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From azure-cosmosdb
Provides best practices for Azure Cosmos DB vector search: enabling feature, defining embedding policies, configuring vector indexes, normalizing embeddings, and implementing RAG patterns.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-cosmosdb:cosmosdb-vector-searchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Best practices for configuring and using vector search in Azure Cosmos DB for AI-powered semantic search and RAG.
Best practices for configuring and using vector search in Azure Cosmos DB for AI-powered semantic search and RAG.
Reference these guidelines when:
For all rules expanded: AGENTS.md
npx claudepluginhub azurecosmosdb/cosmosdb-agent-kit --plugin azure-cosmosdbAzure Cosmos DB performance optimization and best practices for NoSQL, including partitioning, query optimization, SDK usage, and data modeling.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
Designs and optimizes vector database architectures for semantic search, RAG, and recommendation systems using Pinecone, Weaviate, Qdrant, Milvus, and pgvector.