Self Learning

---
name: self-learning
description: Autonomously research any technology, library, framework, or API. Discovers official documentation, extracts key information from authoritative sources, verifies across multiple pages, and generates a reusable Claude skill (SKILL.md) with installation, examples, and best practices.
---

# Self-Learning Skill

Teach Claude about any new technology and create a permanent, reusable skill from the research.

## Invocation

Trigger with:
- `/learn <topic>` - e.g., `/learn anthropic api`
- "Create a skill for <topic>"
- "Teach yourself about <topic>"
- "Learn <topic> and save it as a skill"

## Process

### Step 1: Clarify Scope

Before researching, clarify ambiguous topics:

**If topic is broad** (e.g., "react", "aws"):
Ask: "What aspect of <topic> should I focus on? For example:
- API/SDK usage
- Specific feature (e.g., hooks, lambda)
- Integration with another tool
- Or comprehensive overview?"

**If topic is specific** (e.g., "stripe webhooks", "prisma migrations"):
Proceed directly to research.

### Step 2: Discover Authoritative Sources

Use web search to find authoritative sources with targeted queries:

1. Search: `official <topic> documentation site`
2. Search: `<topic> quickstart guide getting started`
3. Search: `<topic> API reference examples`

**Source prioritization:**
1. Official documentation (docs.*, *.dev, official GitHub)
2. Official blog posts / announcements
3. Reputable tutorials (MDN, Real Python, official guides)
4. Avoid: Medium, dev.to, Stack Overflow (for primary sources)

### Step 3: Extract Content from Top Sources

Fetch and extract content from the top 3-5 URLs found.

**Extract these sections from each source:**
- [ ] Installation / Setup
- [ ] Authentication / API keys
- [ ] Core concepts / Models / Endpoints
- [ ] Basic usage examples (Python, TypeScript)
- [ ] Advanced features (streaming, tools, etc.)
- [ ] Error handling patterns
- [ ] Best practices / Common pitfalls
- [ ] Pricing / Limits (if applicable)

### Step 4: Verify and Cross-Reference

Before including any fact:
1. Confirm it appears in 2+ sources OR is from official docs
2. Check the publication/update date
3. Note any version-specific information
4. Flag anything that seems outdated or conflicting

**If conflicts found:** Prioritize official docs, note the discrepancy in the skill.

### Step 5: Interactive Refinement (Key Differentiator)

After initial research, ask the user:

"I've researched <topic>. Here's what I found:
- Installation: [summary]
- Key features: [list]
- Code examples available for: [languages]

Should I:
1. Proceed with generating the skill as-is
2. Deep-dive into a specific feature
3. Add examples for additional languages
4. Focus on a particular use case"

This ensures the generated skill matches user needs.

### Step 6: Generate the Skill

Create a complete SKILL.md using this structure:

```markdown
---
name: <slugified-topic>
description: <One clear sentence about what this technology does and when to use it>
version: 1.0.0
sources_verified: <YYYY-MM-DD>
---

# <Topic Name>

<2-3 sentence overview of what this is and its primary use case>

## Quick Reference

| Item | Value |
|------|-------|
| Official Docs | <URL> |
| Installation | `pip install X` / `npm install X` |
| Auth Required | Yes/No - <how to get keys> |
| Primary Use | <main use case> |

## Installation

### Python
```bash
pip install <package>
```

### JavaScript/TypeScript
```bash
npm install <package>
# or
bun add <package>
```

## Authentication

<How to set up API keys, environment variables, etc.>

```python
import os
# Recommended: Use environment variables
api_key = os.environ.get("<ENV_VAR_NAME>")
```

## Basic Usage

### Python

```python
# <Clear comment explaining what this does>
from <package> import <Client>

client = <Client>(api_key="your-key")

try:
    response = client.<method>(<params>)
    print(response)
except <SpecificError> as e:
    print(f"Error: {e}")
```

### TypeScript

```typescript
import { <Client> } from '<package>';

const client = new <Client>({ apiKey: process.env.<ENV_VAR> });

const response = await client.<method>(<params>);
console.log(response);
```

## Key Capabilities

### <Capability 1>
<Brief explanation with code example>

### <Capability 2>
<Brief explanation with code example>

## Best Practices

1. **<Practice>**: <Why and how>
2. **<Practice>**: <Why and how>
3. **<Practice>**: <Why and how>

## Common Errors

| Error | Cause | Fix |
|-------|-------|-----|
| <Error> | <Why> | <Solution> |

## Additional Resources

- [Official Docs](<url>)
- [API Reference](<url>)
- [Examples](<url>)

---
*Skill generated by claude-self-learning on <date>*
*Sources verified: <date>*
```

### Step 7: Save the Skill

After generating, offer storage options:

"Skill generated! Where should I save it?

1. **Project-local**: `.claude/skills/<topic>/SKILL.md` (this project only)
2. **User-global**: `~/.claude/skills/<topic>/SKILL.md` (all projects)
3. **Plugin storage**: `<plugin-path>/storage/skills/<topic>/SKILL.md` (git-tracked, shareable)
4. **GitHub Gist**: Create a public gist for sharing"

Then create the file in the chosen location.

### Step 8: Versioning (Future Updates)

When saving, also create a `.meta.json`:

```json
{
  "created": "<ISO date>",
  "updated": "<ISO date>",
  "sources": ["url1", "url2"],
  "topic": "<original query>",
  "version": "1.0.0"
}
```

This enables future `/update-skill <topic>` to refresh the skill.

## Tool Requirements

This skill requires:
- Web search capability
- Web page fetching capability
- File system access (Write tool)
- Optional: GitHub CLI for gist creation

## Examples

### Example 1: Learn about Anthropic API
```
User: /learn anthropic api
Claude: I'll research the Anthropic API. Let me find the official documentation...
[Searches, extracts, generates skill]
```

### Example 2: Learn about a specific feature
```
User: /learn stripe webhooks
Claude: I'll focus on Stripe Webhooks specifically...
[Targeted research, focused skill]
```

### Example 3: Broad topic with clarification
```
User: /learn kubernetes
Claude: Kubernetes is a broad topic. What aspect should I focus on?
- Basic concepts and architecture
- Deployment and kubectl usage
- Helm charts
- Specific resource types (pods, services, etc.)
User: Focus on basic kubectl usage
Claude: Got it, focusing on kubectl fundamentals...
```

## Notes

- Keep generated skills between 2,000-4,000 tokens
- Prioritize Python and TypeScript examples
- Include error handling in all code examples
- Verify all facts against official sources
- Include the verification date in every generated skill