book-to-skills

name	book-to-skills
description	Use when the user wants to turn a book into reusable skills, methodology documents, or a book-derived thinking system for Claude Code, OpenClaw, or similar agent environments. Trigger on phrases like "从书生成skill" or "book to skill" or when user wants to extract methodologies from a book.

Book to Skills

Overview

Transform a book into reusable, evidence-based skills that an agent can actually call. This skill enforces systematic extraction—not summary generation—to produce a structured bundle including author thinking models, methodology cards, routing rules, and execution-ready Markdown outputs.

Core principle: Every conclusion must have evidence from the book. No guesswork, no relying on training data about the book.

When to Use

Use this skill when:

• User says "帮我从《书名》生成skill"
• User wants to extract methodologies, frameworks, or thinking patterns from a book
• User needs a book-derived thinking system for agent environments
• User wants to convert book content into reusable MethodCards

Do NOT use when:

• User just wants a book summary or review
• User wants subjective interpretation without evidence
• The book has no clear methodology or framework

Decision Flowchart

digraph usage_decision {
  "User mentions book + skill/methodology?" [shape=diamond];
  "User wants summary only?" [shape=diamond];
  "Book has extractable methods?" [shape=diamond];
  "Use book-to-skills" [shape=box,style=filled,fillcolor=lightgreen];
  "Refuse or redirect" [shape=box,style=filled,fillcolor=lightcoral];
  "Proceed with summary" [shape=box,style=filled,fillcolor=lightyellow];

  "User mentions book + skill/methodology?" -> "User wants summary only?" [label="yes"];
  "User wants summary only?" -> "Proceed with summary" [label="yes"];
  "User wants summary only?" -> "Book has extractable methods?" [label="no"];
  "Book has extractable methods?" -> "Use book-to-skills" [label="yes"];
  "Book has extractable methods?" -> "Refuse or redirect" [label="no"];
}

Outcomes

This skill produces a structured bundle that helps an agent answer:

1. Should this book's thinking style be applied to the user's problem?
2. Which methodology modules from the book are relevant?
3. Which subagents should be assigned?
4. Which Markdown documents should be produced?

Red Flags - STOP and Reassess

• Thinking "I know this book" → STOP. Use NotebookLM to query the actual content, not your training data.
• Creating content without evidence → STOP. Every MethodCard needs evidence references.
• Skipping the five-stage query process → STOP. This ensures systematic extraction.
• Producing just a summary → STOP. This skill creates executable skills, not summaries.

Required Inputs

Collect or infer a minimal BookBrief:

title: string
author: string
domain: string
audience: string
goal: string
language: zh | en

If some fields are missing, continue with reasonable assumptions and record them in the output.

Default Workflow

Phase 1: Setup (Sequential)

1. Create NotebookLM notebook for the selected book
2. Verify source processing before proceeding

Phase 2: Parallel Extraction (Parallel Execution)

Execute these packs in parallel where dependencies allow:

┌─ Pack 1: 全书结构图 ─┐
│    (Book Profiler)   │
│         ↓            │
│   00-overview.md     │
└──────────────────────┘
                       ↘
                        ┌─ Pack 3: 方法论抽取 ─┐
                        │  (Methodology Miner) │
                        │       ↓              │
                        │   MethodCards[]      │
                        └──────────────────────┘
                       ↗                         ↘
┌─ Pack 2: 作者思维 ───┐                         ┌─ Pack 4: 场景路由 ─┐
│(Author Thinking     │                         │ (Scenario Router)  │
│     Analyst)         │                         │       ↓            │
│         ↓            │                         │ 03-scenario-router │
│ 01-author-thinking   │                         └────────────────────┘
└──────────────────────┘                                          ↘
                                                                    ↘
                        ┌─ Pack 5: 冲突校验与综合 ─────────────────────┐
                        │        (Skill Packager / QA)               │
                        │                    ↓                        │
                        │        Final Skill Bundle                   │
                        └─────────────────────────────────────────────┘

Parallel Rules:

• Pack 1 & Pack 2: Execute in parallel (no dependencies)
• Pack 3: Starts after Pack 1 AND Pack 2 complete
• Pack 4: Starts after Pack 3 completes
• Pack 5: Starts after Pack 4 completes, validates all outputs

Phase 3: Packaging (Sequential)

6. Assign subagents using references/subagent-playbooks.md
7. Package into Markdown output bundle per references/output-schemas.md
8. Prepare infographic structure (optional) using references/visual-infographic-spec.md

Parallel Agent Orchestration

Agent Dependency Graph

Agent	Dependencies	Parallel Groups
Book Profiler	None	Group A
Author Thinking Analyst	None	Group A
Methodology Miner	Pack 1, Pack 2 outputs	Group B
Scenario Router	MethodCards[]	Group C
Skill Packager / QA	All outputs	Group D (final)

Execution Protocol

1. Launch Group A agents simultaneously

   • Both agents query NotebookLM independently
   • Outputs: 00-overview.md, 01-author-thinking.md

2. Merge Group A outputs before Group B

   • Combine book structure + author thinking model
   • Pass to Methodology Miner as unified context

3. Group B: Methodology extraction

   • Can split into 2 parallel sub-agents:
     • Sub-agent B1: Extract explicit methods (named frameworks)
     • Sub-agent B2: Extract implicit methods (recurring patterns)
   • Merge results into single MethodCard[]

4. Group C: Routing logic

   • Single agent, processes all MethodCards
   • Output: 03-scenario-router.md

5. Group D: Validation and packaging

   • Parallel validation tasks:
     • Validate evidence completeness per MethodCard
     • Check cross-references between documents
     • Verify routing logic coverage
   • Final packaging into skill bundle

MethodCard Schema

Every extracted method must be documented as:

name: string              # Method name
thesis: string            # Core claim/principle
triggers:                 # When to use this method
  - string
steps:                    # Executable steps
  - string
scenarios:                # Example applications
  - string
limits:                   # When NOT to use
  - string
evidence:                 # Book evidence references
  - "Chapter X, Section Y: specific quote or reference"
assigned_agents:          # Which subagents handle this
  - string

Subagent System

Use the following fixed roles:

Role	Primary Duty	Key Output
Book Profiler	Build book map, structure, core issues	00-overview.md
Author Thinking Analyst	Extract author's judgment patterns	01-author-thinking.md
Methodology Miner	Convert scattered techniques to MethodCards	02-method-catalog.md
Scenario Router	Map methods to problem types	03-scenario-router.md
Skill Packager / QA	Compile skill spec and validate evidence	04-subagent-playbooks.md, 05-master-skill-spec.md

Read references/subagent-playbooks.md before assigning work.

Output Bundle

Produce these Markdown artifacts:

• 00-overview.md - Book goals, structure, concept map, keywords
• 01-author-thinking.md - Author's observation patterns, judgment framework
• 02-method-catalog.md - All MethodCards, grouped by theme
• 03-scenario-router.md - Problem-to-method mapping and combination rules
• 04-subagent-playbooks.md - Subagent duties, inputs, outputs, handoffs
• 05-master-skill-spec.md - Top-level skill logic, delegation, output rules

Schemas defined in references/output-schemas.md.

Generated Skill Directory

When the extraction is materialized on disk, the final target is a standard skill directory rather than a loose output/ folder:

• skills/<book-slug>/SKILL.md
• skills/<book-slug>/workflow.yaml
• skills/<book-slug>/references/00-05*.md
• skills/<book-slug>/queries/pack-*.yaml
• skills/<book-slug>/logs/run-*.jsonl

Use the lightweight runtime scripts to create this layout:

• scripts/init_generated_skill.py
• scripts/wait_notebooklm.py
• scripts/finalize_generated_skill.py

Operating Rules

Rule	Violation Consequence
Use NotebookLM MCP as primary interface	Risk using training data instead of actual book content
Use staged, purpose-built prompts	Open-ended chat leads to inconsistent extraction
Every conclusion must have evidence	Skills drift from book content, become unreliable
Weak conclusions → mark as pending	Publishing unverified methods damages skill quality
First version: single book only	Scope creep, unfinished multi-book mess
Logical parallel, actual serial execution	Race conditions, inconsistent state

Common Mistakes & Fixes

Mistake	Why It Happens	Fix
Relying on training data about the book	Faster than reading	Force NotebookLM query first, every time
Creating generic summaries	Easier than structured extraction	Follow the five-stage query packs strictly
Missing evidence references	Forget to record during extraction	Add evidence field to every MethodCard template
Weak conclusions promoted	Want to appear complete	Mark as pending_confirmation instead
Skipping subagent assignments	Simpler to do it all yourself	Use fixed role system, delegate systematically

Reference Guide

• NotebookLM workflow: references/notebooklm-mcp-workflow.md
• Question packs: references/query-packs.md
• Subagent contracts: references/subagent-playbooks.md
• Output schemas: references/output-schemas.md
• Infographic structure: references/visual-infographic-spec.md

Quick Reference: Default Prompt Shape

When invoked without detailed spec, assume the user wants:

• Structured extraction of the book's thinking system
• Methodology cards with evidence
• Routing logic for real user problems
• Master skill specification for Claude Code / OpenClaw execution

Start with: "I'll help you convert this book into reusable skills. First, let me set up the extraction workflow..."