
→ Auto-detects encoding (UTF-8/GBK/GB2312/Big5)
→ Runs 4-persona analysis
→ Saves report

Example 3: Local Markdown File

User: Analyze /path/to/notes.md with deep reading
User: 深度阅读这个 Markdown：./chapter1.md
→ Triggers Method B (format parsing)
→ Detects ## headers as chapter markers
→ Runs 4-persona analysis

Example 4: PDF File (requires pdftotext)

User: Deep read /path/to/book.pdf
User: 分析 PDF：./saves/ebook.pdf
→ Triggers Method B (PDF parsing)
→ Uses pdftotext to extract text with layout preservation
→ Detects chapter markers
→ Runs 4-persona analysis

Example 5: EPUB File (requires ebooklib)

User: Analyze /path/to/book.epub with four-dimensional deep reading
User: 深度阅读 EPUB：./books/novel.epub
→ Triggers Method B (EPUB parsing)
→ Extracts HTML content from EPUB
→ Preserves chapter structure
→ Runs 4-persona analysis

Example 6: Link (Direct Retrieve)

User: Analyze this book from a public link
User: 深度阅读这个在线电子书链接
→ Triggers Method C (Link retrieve)
→ Saves to workspace
→ Detects file type from Content-Type header
→ Parses and analyzes
→ Cleans up temp file

Example 7: With Language Parameter

User: Analyze "三体" --lang en        # Chinese book, English output
User: 分析 "Atomic Habits" --lang zh-CN  # English book, Chinese output
User: 深読みで「道徳経」--lang ja       # Japanese output

Example 8: With Mode Parameter

User: Analyze "1984" --mode speed        # Speed reading (~30s)
User: 分析《红楼梦》--mode standard     # Standard 4-persona
User: 深度阅读《三体》--mode hv-enhanced  # Full H-V analysis
User: 速读《原子习惯》                    # Speed mode (trigger keyword)

✅ 质检清单 (Quality Checklist)

报告生成后必须通过以下质检项，不合格则返工修正：

一、角色输出完整性

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 第一性原理师 | 800-1500字 | 包含核心前提、底层假设、书名隐喻、一句话总结 | | 结构化笔记师 | 1000-2000字 | 包含章节结构、人物网络、转折点、L-M-S | | 黑天鹅猎手 | 800-1500字 | 包含黑天鹅事件、边界条件、假设脆弱性 | | 随机变量X | 600-1000字 | 包含角色背景、独特视角、核心问题、跨界联想 |

二、内容质量标准

| 检查项 | 标准 | 检查方法 | |--------|------|----------| | 原子命题不可再分 | 每条核心前提都是最小单元 | 尝试拆分，无法拆分则通过 | | 假设可证伪 | 每条底层假设都可被证伪 | 存在反例则通过 | | 反驳有事实支撑 | 每个黑天鹅事件有来源 | 标注来源则通过 | | 边界条件可验证 | 每个边界条件可被检验 | 存在检验方法则通过 | | 视角真正独特 | 随机角色视角不与前三个重复 | 无重复观点则通过 |

三、横纵分析质量（H-V模式）

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 纵向叙事完整 | 有起源、演进、阶段划分 | 故事线完整则通过 | | 横向对比充分 | 至少对比3个同类作品 | 对比表完整则通过 | | 交汇洞察新颖 | 不是前文内容的缩写 | 有新判断则通过 |

四、写作风格检查

| 检查项 | 禁止 | 检查方法 | |--------|------|----------| | 无AI套话 | "首先...其次...最后"、"综上所述" | 未出现则通过 | | 无空洞词 | "赋能"、"抓手"、"打造闭环" | 未出现则通过 | | 无教科书开头 | "在当今...的时代"、"随着...的发展" | 未出现则通过 | | 无高频踩雷 | "说白了"、"意味着什么？"、"本质上" | 出现<2次则通过 |

五、信息来源标注

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 关键事实有来源 | 重要论断标注出处 | 有来源链接则通过 | | 暂缺信息诚实标注 | 不编造无法获取的信息 | 标注"暂缺"则通过 |

六、输出路径检查

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 文件保存目录 | 必须在 /root/.openclaw/workspace/reports/ | 目录正确则通过 | | 文件命名规范 | 格式：[书名]_[模式]_[日期].md | 命名规范则通过 | | 文件存在性 | 报告生成后文件必须实际存在 | 文件存在则通过 | | 文件非空 | 文件大小 > 1KB | 有内容则通过 |

质检评分计算

def calculate_quality_score(report):
    """计算质检总分 (0-100)"""
    
    scores = {
        "角色完整性": check_persona_completeness(report) * 25,  # max 25
        "内容质量": check_content_quality(report) * 25,  # max 25
        "横纵分析": check_hv_quality(report) * 20,  # max 20
        "写作风格": check_writing_style(report) * 15,  # max 15
        "来源标注": check_source_attribution(report) * 15  # max 15
    }
    
    total = sum(scores.values())
    return {
        "scores": scores,
        "total": total,
        "passed": total >= 70  # 70分及格
    }

质检报告输出模板

## 📊 质检报告
| 检查维度 | 得分 | 满分 | 状态 |
|----------|------|------|------|
| 角色完整性 | 23 | 25 | ✅ |
| 内容质量 | 22 | 25 | ✅ |
| 横纵分析 | 18 | 20 | ✅ |
| 写作风格 | 14 | 15 | ✅ |
| 来源标注 | 13 | 15 | ⚠️ |
| 总分 | 90 | 100 | ✅ 通过 |
改进建议
⚠️ 来源标注：建议补充更多数据来源

📁 报告生成检查 (Report Generation Check) - v1.7.2

报告生成后必须确认以下内容：

一、文件保存检查

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 文件存在 | 报告文件已创建 | ls workspace/reports/ 存在则通过 | | 命名规范 | 格式：书名-四维深度阅读-YYYY-MM-DD.md | 符合格式则通过 | | 内容非空 | 文件大小 > 1KB | 文件不为空则通过 |

二、仲裁者总结检查 (v1.7.2新增)

| 检查项 | 标准 | 通过条件 | |--------|------|----------| | 核心共识存在 | 包含 ## 核心共识 或 ## 🎯 | 找到则通过 | | 分歧分析存在 | 包含 ## 分歧 或 ## ⚔️ | 找到则通过 | | 加权可信度 | 包含权重乘数说明 | 包含 BS×1.5 等则通过 | | 行动清单 | 包含 4 项检查 (Verify/Question/Apply/Avoid) | 4项齐全则通过 | | 最终评判 | 包含一句话总结 | 找到 [Final Verdict] 或 [最终评判] 则通过 |

三、报告完整性快速检查

# 脚本：report_check.sh
#!/bin/bash
REPORT_FILE="$1"
echo "=== 报告生成检查 ==="
1. 文件存在
if [ -f "$REPORT_FILE" ]; then
    echo "✅ 文件存在: $REPORT_FILE"
else
    echo "❌ 文件不存在: $REPORT_FILE"
    exit 1
fi
2. 包含4个角色
for role in "第一性原理师" "结构化笔记官" "黑天鹅猎手" "随机变量"; do
    if grep -q "$role\|Axiom\|LMS\|Black Swan" "$REPORT_FILE"; then
        echo "✅ 包含角色: $role"
    else
        echo "⚠️ 缺少角色: $role"
    fi
done
3. 包含仲裁者总结 (v1.7.2)
if grep -q "仲裁者\|Arbiter\|核心共识\|Consensus" "$REPORT_FILE"; then
    echo "✅ 包含仲裁者总结"
else
    echo "⚠️ 缺少仲裁者总结 (v1.7.2)"
fi
echo "=== 检查完成 ==="

四、多语言适配检查

| 语言 | 检查项 | |------|--------| | 中文 (zh) | 包含"仲裁者"、"核心共识"、"分歧" | | 英文 (en) | 包含"Arbiter"、"Consensus"、"Dissent" | | 日文 (ja) | 包含"仲裁者"、"コア"、"不合" | | 韩文 (ko) | 包含"중재자"、"합의"、"不合" |

📈 Performance Metrics (v1.5.0)

Data Fetching Success Rate

| Metric | Before | After | Improvement | |--------|--------|-------|-------------| | Success Rate | ~70% | ~95% | +25% | | Avg Response Time | 3-5s | 1-2s (cached) | -60% | | Retry Coverage | 0% | 100% | +100% | | Cache Hit Rate | 0% | ~40% | +40% |

Cache Statistics

# 查看缓存统计
from book_fetcher_enhanced import get_cache_stats
stats = get_cache_stats()
输出: {"total": 15, "size_mb": 0.05}

File Format Details

TXT File Parsing

Markdown File Parsing

PDF File Parsing

EPUB File Parsing

MOBI File Parsing

License

MIT License

Changelog

v1.7.5 (2026-05-06)

v1.7.4 (2026-05-06)

v1.7.3 (2026-05-03)

v1.7.2 (2026-05-02)

v1.7.1 (2026-04-30)

v1.7.0 (2026-04-28)

v1.6.0 (2026-04-28)

v1.5.0 (2026-04-27)

v1.4.4 (2026-04-27)

v1.2.0 (2026-04-26)

v1.1.0 (2026-04-26)

v1.0.0 (2026-04-26)

Environment Variables

DEEP_READER_REPORTS_DIR=/root/.openclaw/workspace/reports
DEEP_READER_TEMP_DIR=/tmp/deep-reader
DEEP_READER_DEFAULT_MODE=hv-enhanced  # standard | hv-enhanced
DEEP_READER_DEFAULT_LANG=auto  # auto | en | zh-CN | ja | ko | ...

Skill Parameters

| Parameter | Default | Description | |-----------|---------|-------------| | mode | hv-enhanced | Analysis mode: speed | standard | hv-enhanced | | lang | auto | Output language (auto-detect if auto) | | save_report | true | Auto-save report | | random_persona_category | all | Restrict random persona pool | | quality_threshold | 70 | Minimum quality score to pass |

Mode Comparison

| Mode | Time | Personas | Output | Best For | |------|------|----------|--------|----------| | speed | ~30s | Axiom Analyst only | Core premises + Summary + Questions | Quick screening | | standard | ~2min | All 4 personas | Full 4-persona analysis | Deep understanding | | hv-enhanced | ~5min | All 4 + H-V | 4-persona + Horizontal-Vertical | Comprehensive research |