docs-seeker

Documentation discovery combines llms.txt files, Repomix repository analysis, and parallel web search to locate authoritative sources. This approach gathers up-to-date references from AI-ready documentation formats and GitHub repositories in a single aggregated report.

llms.txt is a standardized format for exposing AI-ready documentation at domain roots. This Skill prioritizes llms.txt discovery as the fastest path to authoritative technical docs before falling back to repository analysis or web search.

Repomix-based analysis automates repository examination to extract structured documentation when llms.txt is unavailable. The Skill performs parallel exploration across multiple sources, version-aware retrieval, and cross-source aggregation into a consolidated report.

Deploying multiple Explorer agents concurrently accelerates multi-source retrieval by exploring different documentation targets simultaneously. This coordination reduces wait time and aggregates findings into structured output while managing rate limits and resource constraints.

The Skill manages errors, rate-limits, and resource constraints through coordinated agent execution and fallback strategies. It identifies targets and versions upfront, performs WebSearch and WebFetch operations within boundaries, and surfaces limitations in the aggregated report.