What problem does it solve? Developers often need answers from library docs, framework guides, and protocol specs while coding, but manually searching scattered reference repositories is slow. This Skill lets you ask natural-language questions against your project's own indexed reference repos and get sourced, RAG-generated answers. ## Core Features & Use Cases - Semantic Doc Search: Search indexed reference repos (framework docs, protocol specs, API references) with ch_discover_context for factual lookups and browsing. - RAG Answers: Get synthesized, sourced answers to questions like "how do I create a Strands agent tool?" with citations from the docs. - Docs-to-Code Cross-Reference: Bridge documentation with your actual codebase using ch_ocxp_search and ch_ocxp_grep to see how documented patterns are used in practice. - Use Case: You are integrating EMQX authentication and ask "what MQTT QoS levels does EMQX support?" — the Skill searches the indexed EMQX docs, returns a sourced answer, then shows how your codebase already configures QoS. ## Quick Start Ask the kb skill a question like "how do I configure Strands agent memory?" to search the project's reference docs and get a sourced answer.