name: value-mining-lengthybooks description: Extract actionable insights from books using Four-Layer Methodology: (1) Skeleton - conceptual frameworks and mental models, (2) Flesh - 2-3 detailed case studies including original examples, cross-industry analogies, and real-world applications, (3) Essence - cross-industry migration matrices with specific industry adaptations and 3-5 step executable SOPs, (4) Residue - critical analysis of boundaries, limitations, and failure conditions. Dual processing modes: Quick (5 core points, 10-15 min) for rapid assessment and Deep (10-20 comprehensive points, 30-45 min) for systematic learning. Includes Feynman validation testing with scenario-based problems and scoring rubrics. Generates structured reports in Markdown/PDF/Word formats. Use when user requests systematic knowledge extraction, concept distillation, or implementation guidance from methodology/business/psychology/self-help books with emphasis on practical application and cross-domain transfer. version: 1.0.0 metadata: {"openclaw": {"emoji": "📚", "os": ["darwin", "linux", "win32"], "homepage": "https://github.com/your-repo/value-mining-lengthybooks"}}

ValueMining-Lengthybooks - Advanced Book Value Extraction System

A sophisticated knowledge extraction framework that transforms lengthy books into actionable business intelligence through a rigorous Four-Layer Methodology. This system systematically deconstructs methodology, thinking model, and skill-building books into transferable insights with measurable implementation pathways.

Core Methodology: Four-Layer Extraction Framework

Layer 1: Skeleton Extraction - Conceptual Foundation

Objective: Precisely define core conceptual frameworks and mental models

Systematic Approach:

1. Concept Hierarchy Mapping

   • Primary concepts and sub-concepts identification
   • Relationship mapping between concepts (parent-child, parallel, sequential)
   • Dependency analysis (which concepts depend on others)
   • Taxonomy creation for knowledge organization

2. Framework Structure Analysis

   • Core principles and axioms extraction
   • Dimension identification (e.g., time, scope, impact)
   • Decision criteria and success factors
   • Boundary conditions and applicability limits

3. Mental Model Decomposition

   • Underlying cognitive patterns
   • Assumption surfaces and implicit beliefs
   • Heuristic extraction (rules of thumb)
   • Bias identification within the framework

Output Format:

Concept Name: [Clear definition]
├── Core Principles: [List of fundamental principles]
├── Key Dimensions: [Major aspects/variations]
├── Dependencies: [Prerequisite concepts]
├── Applications: [Typical use cases]
└── Limitations: [Boundary conditions]

Layer 2: Flesh Mining - Case Study Analysis

Objective: Provide 2-3 detailed case studies demonstrating practical application