What happens if a website does not have an llms.txt file?

The skill includes a fallback mechanism that will automatically attempt to read the original HTML URL if the optimized .txt files are not found.

What is the llms.txt standard?

llms.txt is a proposed standard for providing LLM-friendly documentation in a concise markdown format, making it easier for AI models to parse technical information.

Does this skill work with any documentation site?

It works best with sites that have implemented the llms.txt standard, but it can extract the base URL and attempt discovery on any technical documentation host.

How does this skill improve Claude's performance?

By fetching structured text instead of raw HTML, it reduces token usage, minimizes noise, and provides Claude with more accurate technical context.

LLM Documentation Fetcher

Name: LLM Documentation Fetcher
Author: thkt

bythkt

•

Learning & Documentation

Optimizes external documentation retrieval by leveraging the llms.txt standard for faster and more accurate context ingestion.

About

This skill enables Claude to efficiently navigate and ingest external documentation by prioritizing LLM-friendly formats over standard HTML. It automatically implements a multi-step discovery process, checking for llms.txt and llms-full.txt at the base domain before falling back to the original URL. By parsing structured markdown headers and links, it filters out irrelevant web noise and focuses on the technical content Claude needs to solve coding tasks, making it an essential tool for working with modern APIs and libraries that support the llms.txt standard.

Key Features

Intelligent base URL extraction from deep-link API documentation
Reduces token waste by avoiding bloated HTML structures
3 GitHub stars
Automated llms.txt discovery and fallback logic
Structured parsing of markdown titles, summaries, and categories
Priority-based section navigation to skip low-relevance content

Use Cases

Quickly indexing documentation for new frameworks during development
Referencing external technical guides using LLM-optimized text formats
Fetching the latest API references for libraries like Stripe or Supabase

About

Key Features

Intelligent base URL extraction from deep-link API documentation
Reduces token waste by avoiding bloated HTML structures
3 GitHub stars
Automated llms.txt discovery and fallback logic
Structured parsing of markdown titles, summaries, and categories
Priority-based section navigation to skip low-relevance content

Use Cases

Quickly indexing documentation for new frameworks during development
Referencing external technical guides using LLM-optimized text formats
Fetching the latest API references for libraries like Stripe or Supabase