vect-recon
De la autor
Audit 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".
Instalare
npx skills add tonone-ai/tonone --skill vect-recon -a claude-code
Se instalează în .claude/skills/vect-recon/ · 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.
- vect-searchtonone-ai/tononeDesign 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".Date★ 76
- rag-evalagentscope-ai/OpenJudgeUse when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.Altele★ 888
- rank-recontonone-ai/tononeAudit ranking quality — metric trends, failure modes, dataset coverage, reranker performance. Use when asked to "audit our ranking quality", "find search relevance failures", or "review reranker performance".Inginerie★ 76
- 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
- patch-recontonone-ai/tononeAudit existing vulnerability management — find SLA gaps, missing tiers, and process failures. Use when asked to "audit our vulnerability management", "find patch SLA gaps", or "are we patching fast enough".Securitate★ 76
Note și recenzii
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Discuție
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