开发编程
#agent
book-to-skills
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.
DeepseekModel
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质量 良好 · 48
v1.0.0
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https://deepseekmodel.com/api/download.php?id=hoangngochuong24947-gif-skillify-textbooks-skills-book-to-skills-skill-md&format=skill
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标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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: Should this book's thinking style be applied to the user's problem? Which methodology modules from the book are relevant? Which subagents should be assigned? 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) Create NotebookLM notebook for the selected book 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) Assign subagents using references/subagent-playbooks.md Package into Markdown output bundle per references/output-schemas.md 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 Launch Group A agents simultaneously Both agents query NotebookLM independently Outputs: 00-overview.md , 01-author-thinking.md Merge Group A outputs before Group B Combine book structure + author thinking model Pass to Methodology Miner as unified context 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[] Group C: Routing logic Single agent, processes all MethodCards Output: 03-scenario-router.md 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..."
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |