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hungtrandigital
hungtrandigitalCOMMUNITY
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docs-seeker

Fetch and analyze llms.txt documentation from context7 with fallback chains.

Updated Dec 12, 2025
Search Tags:#documentation#technical writing#AI agent#search#llms.txt#context7#knowledge discovery
One-click install
npx skills add https://github.com/hungtrandigital/ResidentOne --skill docs-seeker-hungtrandigital
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: docs-seeker
Source: https://github.com/hungtrandigital/ResidentOne/tree/main/0-agents/agents/skills/docs-seeker
Command: npx skills add https://github.com/hungtrandigital/ResidentOne --skill docs-seeker-hungtrandigital

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solves?

Sifting through vast technical documentation to find precise answers or comprehensive overviews is time-consuming and inefficient. This Skill automates the discovery, fetching, and analysis of technical documentation, optimizing the process for AI agents and delivering targeted information quickly.

Core Features & Use Cases

  • Intelligent Query Detection: Automatically classifies user queries as topic-specific or general, extracting relevant keywords and library names to narrow down searches.
  • Automated Doc Fetching: Fetches documentation from context7.com and other sources using a robust fallback chain, eliminating manual URL construction and ensuring comprehensive coverage.
  • Smart Analysis & Agent Distribution: Analyzes fetched llms.txt content, categorizes URLs by priority, and recommends an optimal agent distribution strategy for efficient parallel processing.
  • Use Case: Find comprehensive documentation for the 'Next.js' framework, and suggest how to distribute the search across multiple agents for efficiency.

Quick Start

Search for documentation on 'How to use date picker in shadcn/ui?'

Frequently Asked Questions about docs-seeker

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search technical documentation across multiple sources automatically?▼

Documentation search automation detects your query type, fetches llms.txt files from context7 and fallback sources, and analyzes results to identify the most relevant content without manual URL construction or repeated searches.

Can I use an AI agent to discover documentation for specific frameworks and libraries?▼

Yes. Intelligent query detection extracts library names and keywords from your search, retrieves topic-specific documentation sources, and recommends how to distribute the analysis across multiple agents for parallel processing efficiency.

What's the best way to find comprehensive docs for a specific feature across multiple llms.txt sources?▼

Automated doc fetching constructs and prioritizes URLs, applies robust fallback chains to ensure coverage, and categorizes results by relevance, surfacing the most comprehensive feature documentation first without manual filtering.

How does documentation discovery handle cases where the primary source is unavailable?▼

The Skill implements fallback chains that automatically retry alternative doc sources when primary URLs fail, ensuring comprehensive coverage and eliminating gaps in your search results even when some endpoints are unreachable.

Can I optimize documentation searches across many agents at once?▼

Yes. Smart analysis categorizes fetched documentation by priority and recommends an optimal agent distribution strategy, enabling efficient parallel processing and reducing redundant searches when multiple agents query the same knowledge base.

Does this work for general documentation searches or only specific libraries?▼

Both. The Skill classifies queries as topic-specific or general; for topic-specific searches it extracts keywords and library names, while general documentation searches leverage the same automated fetching and analysis pipeline with broader source coverage.

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