{"skill":{"slug":"vector-databases","displayName":"Vector Databases","summary":"Deep vector database workflow—embedding choice, index algorithms, recall/latency trade-offs, hybrid search, filtering, operational tuning, and cost. Use when...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":216,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1774368165178,"updatedAt":1774373218210},"latestVersion":{"version":"1.0.0","createdAt":1774368165178,"changelog":"Initial release with a comprehensive workflow for selecting and optimizing vector databases.\n\n- Covers end-to-end process: problem definition, embeddings, index algorithms, hybrid search, operations, and evaluation.\n- Supports decision-making for Pinecone, Milvus, Qdrant, Weaviate, pgvector, OpenSearch kNN, and similar tools.\n- Emphasizes recall, latency, cost trade-offs, tuning, and metadata filtering.\n- Includes actionable checklists, metrics, and guidance for troubleshooting and iteration.","license":"MIT-0"},"metadata":null,"owner":{"handle":"clawkk","userId":"s170g5yz1q3ksjnn4gz6v24af983h1mh","displayName":"clawkk","image":"https://avatars.githubusercontent.com/u/265748372?v=4"},"moderation":null}