Can I search for documentation for a specific version of a library?

Yes. The skill supports version-specific searches by checking versioned paths (e.g., /v2.0/llms.txt) or by checking out specific tags and branches during GitHub repository analysis.

How does this skill handle large documentation sets?

When a documentation source contains many URLs, the skill launches multiple 'Explorer' agents in parallel. Each agent analyzes a specific section, and the findings are consolidated into a single report for the user.

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

The skill automatically falls back to secondary strategies: it searches for a GitHub repository to analyze via Repomix or deploys 'Researcher' agents to gather information from official websites and community guides.

What is the llms.txt standard used by this skill?

llms.txt is a standardized way for projects to provide a single, AI-friendly markdown file that contains essential documentation, making it easier for LLMs to ingest technical context without navigating complex UI elements.

Documentation Discovery & Analysis

Name: Documentation Discovery & Analysis
Author: rafaelcalleja

byrafaelcalleja

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Learning & Documentation

Discovers and analyzes the latest technical documentation using llms.txt standards, GitHub repository analysis, and parallel AI exploration.

About

Docs Seeker is a high-performance skill for Claude Code designed to eliminate the limitations of static training data by fetching real-time technical documentation. It prioritizes AI-optimized formats via the llms.txt standard—specifically leveraging aggregators like context7.com—to provide Claude with high-density, low-noise context. When standardized docs are unavailable, it employs sophisticated fallback strategies including deep GitHub repository analysis via Repomix and multi-agent parallel exploration to ensure you always have the most accurate and up-to-date implementation patterns for any library or framework.

Key Features

Comprehensive GitHub repository packing using Repomix
1 GitHub stars
AI-optimized documentation discovery via the llms.txt standard
Explicit version handling for legacy, current, and beta software releases
Parallel multi-agent exploration for rapid analysis of large docsets
Targeted topic-specific searching to reduce context window noise

Use Cases

Extracting documentation directly from source code when official websites are insufficient
Retrieving the latest API references for rapidly evolving frameworks like Next.js or Astro
Performing deep-dive research into specific library features using parallelized explorer agents

About

Key Features

Comprehensive GitHub repository packing using Repomix
1 GitHub stars
AI-optimized documentation discovery via the llms.txt standard
Explicit version handling for legacy, current, and beta software releases
Parallel multi-agent exploration for rapid analysis of large docsets
Targeted topic-specific searching to reduce context window noise

Use Cases

Extracting documentation directly from source code when official websites are insufficient
Retrieving the latest API references for rapidly evolving frameworks like Next.js or Astro
Performing deep-dive research into specific library features using parallelized explorer agents