Does this skill consume tokens during the search process?

No, the core logic for query detection and documentation fetching runs via Node.js scripts locally, providing zero-token overhead for the initial discovery phase.

What is the main benefit of using Docs Seeker?

It automates the search for technical documentation by using local scripts to find the most relevant llms.txt files, saving time on manual searching and URL construction.

Can I use it for specific library components?

Yes, the detect-topic script is specifically designed to identify components, features, or concepts within a query to fetch the most relevant sub-documentation.

How does it handle large amounts of documentation?

The skill includes an analysis script that categorizes URLs and recommends an agent distribution strategy, allowing you to process large documentation sets using multiple AI agents in parallel.

Which sources does Docs Seeker use for documentation?

It primarily leverages the llms.txt standard and optimized URL patterns from context7.com to fetch structured, LLM-friendly documentation for frameworks and libraries.

Docs Seeker

Name: Docs Seeker
Author: CongDon1207

byCongDon1207

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Aprendizaje y Documentación

Streamlines technical documentation discovery and analysis using automated scripts and the llms.txt standard.

Acerca de

Docs Seeker is a specialized Claude Code skill designed to automate the process of finding and analyzing technical documentation. By utilizing a script-first workflow, it identifies whether a user needs general library information or specific topic-based guidance, automatically constructs optimized URLs for context7.com, and fetches relevant llms.txt content. This tool is particularly useful for developers who need to quickly ingest documentation for new frameworks, components, or GitHub repositories without the overhead of manual searching or high token consumption.

Características Principales

Intelligent analysis of llms.txt files to prioritize critical and supplementary documentation
1 GitHub stars
Automated topic detection to distinguish between general libraries and specific features
Agent distribution strategies to handle large-scale documentation via parallel processing
Zero-token execution scripts that handle classification and logic outside the LLM context
Script-based fetching from context7.com for automated URL construction and fallback logic

Casos de Uso

Analyzing complex GitHub repositories by automatically parsing their structure via llms.txt
Onboarding onto a new framework by fetching comprehensive, LLM-friendly documentation sets
Quickly locating specific implementation details for UI components like Shadcn or Radix

Acerca de

Características Principales

Intelligent analysis of llms.txt files to prioritize critical and supplementary documentation
1 GitHub stars
Automated topic detection to distinguish between general libraries and specific features
Agent distribution strategies to handle large-scale documentation via parallel processing
Zero-token execution scripts that handle classification and logic outside the LLM context
Script-based fetching from context7.com for automated URL construction and fallback logic

Casos de Uso

Analyzing complex GitHub repositories by automatically parsing their structure via llms.txt
Onboarding onto a new framework by fetching comprehensive, LLM-friendly documentation sets
Quickly locating specific implementation details for UI components like Shadcn or Radix