LLMs.txt Finder

I specialize in detecting and fetching AI-optimized documentation formats like llms.txt and claude.txt from documentation websites.

What I Do

I help the doc-fetcher plugin find the most AI-friendly documentation format available:

1. Check for llms.txt: Look for /llms.txt or /llms-full.txt at the site root
2. Check for claude.txt: Look for Claude-specific documentation at /claude.txt
3. Parse and validate: Ensure the file contains valid documentation content
4. Extract metadata: Pull version info, update dates, and structure from the file
5. Download content: Fetch the complete content for caching

AI-Optimized Documentation Formats

llms.txt

A standard format designed specifically for AI consumption:

# Library Name

> Version: 1.0.0
> Last Updated: 2025-01-17

## Overview

[Concise description optimized for AI understanding]

## Installation

[Installation instructions]

## Quick Start

[Getting started guide]

## API Reference

[Complete API documentation]

## Examples

[Code examples with context]

Benefits:

• Pre-formatted for AI parsing
• Optimized for token efficiency
• Includes structured metadata
• Often more concise than web docs

claude.txt

Claude-specific documentation format:

# Library Name for Claude

This documentation is optimized for Claude AI assistant.

[Documentation structured specifically for Claude's capabilities]

Detection Strategy

When given a documentation URL, I check in this order:

1. https://example.com/llms-full.txt (comprehensive version)
2. https://example.com/llms.txt (standard version)
3. https://example.com/claude.txt (Claude-specific)
4. https://example.com/.well-known/llms.txt (alternative location)
5. https://example.com/docs/llms.txt (docs subdirectory)

Usage

Automatic Activation

I'm automatically invoked when you run:

/fetch-docs <library>

I run first and check for AI-optimized formats before falling back to web crawling.

Manual Usage

You can also invoke me directly through the skill:

"Check if Next.js has an llms.txt file"
"Find AI-optimized docs for Supabase"
"Does https://react.dev have claude.txt?"

What I Return

If I find an AI-optimized documentation file, I return:

{
  "found": true,
  "type": "llms.txt",
  "url": "https://nextjs.org/llms.txt",
  "size_bytes": 524288,
  "version": "15.0.3",
  "last_updated": "2025-01-15",
  "content_preview": "# Next.js 15.0.3...",
  "should_use": true,
  "reason": "AI-optimized format available, more efficient than crawling"
}

If not found:

{
  "found": false,
  "checked_urls": [
    "https://example.com/llms.txt",
    "https://example.com/claude.txt",
    "https://example.com/.well-known/llms.txt"
  ],
  "fallback": "sitemap.xml",
  "reason": "No AI-optimized format found, falling back to web crawling"
}

Integration with Doc Fetcher

When I find an AI-optimized file:

1. Skip Crawling: No need to crawl hundreds of pages
2. Faster Fetching: Single file download vs. many HTTP requests
3. Better Structure: Pre-formatted for AI understanding
4. Token Efficient: Content is already optimized for LLM context windows
5. More Accurate: Maintained specifically for AI assistants

Example Flow

User: /fetch-docs nextjs

Doc Fetcher: Invoking llms-txt-finder skill...

llms-txt-finder:
  ✓ Checking https://nextjs.org/llms.txt
  ✓ Found! (524 KB)
  ✓ Version: 15.0.3
  ✓ Last updated: 2025-01-15

  Recommendation: Use llms.txt instead of crawling
  Benefits:
    - 1 file vs ~234 pages to crawl
    - Pre-optimized for AI
    - Faster download (5 seconds vs 4 minutes)

Doc Fetcher: Using llms.txt...
  ✓ Downloaded and cached
  ✓ Generated skill: nextjs-15-expert

Validation

I validate AI-optimized documentation files for:

1. Format: Proper markdown structure
2. Completeness: Contains essential sections (installation, API, examples)
3. Version Info: Includes version metadata
4. Size: Reasonable size (not truncated or too small)
5. Content Quality: Actual documentation, not just a placeholder

If validation fails, I recommend falling back to crawling:

⚠ Found llms.txt but validation failed
  Issue: File is only 2 KB (likely incomplete)
  Recommendation: Fall back to sitemap.xml crawling

Known Sites with AI-Optimized Docs

I maintain awareness of popular libraries that provide AI-optimized documentation:

• Next.js: /llms.txt ✓
• Vercel AI SDK: /llms.txt ✓
• Anthropic Claude: /claude.txt ✓
• OpenAI: /llms.txt ✓
• Supabase: Checking...
• Tailwind CSS: Checking...

This list helps me make intelligent guesses about where to look.

Creating llms.txt for Your Project

If you maintain a library, I can help you create an llms.txt file:

"Help me create an llms.txt file for my library"

I'll generate a template following best practices:

# Your Library Name

> Version: 1.0.0
> Last Updated: 2025-01-17
> Repository: https://github.com/you/your-lib
> Documentation: https://docs.yourlib.com

## Overview

[2-3 sentence description of what your library does]

## Installation

[Installation commands for different package managers]

## Quick Start

[Minimal example to get started]

## Core Concepts

[Key concepts users need to understand]

## API Reference

[Complete API documentation]

## Examples

[Common use cases with code examples]

## Advanced Usage

[Advanced patterns and techniques]

## Troubleshooting

[Common issues and solutions]

## Changelog

[Recent changes and migration notes]

Performance Impact

Using AI-optimized documentation dramatically improves performance:

Metric	Web Crawling	llms.txt
Files fetched	100-500	1
Time to fetch	2-10 min	5-30 sec
Network requests	100-500	1
Size on disk	5-50 MB	0.5-5 MB
Token efficiency	Variable	Optimized

Error Handling

I handle various error scenarios:

• 404: File doesn't exist, fall back to crawling
• 403: Access forbidden, try alternative URLs
• Timeout: Server slow, retry with backoff
• Invalid format: Not proper documentation, fall back
• Too large: File >50MB, might be data dump, validate carefully

Configuration

Configure my behavior in doc-fetcher-config.json:

{
  "llms_txt": {
    "enabled": true,
    "check_locations": [
      "/llms-full.txt",
      "/llms.txt",
      "/claude.txt",
      "/.well-known/llms.txt",
      "/docs/llms.txt"
    ],
    "max_size_bytes": 52428800,
    "validation_strict": true,
    "prefer_over_crawling": true
  }
}

Future Enhancements

• Registry: Maintain a registry of known llms.txt URLs
• Validation Service: Validate llms.txt files against a schema
• Generator: Auto-generate llms.txt from existing docs
• Suggestions: Suggest improvements to existing llms.txt files

Technical Implementation

I use these tools to accomplish my tasks:

1. WebFetch: Fetch content from URLs
2. Bash: Check HTTP headers with curl
3. Read: Read configuration files
4. Write: Cache fetched llms.txt files

Example Checks

Successful Find

Checking for AI-optimized docs at https://nextjs.org...

✓ https://nextjs.org/llms.txt
  Status: 200 OK
  Size: 524 KB
  Content-Type: text/plain
  Last-Modified: 2025-01-15

✓ Validation passed
  - Valid markdown format
  - Contains version: 15.0.3
  - Comprehensive content
  - Reasonable size

Recommendation: Use llms.txt
Benefits: 40x faster than crawling 234 pages

Not Found

Checking for AI-optimized docs at https://example.com...

✗ https://example.com/llms-full.txt (404)
✗ https://example.com/llms.txt (404)
✗ https://example.com/claude.txt (404)
✗ https://example.com/.well-known/llms.txt (404)

No AI-optimized documentation found.
Falling back to sitemap.xml or web crawling.

See Also

• doc-indexer skill - Main documentation crawling logic
• doc-crawler agent - Advanced web crawling for non-standard sites
• /fetch-docs command - Primary entry point that uses this skill