plugins/vector-db/skills/vector-db-search/SKILL.md
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.
npx skillsauth add richfrem/agent-plugins-skills vector-db-searchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill requires the chromadb and langchain packages defined in the plugin root.
Semantic (meaning-based) search against the ChromaDB vector store using a high-precision Parent-Child architecture. Use for Phase 2 of the 3-phase search protocol (RLM -> Vector -> Grep).
| Script | Role |
|:-------|:-----|
| scripts/query.py | Semantic search CLI -- recovers context-rich parent chunks. |
| scripts/operations.py | Core domain logic for retrieval. |
| scripts/vector_config.py | Unified profile-based configuration loader. |
This skill defaults to In-Process mode for zero-latency direct disk access. No background server is required. This ensures maximum stability in isolated project environments.
Verify available profiles in .agent/learning/vector_profiles.json. The default profile is usually wiki.
Note: The --profile flag is mandatory to ensure the correct model and collection are loaded.
python ./scripts/query.py "your natural language question" --profile wiki --limit 5
Results include ranked parent chunks (2,000 chars) that provide broad context to the LLM for reasoning.
--profile to ensure the correct semantic space is searched.query.py.tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.