Answer questions from Quaid's brain using FTS5, semantic search, and structured queries.
npx skills add https://github.com/quaid-app/quaid --skill quaid-query
Please help me install this Agent Skill. Skill: quaid-query Source: https://github.com/quaid-app/quaid/tree/main/skills/query Command: npx skills add https://github.com/quaid-app/quaid --skill quaid-query
Answer questions from Quaid's brain using FTS5 + semantic search + structured queries. Synthesize across multiple pages. Cite sources.
Ask Quaid to run a query and fetch results.
High-intent search queries and answers about installing and using this skill.
Citation-enabled semantic search works by running hybrid queries across FTS5 and vector semantics to surface relevant pages, then synthesizing cross-page results with direct references to source pages for trust and traceability.
To scope structured queries, apply namespace-aware querying to isolate search results by project or domain. This restricts the four-layer search strategy to only match pages within the specified namespace boundaries.
Yes, you can use FTS5 and vector search together. The hybrid search strategy combines full-text matching and semantic search to surface relevant pages, enabling accurate cross-page synthesis across multi-page knowledge bases.
The best way to get precise, sourced answers is using a four-layer search strategy combining FTS5, semantic search, and structured queries. This approach applies configurable JSON output to meet accuracy and traceability requirements.
Configurable JSON output is required to meet traceability requirements when querying a knowledge base. It structures the citation-aware results, ensuring answers reference the correct source pages across namespaces.
mindmorassIngest documents as vector embeddings for semantic search across multi-project workspaces.
adoreseverFetch, parse, and extract text and metadata from web pages.
thepradipOptimizes vector retrieval systems and RAG workflows for semantic search and scalable indexing.
ruvnetPerform semantic vector search and retrieval over documents using AgentDB.
