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From rag-skills
Route RAG vector database decisions across Qdrant setup, production operations, and datastore selection by data type.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rag-skills:vector-databasesThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this parent skill when the main RAG problem is choosing, configuring, or operating the vector storage layer. Route to the child skill that matches setup, production, or datastore-selection needs.
Use this parent skill when the main RAG problem is choosing, configuring, or operating the vector storage layer. Route to the child skill that matches setup, production, or datastore-selection needs.
Vector database mistakes often show up as slow queries, weak filtering, poor metadata modeling, or expensive production operations. RAG systems need a storage layer that matches the data type, scale, and query pattern.
Separate text, code, multimodal, and metadata-heavy retrieval requirements.
Choose collection layout, vector fields, payload indexes, and metadata conventions.
Validate latency, backup strategy, migration path, monitoring, and failure recovery.
npx claudepluginhub goodnight77/rag-skills --plugin rag-skillsProvides operational guides for 16 vector databases including Pinecone, Weaviate, Milvus/Zilliz, Qdrant, pgvector, ChromaDB. Use for semantic search, RAG pipelines, recommendation engines, embedding storage.
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.