vd

Developer skill for implementing a new vector-database backend in the vd package. Use when adding, refactoring, or reviewing a vd backend adapter (qdrant, pinecone, weaviate, lancedb, pgvector, faiss, etc.) — covers the AbstractClient/AbstractCollection raw-primitive contract, filter handling, capability protocols, the escape hatch, and the provider registry. Trigger on "add a backend to vd", "implement the X backend", "vd adapter".

Backend-selection and setup tooling for the vd package. Use this skill when the user is picking a vector database with vd, asks "which backend should I use", weighs persistence / cloud / cost / hybrid / scale trade-offs, hits a "backend not installed" error, or needs help installing and starting a vectorDB (pip packages, Docker, API keys, env vars).

Operational tooling for the vd package. Use this skill when the user is managing a vd collection beyond reads/writes — exporting or importing data (JSONL / JSON / directory), migrating between backends, computing collection statistics, finding duplicates / outliers, validating integrity, running health checks, benchmarking search or insert latency, or driving any of the above from the `vd` CLI.

Vector-search tooling for the vd package. Use this skill when the user wants to do semantic / vector search in Python with the vd package — connecting to a backend, creating a collection, adding documents, and running a query. Also trigger on mentions of "vector database", "embeddings + search", or any time the user imports `vd` for a new task and needs the basic happy-path setup.

Advanced-search tooling for the vd package. Use this skill when the user goes beyond a single basic .search() call with vd — metadata filters with MongoDB-style operators, multi-query searches, reciprocal rank fusion, finding documents similar to an existing one, deduplicating result sets, or searching by a pre-computed query vector.

Corpus-ingestion tooling for the vd package. Use this skill when the user has documents, files, articles, or long text they need to load into a vd collection — including cleaning text, splitting it into chunks, attaching metadata, and adding the result in batches. Trigger on requests like "load these docs into a vector DB", "chunk this text", "preprocess before embedding", or "bulk insert".