Detect topics, fetch context7 docs, and analyze llms.txt for agent planning.
npx skills add https://github.com/EdgeHunt/EdgeHunt --skill ck-docs-seeker-edgehunt
Please help me install this Agent Skill. Skill: ck:docs-seeker Source: https://github.com/EdgeHunt/EdgeHunt/tree/main/.claude/skills/docs-seeker Command: npx skills add https://github.com/EdgeHunt/EdgeHunt --skill ck-docs-seeker-edgehunt
Documentation discovery for libraries and frameworks often requires many manual searches across multiple sources. This skill provides a script-first workflow to detect topic relevance, fetch authoritative docs, and analyze results to guide agent deployment.
Start by running the topic-detection script on a user query to classify it as topic-specific or general, then fetch documentation with the docs-fetching script, and finally analyze the llms.txt results to plan agent deployment.
High-intent search queries and answers about installing and using this skill.
Automating documentation discovery is handled through a script-first workflow that classifies queries, fetches context7 docs, and analyzes llms.txt results to plan agent deployment. This removes the need for manual searches across multiple sources.
The llms.txt analysis workflow evaluates collected URLs to determine priority, workload distribution, and recommended agent counts. It outputs JSON data that drives downstream agents for efficient documentation exploration.
Yes, context7 fetches llms.txt content with automatic fallbacks and error handling. This ensures the documentation discovery process continues smoothly even if the primary source fails.
Routing topic-specific and general queries is achieved by running a topic-detection script first. This deterministic script classifies the query and then routes it to the appropriate context7 docs sources.
Yes, the skill relies on environment-driven configuration to support script-initiated workflows. This setup enables zero-token context loading and generates JSON outputs for downstream agents.
Automated docs discovery uses JSON outputs to drive downstream agents directly. This structured format includes planning data, priority rankings, and agent-distribution guidance, which raw text cannot easily provide.
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