docs-seeker

Name: docs-seeker
Availability: InStock
Author: 0xKermini

Detect topics, retrieve llms.txt content, and produce structured documentation reports.

Updated Jan 20, 2026

Search Tags:#documentation #docs #llms.txt #context7 #ai-assisted #library-search

Ready to run

npx skills add https://github.com/lukebaze/forex-rebate-bot --skill docs-seeker-lukebaze

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: docs-seeker
Source: https://github.com/lukebaze/forex-rebate-bot/tree/main/.opencode/skill/docs-seeker
Command: npx skills add https://github.com/lukebaze/forex-rebate-bot --skill docs-seeker-lukebaze

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Search library/framework documentation quickly by orchestrating topic detection, URL discovery, and structured llms.txt analysis to deliver targeted results for developers and teams.

Core Features & Use Cases

Topic-aware searches: Distinguishes topic-specific queries from general queries and fetches relevant docs.
Fallback-driven workflow: Automatically falls back from topic URLs to general URLs and finally to repository analysis when needed.
Use Case: A developer asks for Next.js caching guidance, and the skill returns a concise set of URLs with a summary suitable for rapid review.

Quick Start

Start by running the detect-topic.js script to classify the query, then run fetch-docs.js to gather llms.txt content, and finally pipe it to analyze-llms-txt.js to obtain a structured report.

Frequently Asked Questions about docs-seeker

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

FAQPage Schema

How do I find library documentation quickly using llms.txt?▼

You can find library documentation by orchestrating topic detection, URL discovery, and llms.txt analysis to deliver targeted results. The process distinguishes topic-specific queries from general queries and fetches relevant docs automatically.

What is the best way to retrieve docs for a specific framework query?▼

Retrieving docs for a specific framework query is best handled by a fallback-driven workflow that automatically falls back from topic URLs to general URLs and finally to repository analysis when needed, ensuring you get a structured, actionable report.

How does topic detection work for AI-assisted library searches?▼

Topic detection for AI-assisted library searches works by classifying the query to distinguish topic-specific requests from general queries. It then coordinates URL discovery and llms.txt retrieval to produce a concise set of URLs with a summary suitable for rapid review.

Can I get a structured summary of Next.js caching guidance from documentation?▼

Yes, you can get a structured summary of Next.js caching guidance by running the detection, fetching, and analysis sequence. It returns a concise set of URLs with a summary suitable for rapid review, coordinating topic detection and llms.txt analysis.

What happens when topic-specific documentation URLs are not found?▼

When topic-specific documentation URLs are not found, the fallback-driven workflow automatically falls back to general URLs and finally to repository analysis. This ensures the library search still produces a structured, actionable report for the developer.

Do I need any external dependencies to analyze llms.txt files for library docs?▼

No external dependencies are required to analyze llms.txt files for library docs. The process relies entirely on internal scripts for topic detection, fetching, and analysis to produce a structured, actionable report without external packages.