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pgvector · For data scientists
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. | timescale/ | 1.9k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 2 | A skill your agent uses to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). | timescale/ | 1.9k | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 3 | Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline. | wshobson/ | 40k | 9 repos | ~1.1k | Automated safety check: Pass | MIT | 6 days ago |
| 4 | 4.Postgres A skill your agent uses for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. | timescale/ | 1.9k | — | ~941 | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 5 | Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step. | Jeffallan/ | 12k | — | ~2k | Automated safety check: Pass | MIT | 7 days ago |
| 6 | Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j. | rileyhilliard/ | 130 | — | ~1.6k | Automated safety check: Pass | MIT | 1 mo ago |
| 7 | Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from… | elementalsouls/ | 4.8k | — | ~2.6k | Automated safety check: Pass | MIT | yesterday |
| 8 | Vector database implementation for AI/ML applications, semantic search, and RAG systems. | ancoleman/ | 525 | — | ~3.5k | Automated safety check: Pass | MIT | 10 mo ago |
| 9 | Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. | github/ | 40k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 10 | Retrieval-Augmented Generation patterns for grounded LLM responses. | yonatangross/ | 292 | — | ~4.4k | Automated safety check: Pass | MIT | yesterday |
| 11 | 11.Vector DB A skill your agent uses 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… | ericrisco/ | 180 | — | ~2.8k | Automated safety check: Pass | MIT | yesterday |
| 12 | 12.Supabase Connect to Supabase for database operations, vector search, and storage. | sundial-org/ | 663 | — | ~1.6k | Automated safety check: Pass | No licence | 7 mo ago |
| 13 | Administrative operations on the Knowledge base: connect new pgvector servers, check health, view stats, export data, install parser models. | evolution-foundation/ | 545 | — | ~1k | Automated safety check: Pass | Unknown | 5 mo ago |