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pgvector · Retrieval-augmented generation
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 | 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 | Vector database implementation for AI/ML applications, semantic search, and RAG systems. | ancoleman/ | 525 | — | ~3.5k | Automated safety check: Pass | MIT | 10 mo ago |
| 10 | Choose and wire AI features in the .NET backend — LLM calls, agentic tool-calling/multi-step workflows, RAG/vector search, structured output, streaming. | atherio-danp/ | 109 | — | ~1.2k | Automated safety check: Notes | No licence | 2 mo ago |
| 11 | 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 |
| 12 | Security-first SOP for multi-tenant RAG systems. An agent skill from agentsope/SkillAlchemy. | agentsope/ | 436 | — | ~9.8k | Automated safety check: Pass | MIT | 2 days ago |
| 13 | 13.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 |
| 14 | Retrieval-Augmented Generation patterns for grounded LLM responses. | yonatangross/ | 292 | — | ~4.4k | Automated safety check: Pass | MIT | yesterday |
| 15 | Provides configuration patterns for LangChain4J vector stores in RAG applications. | giuseppe-trisciuoglio/ | 357 | — | ~2.7k | Automated safety check: Notes | MIT | 1 mo ago |
| 16 | Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis. | evolution-foundation/ | 545 | — | ~804 | Automated safety check: Notes | Unknown | 5 mo ago |