Skill From Notebook

Skill from Notebook

Extract actionable methodologies from learning materials (documents, articles, videos) or quality examples (blog posts, designs, code) to generate reusable Skills.

Core Philosophy: NotebookLM helps you understand. This skill helps you do.

When to Use

When users want to turn knowledge into executable skills:

• "I just read this article about code review, help me create a skill from it"
• "Here's a great technical blog post, extract the writing methodology"
• "Turn this PDF guide into a skill I can reuse"
• "Learn from this example and create a skill to produce similar output"

Supported Input Types

Step 0: Identify Input Type

Critical first step - Determine which processing path to use:

User Input
    │
    ├─ Has teaching intent? ("how to", "steps", "guide")
    │   └─ YES → Path A: Methodology Document
    │
    ├─ Is a finished work? (article, design, code, proposal)
    │   └─ YES → Path B: Example (Reverse Engineering)
    │
    └─ Neither? → Tell user this content is not suitable

Path A indicators (Methodology Document):

• Contains words like "how to", "steps", "method", "guide"
• Has numbered lists or step sequences
• Written with teaching intent
• Describes "what to do"

Path B indicators (Example/Output):

• Is a complete work/artifact
• No teaching intent
• Is "the thing itself" rather than "how to make the thing"
• Examples: a well-written blog post, a polished proposal, a code project

---

Path A: Extract from Methodology Document

A1: Validate Document Suitability

Check if the document is suitable for skill generation (must meet at least 2):

• [ ] Has clear goal/outcome
• [ ] Has repeatable steps/process
• [ ] Has quality criteria
• [ ] Has context/scenario description

If not suitable: Tell user honestly and explain why.

A2: Identify Skill Type

A3: Extract Structure by Type

For How-to:

• Prerequisites
• Step sequence (with expected output per step)
• Final expected result
• Common errors

For Decision:

• Decision factors
• Options with pros/cons
• Decision tree/flowchart
• Recommended default

For Framework:

• Core concepts
• Analysis dimensions
• Application method
• Limitations

For Checklist:

• Check items with criteria
• Priority levels
• Commonly missed items

A4: Generate Skill

Use this template:

## Applicable Scenarios
[When to use this skill]

## Prerequisites
- [What's needed before starting]

## Steps
1. [Step 1] - [Expected outcome]
2. [Step 2] - [Expected outcome]
...

## Quality Checkpoints
- [ ] [Checkpoint 1]
- [ ] [Checkpoint 2]

## Common Pitfalls
- [Pitfall 1]: [How to avoid]

## Source
- Document: [name/URL]
- Extracted: [timestamp]

---

Path B: Reverse Engineer from Example

When input is a finished work (not a tutorial), reverse engineer the methodology.

B1: Identify Output Type

What kind of artifact is this?

• Technical blog post
• Product proposal/PRD
• Academic paper
• Code architecture
• Design document
• Other: [specify]

B2: Analyze Structure

Break down the example:

Structure Analysis:
├── [Part 1]: [Function] - [Proportion %]
├── [Part 2]: [Function] - [Proportion %]
├── [Part 3]: [Function] - [Proportion %]
└── [Part N]: [Function] - [Proportion %]

Questions to answer:

• How many parts does it have?
• What's the function of each part?
• What's the order and proportion?

B3: Extract Quality Characteristics

What makes this example good?

B4: Reverse Engineer the Process

Deduce: To create this output, what steps are needed?

## Deduced Production Steps
1. [Step 1]: [What to do] - [Key point]
2. [Step 2]: [What to do] - [Key point]
...

## Key Decisions
- [Decision 1]: [Options] - [This example chose X because...]

## Reusable Techniques
- [Technique 1]: [How to apply]
- [Technique 2]: [How to apply]

B5: Generate Skill

Use this template for reverse-engineered skills:

## Output Type
[What kind of artifact this produces]

## Applicable Scenarios
[When to create this type of output]

## Structure Template
1. [Part 1]: [Function] - [~X%]
2. [Part 2]: [Function] - [~X%]
...

## Quality Characteristics (Learned from Example)
- [Characteristic 1]: [How it manifests]
- [Characteristic 2]: [How it manifests]

## Production Steps
1. [Step 1]: [What to do] - [Tips]
2. [Step 2]: [What to do] - [Tips]
...

## Checklist
- [ ] [Check item 1]
- [ ] [Check item 2]

## Reference Example
- Source: [name/URL]
- Analyzed: [timestamp]

---

Example: Path A (Methodology Document)

User: "Extract a skill from this article about writing good commit messages"

Process: 1. Read the article 2. Identify: This is a How-to type (has steps, teaching intent) 3. Extract:

• Goal: Write clear, useful commit messages
• Steps: Use conventional format, separate subject/body, etc.
• Quality criteria: Subject < 50 chars, imperative mood, etc.

4. Generate skill with steps and checklist

---

Example: Path B (Reverse Engineering)

User: "Here's a great technical blog post. Learn from it and create a skill for writing similar posts."

Process: 1. Identify: This is an example (finished work, no teaching intent) 2. Analyze structure:

   ├── Hook: Real pain point (2-3 sentences)
   ├── Problem: 3 sentences on the core issue
   ├── Solution: Conclusion first, then details
   ├── Code: Each snippet < 20 lines, with comments
   ├── Pitfalls: 3 common errors
   └── Summary: One-line takeaway

3. Extract quality characteristics:

• Title = specific tech + problem solved
• One idea per paragraph
• Code:text ratio ~40:60
• Personal anecdotes for credibility

4. Reverse engineer steps:

• Start with a real problem you solved
• Write the solution first, then the setup
• Add code samples progressively
• etc.

5. Generate skill: "How to Write a Technical Blog Post"

---

Advanced: Multi-Example Learning

When user provides multiple examples of the same type:

Example A ──┐
Example B ──┼──> Extract commonalities ──> Core methodology
Example C ──┘           │
                        ▼
                  Analyze differences ──> Style variants / Optional techniques

This produces more robust, generalizable skills.

---

Important Notes

1. Always validate first - Not all content is suitable for skill extraction 2. Identify the path early - Methodology doc vs Example require different approaches 3. Be specific - Vague skills are useless; include concrete steps and criteria 4. Preserve the source - Always credit where the knowledge came from 5. Ask for clarification - If unsure about user intent, ask before proceeding 6. Quality over speed - Take time to truly understand the content

What This Skill is NOT

• NOT a summarizer (that's NotebookLM's job)
• NOT a document converter
• It's about extracting actionable methodology that can be repeatedly executed

skill-from-notebook 测试用例

测试覆盖

---

Test A1: How-to 类型（方法论文档）

输入: https://cbea.ms/git-commit/

描述: 经典的 Git commit message 写作指南，包含 7 条明确规则。

测试命令:

请用 skill-from-notebook 从这篇文章中提取一个 Skill：
https://cbea.ms/git-commit/

预期结果:

• [ ] 识别为 Path A（方法论文档）
• [ ] Skill 类型识别为：How-to
• [ ] 提取出 7 条规则作为步骤
• [ ] 生成包含质量检查点的 commit message 写作 Skill

验证要点:

• 是否包含 "50 字符限制" 规则
• 是否包含 "使用祈使句" 规则
• 是否有 checklist 格式的检查点

---

Test A2: Framework 类型

输入: 搜索 "SWOT analysis guide" 或使用任意 SWOT 分析方法文章

描述: 经典的商业分析框架，包含 4 个分析维度。

测试命令:

从这篇 SWOT 分析方法的文章提取一个 Skill：
[文章 URL]

预期结果:

• [ ] 识别为 Path A（方法论文档）
• [ ] Skill 类型识别为：Framework
• [ ] 提取出 4 个维度（Strengths, Weaknesses, Opportunities, Threats）
• [ ] 生成包含应用场景和使用方法的分析框架 Skill

验证要点:

• 是否包含核心维度定义
• 是否说明适用场景
• 是否有使用步骤

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Test B1: 逆向工程 - 技术博客

输入: https://notes.eatonphil.com/2024-04-10-what-makes-a-great-tech-blog.html

描述: Phil Eaton 写的一篇关于技术博客的文章，本身就是优秀技术博客的范例。

测试命令:

这是一篇写得很好的技术博客，请逆向分析它，提取出"如何写技术博客"的 Skill：
https://notes.eatonphil.com/2024-04-10-what-makes-a-great-tech-blog.html

预期结果:

• [ ] 识别为 Path B（成品范例）
• [ ] 分析文章结构（各部分及占比）
• [ ] 提取质量特征（写作风格、技巧）
• [ ] 逆向推导写作步骤
• [ ] 生成技术博客写作 Skill

验证要点:

• 是否分析了文章的结构组成
• 是否提取了"好在哪里"的特征
• 是否有可执行的写作步骤

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Test B2: 逆向工程 - README

输入: https://github.com/sindresorhus/awesome

描述: Awesome 列表的开创者，README 结构清晰、格式规范。

测试命令:

分析这个项目的 README，逆向提取出"如何写好 README"的 Skill：
https://github.com/sindresorhus/awesome

预期结果:

• [ ] 识别为 Path B（成品范例）
• [ ] 分析 README 结构
• [ ] 提取格式规范和组织方式
• [ ] 生成 README 写作 Skill

验证要点:

• 是否分析了章节结构
• 是否提取了格式特点（badge、目录、分类等）
• 是否有可复用的模板

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Test C: 边界情况

C1: 不适合生成 Skill 的内容

输入: 一篇新闻报道或纯观点文章

预期结果:

• [ ] 识别为"不适合"
• [ ] 向用户解释原因
• [ ] 不强行生成 Skill

C2: 混合类型

输入: 既包含教程又包含范例的文章

预期结果:

• [ ] 正确识别主要类型
• [ ] 或询问用户想要哪种提取方式

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测试执行记录

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参考资源

• 设计文档: docs/skill-from-notebook-design.md
• Skill 实现: SKILL.md