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pgvector · Embeddings
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Persistent compounding memory for AI agents. An agent skill from Goldentrii/AgentRecall-X. | Goldentrii/ | 371 | — | ~5.2k | Automated safety check: Notes | MIT | 13 days ago |
| 2 | 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 |
| 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 | Builds and debugs retrieval with the gaik toolkit — PgVectorStore, Ranker, FinnishTextProcessor, RelevanceGate — as hybrid search: pgvector similarity plus Postgres full-text, fused by rank, and the… | GAIK-project/ | 100 | — | ~4.2k | Automated safety check: Pass | MIT | 2 days ago |
| 6 | 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 | 8 days 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 | Probes Retrieval-Augmented Generation pipelines for indirect prompt injection via poisoned retrieved documents and embedding-space manipulation, using NVIDIA garak, Promptfoo red-team plugins, and… | mukul975/ | 34k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | 1 mo ago |
| 9 | Expert in vector databases, embedding strategies, and semantic search implementation. | aiskillstore/ | 433 | 7 repos | ~563 | Automated safety check: Pass | No licence | yesterday |
| 10 | 10.RAG Patterns RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop. | softspark/ | 179 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 11 | Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. | jeremylongshore/ | 2.8k | — | ~2.6k | Automated safety check: Pass | MIT | yesterday |
| 12 | 12.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 | 2 days ago |
| 13 | 13.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 |