vect-search
De la autor
Design a vector search or RAG system — retrieval strategy, reranking, and database selection. Use when asked to "build a RAG system", "design vector search", or "which vector database should we use".
Instalare
npx skills add tonone-ai/tonone --skill vect-search -a claude-code
Se instalează în .claude/skills/vect-search/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 83
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- rag-architectJeffallan/claude-skillsDesigns and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.Planificare★ 12K
- vect-recontonone-ai/tononeAudit existing vector search or RAG implementation — find quality gaps and performance issues. Use when asked to "audit our RAG system", "why is retrieval bad", or "find vector search quality gaps".Altele★ 76
- rag-engineertranhieutt/software_development_departmentProvides Retrieval-Augmented Generation patterns covering embedding models, vector databases, chunking strategies, and retrieval optimization. Use when building RAG systems or when the user mentions RAG, vector search, embeddings, or retrieval-augmented generation.Altele★ 74
- retrieval-ragnahid-sparktales/agent-dispatcherBuild and fix retrieval that actually returns the right passage — structure-aware chunking, one pinned embedding model, lexical plus vector search fused, reranking, and a recall measurement that is run separately from the generator. Use when a RAG system answers wrong or vaguely, when an index is being designed or reindexed, or when someone proposes a prompt change to fix what is really a retrieval miss. Not for prompt or output-quality work once the right passage is already in context, not for agent memory design, and not for choosing a vector database.Date★ 59
- hybrid-search-implementationwshobson/agentsCombine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.Altele★ 41.6K
- rag-implementationwshobson/agentsBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.Altele★ 41.6K
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