Is this skill active automatically?

It is triggered whenever Claude needs to fetch external documentation, API references, or library manuals to answer a query.

Can this skill navigate deep documentation links?

Yes, it extracts the base URL from any given subpage to look for a centralized documentation index, ensuring it finds the most comprehensive information available.

How does this skill improve Claude's coding help?

By fetching text-dense files instead of raw HTML, Claude receives higher-quality information with less irrelevant data, resulting in more accurate code suggestions.

What is the llms.txt standard?

llms.txt is a proposed standard for a simple text file that provides LLMs with a structured, concise map of a project's documentation for better context ingestion.

What happens if a website doesn't have an llms.txt file?

The skill includes a built-in fallback sequence that automatically retrieves the original HTML content if the optimized .txt files are not found.

LLMS.txt Documentation Optimizer

Name: LLMS.txt Documentation Optimizer
Author: thkt

bythkt

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Learning & Documentation

Optimizes documentation retrieval by prioritizing llms.txt standards for cleaner, token-efficient context ingestion from external APIs and libraries.

About

This skill enhances Claude's research capabilities by implementing the emerging llms.txt standard for documentation discovery. When tasked with referencing external APIs or library documentation, the skill automatically attempts to locate structured, LLM-optimized text files at the project's root before falling back to traditional HTML scraping. By parsing these high-density Markdown files, Claude gains a more accurate and concise understanding of technical requirements, leading to better code generation with significantly less noise from non-essential web elements.

Key Features

Automated discovery of /llms.txt and /llms-full.txt files
Smart fallback mechanism to HTML when optimized files are missing
Context-aware prioritization of essential documentation sections
Structured parsing of Markdown headers, blockquotes, and links
3 GitHub stars
Intelligent base URL extraction from deep documentation links

Use Cases

Fetching high-density API references for libraries like Stripe or Firebase
Onboarding Claude to new frameworks using optimized documentation maps
Reducing token usage and context clutter during deep research tasks

About

Key Features

Automated discovery of /llms.txt and /llms-full.txt files
Smart fallback mechanism to HTML when optimized files are missing
Context-aware prioritization of essential documentation sections
Structured parsing of Markdown headers, blockquotes, and links
3 GitHub stars
Intelligent base URL extraction from deep documentation links

Use Cases

Fetching high-density API references for libraries like Stripe or Firebase
Onboarding Claude to new frameworks using optimized documentation maps
Reducing token usage and context clutter during deep research tasks