Search the vector database for semantically relevant code and documentation
From vector-dbnpx claudepluginhub richfrem/agent-plugins-skills --plugin vector-db"search query" [--profile knowledge] [--limit N] [--stats]/queryExecutes SQL queries against Claude Code/Codex CLI history and current Git repo data (commits, branches, diffs). Displays results in formatted tables.
/querySearches Weaviate collections using natural language queries. Specify query text, collections, and limit. Returns matching objects for further processing.
/queryQueries compiled wiki for answers using quick (indexes), standard (articles), or deep (full+raw+sibling) depths, citing sources only from wiki content.
/querySearches RAG database with a question, optionally limited to a book slug and top N results. Returns formatted results: book title, chapter, text snippet (150 chars), relevance score.
/queryRuns HogQL queries on PostHog data or converts natural language questions to HogQL using MCP tools and executes them.
/queryQuery optimization and data analysis. Interprets EXPLAIN plans, recommends indexes, rewrites queries for performance, detects N+1 problems, and profiles slow queries across SQL databases, MongoDB, Redis, and any ORM.
Semantic (meaning-based) search against the ChromaDB vector store.
For constraints and Source Transparency rules, see skill:
vector-db-search
# Semantic search
python3 ./scripts/query.py \
"your natural language question" --profile knowledge --limit 5
# Check DB stats / health
python3 ./scripts/query.py --profile knowledge --stats
Results are ranked by cosine similarity and include file path, chunk content, and score.
If results are poor or empty, run /vector-db:ingest to rebuild the index.