Self learning

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 Flag anything that seems outdated or conflicting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:

---
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

npm install <package>
# or
bun add <package>

Authentication

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

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

Basic Usage

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

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
• API Reference
• Examples

---

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