Why use this skill instead of regular web scraping?

This skill is up to 10x faster and significantly more token-efficient because it fetches pre-formatted, structured data specifically designed for AI consumption.

Can I use this with other documentation tools?

Yes, it is designed to integrate with tools like doc-scraper, using the llms.txt file as a base and filling in gaps with targeted scraping where necessary.

What is an llms.txt file?

It is a proposed standard for websites to provide a specific text or markdown file optimized for LLMs, making it easier for AI agents to process documentation without messy HTML parsing.

How does it handle different documentation sizes?

The skill automatically checks for full, standard, and small variants of the documentation, allowing you to choose the version that best fits your current context window.

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

The skill includes a recovery protocol that detects the missing file and alerts the user to fall back to traditional scraping or manual documentation entry.

LLM-Optimized Documentation Support

Name: LLM-Optimized Documentation Support
Author: jmagly

byjmagly

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콘텐츠 관리

Detects and utilizes llms.txt files to provide high-speed, AI-friendly documentation ingestion for agentic coding workflows.

소개

This skill automates the discovery and processing of llms.txt files, a modern standard for providing structured, LLM-ready website documentation. By intelligently probing for hierarchical variants—full, standard, or small—it ensures Claude receives the most relevant technical context without the overhead and noise of traditional web scraping. It significantly reduces token consumption and improves grounding accuracy by fetching clean markdown that is already optimized for large language models, making it an essential utility for developers working with rapidly evolving APIs and frameworks.

주요 기능

Hierarchical detection of llms-full.txt, llms.txt, and llms-small.txt variants
Seamless integration with existing scrapers for hybrid documentation gathering
Smart variant selection based on context window limits and documentation scope
67 GitHub stars
Detailed JSON reporting of detection results and recommended documentation sources
Automated validation of content integrity and file size to filter out error pages

사용 사례

Reducing token consumption and latency during automated documentation ingestion
Rapidly onboarding new libraries or frameworks by fetching AI-optimized documentation
Providing high-quality grounding data for autonomous coding agents

소개

주요 기능

Hierarchical detection of llms-full.txt, llms.txt, and llms-small.txt variants
Seamless integration with existing scrapers for hybrid documentation gathering
Smart variant selection based on context window limits and documentation scope
67 GitHub stars
Detailed JSON reporting of detection results and recommended documentation sources
Automated validation of content integrity and file size to filter out error pages

사용 사례

Reducing token consumption and latency during automated documentation ingestion
Rapidly onboarding new libraries or frameworks by fetching AI-optimized documentation
Providing high-quality grounding data for autonomous coding agents