Search a Lorekit corpus and return cited, synthesized answers.
npx skills add https://github.com/GYF0311/lorekit --skill corpus-query
Please help me install this Agent Skill. Skill: corpus-query Source: https://github.com/GYF0311/lorekit/tree/main/skills/corpus-query Command: npx skills add https://github.com/GYF0311/lorekit --skill corpus-query
This Skill helps you find and synthesize accurate answers from a persistent Lorekit knowledge base without re-reading scattered sources every time.
Ask your AI agent: “Search the default Lorekit corpus for the answer to ‘What is lorekit’s incremental compilation workflow and how should evidence be promoted into the raw layer?’ and cite the exact canonical pages.”
High-intent search queries and answers about installing and using this skill.
Semantic search synthesizes answers from a knowledge corpus by routing questions to a central Lorekit database, applying hybrid vector recall, and returning cited results grounded in canonical pages. It combines exact-term lookup with optional GBrain-assisted candidate recall.
Hybrid retrieval for cross-project knowledge queries works by combining exact term search, semantic vector recall, and optional GBrain-assisted candidate recall. It routes questions to a persistent central Lorekit corpus and synthesizes responses grounded in authoritative canonical documentation.
To get cited answers with confidence notes from a documentation knowledge base, you query the central corpus using hybrid retrieval. The system returns synthesized responses that include citations pointing to real corpus pages and provide confidence notes indicating the reliability of the recalled information.
Yes, you can use this knowledge retrieval approach for read-only documentation workflows. It ensures query-time reads from the Lorekit corpus only, keeping the knowledge base safe and unmodified while applying per-corpus rules and utilizing configured corpus-local binaries.
Yes, this semantic search tool requires reading per-corpus rules and canonical pages before querying. This ensures the synthesized responses accurately respect the specific boundaries and configurations of the central Lorekit knowledge base you are searching.
Limitations of using a central knowledge corpus for information recall include being restricted to read-only operations and depending on the configured corpus-local binaries. You cannot write to the corpus, and recall accuracy relies on the existing canonical pages within the persistent knowledge base.
GYF0311Capture ad-hoc notes into a Lorekit corpus workbench inbox as timestamped Markdown fragments.
lhlExport Reality Check claims, sources, and chains to YAML or Markdown.
ArogyaReddyCombine vector similarity and keyword matching with RRF, Linear, Cross-encoder, or Cascade fusion.
nilecuiImplement hybrid search pipelines combining vector and keyword results with RRF fusion.
