Individual skill
vector-db
Use when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric, dimensions, HNSW parameters, named vectors); filtering on metadata; hybrid dense-plus-sparse search; and quantization to cut RAM and cost. Covers garbage results, silently ignored filters, low recall, slow queries, and filtered queries returning fewer than k rows. NOT producing, chunking or judging embeddings (that is `embeddings-search`).
skills/vector-db/SKILL.md
Source commit d9d6bb338f66cc1e23998585e580a0ea45bbcb82
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
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- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 561
- Repository last updated
- Nov 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
- cursor Repository topic
- vector-database Declared skill tag
- pinecone Declared skill tag
- qdrant Declared skill tag
- weaviate Declared skill tag
- pgvector Declared skill tag
- hnsw Declared skill tag
- hybrid-search Declared skill tag
- metadata-filter Declared skill tag
- quantization Declared skill tag
- data Derived category
- data-databases Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 11,410 bytesevals/README.mddoc · 784 bytesevals/cases.yamlasset · 3,173 bytesreferences/engines.mddoc · 5,242 bytesreferences/tuning.mddoc · 3,213 bytesscripts/verify.shruns · 5,232 bytes
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# vector-db — operate the store, not the embeddings
You own the **store**: the collection schema, the index, the filter path, recall-vs-latency
tuning, hybrid fusion, quantization, and the production knobs (upserts, namespaces, deletes,
backups). You operate it across the four engines a Claude agent actually meets: **pgvector**
(Postgres extension), **Qdrant**, **Weaviate**, **Pinecone** (serverless).
Three things are **not** yours, and pretending they are produces wrong advice:
- Producing, chunking, rewriting, or scoring embeddings → `../embeddings-search/SKILL.md`. You
store vectors; you do not make or judge them.
- Assembling the retrieve → rerank → prompt → generate loop and its eval → `../rag/SKILL.md`.
- General Postgres (non-vector schema, EXPLAIN, RLS, VACUUM, pooling) → `../postgresdb/SKILL.md`.
You own **only** the pgvector surface: the `vector`/`halfvec` column, its index, its operators,