skill-from-notebook
Extracts methodologies from external content to generate reusable AI agent skills.
Install
mkdir -p .claude/skills/skill-from-notebook && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2718" && unzip -o skill.zip -d .claude/skills/skill-from-notebook && rm skill.zipInstalls to .claude/skills/skill-from-notebook
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Extract methodologies from documents or examples to create executable skillsKey capabilities
- →Extract methodologies from documents
- →Reverse engineer workflows from finished artifacts
- →Identify teaching patterns in instructional content
- →Generate structured skill templates
- →Perform multi-example analysis for generalizable skills
How it works
The skill identifies the input type as either a methodology document or a finished example, then applies specific extraction paths to deduce actionable steps, quality criteria, and reusable techniques.
Inputs & outputs
When to use skill-from-notebook
- →Turn technical blog into a skill
- →Extract workflow from PDF
- →Reverse engineer methodology from example
- →Convert docs to executable steps
About this skill
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
| Type | How to Process |
|---|---|
| Local files | PDF, Word, Markdown - Read directly |
| Web URL | WebFetch to extract content |
| YouTube | Use yt-dlp for subtitles, Whisper if unavailable |
| NotebookLM link | Browser automation to extract notes/summaries |
| Example/Output | Reverse engineer the methodology |
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
| Type | Characteristics | Examples |
|---|---|---|
| How-to | Clear step sequence, input→output | Deploy Docker, Configure CI/CD |
| Decision | Conditions, trade-offs, choices | Choose database, Select framework |
| Framework | Mental model, analysis dimensions | SWOT, 5W1H, First Principles |
| Checklist | Verification list, pass/fail criteria | Code review checklist, Launch checklist |
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?
| Dimension | Questions |
|---|---|
| Structure | How is content organized? |
| Style | Tone, word choice, expression? |
| Technique | What methods make it effective? |
| Logic | How does information flow? |
| Details | Small but important touches? |
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:
- Read the article
- Identify: This is a How-to type (has steps, teaching intent)
- Extract:
- Goal: Write clear, useful commit messages
- Steps: Use conventional format, separate subject/body, etc.
- Quality criteria: Subject < 50 chars, imperative mood, etc.
- 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:
- Identify: This is an example (finished work, no teaching intent)
- 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 - Extract quality characteristics:
- Title = specific tech + problem solved
- One idea per paragraph
- Code:text ratio ~40:60
- Personal anecdotes for credibility
- Reverse engineer steps:
- Start with a real problem you solved
- Write the solution first, then the setup
- Add code samples progressively
- etc.
- 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
- Always validate first - Not all content is suitable for skill extraction
- Identify the path early - Methodology doc vs Example require different approaches
- Be specific - Vague skills are useless; include concrete steps and criteria
- Preserve the source - Always credit where the knowledge came from
- Ask for clarification - If unsure about user intent, ask before proceeding
- 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
When not to use it
- →Summarizing documents
- →Converting file formats
- →Generating content without actionable methodology
Prerequisites
Limitations
- →Requires content with clear goal or process indicators
- →Not suitable for unstructured or non-methodological content
How it compares
Unlike a general summarizer, this skill focuses on reverse-engineering the production process to create a repeatable, executable task structure.
Compared to similar skills
skill-from-notebook side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| skill-from-notebook (this skill) | 2 | 6mo | No flags | Intermediate |
| prompt-optimize | 13 | 9mo | No flags | Advanced |
| ai-cost-optimizer | 9 | 5mo | Caution | Intermediate |
| self-improving-agent | 12 | 1mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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