De la autor
Use when building grounded Q&A over your own corpus — chunk, retrieve hybrid, rerank, ground, cite chunk ids, refuse when the sources fall short — or when the right document is retrieved but the answer is still wrong, invented, or unmeasured. NOT operating the store itself — collection schema, HNSW ef_search, quantization (that is `vector-db`).
Instalare
npx skills add ericrisco/rsc-harness --skill rag -a claude-code
Se instalează în .claude/skills/rag/ · 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
- Include scripturi pe care nu le-am verificatScăzut
De ce 80
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.
- 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
- paper-corpus-ragKalarisLabs/research-agent-skillsBuild grounded question answering and retrieval-augmented generation (RAG) over your own collection of research papers, with answers that cite the exact paper and passage. Use when a user wants to "chat with" or search a folder of PDFs, synthesize evidence across a literature corpus, find which paper says X, or build a vector/hybrid index with SQLite FTS5, pgvector (Postgres), Chroma, Qdrant or FAISS. Includes a zero-dependency local full-text index with citable hits.Știință★ 79
- 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★ 12.1K
- 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★ 42K
- rag-devbutterbase-ai/butterbase-skillsUse when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAGAltele★ 535
- embeddings-searchericrisco/rsc-harnessUse when choosing an embedding model, chunk size, or query form, when semantic search returns irrelevant results, when adding hybrid BM25+vector or a reranker, or when a retrieval change needs a number (recall@k, nDCG, MRR). NOT operating the store — index tuning, quantization (that is `vector-db`) — nor the retrieve-to-answer loop (that is `rag`).Altele★ 395
Note și recenzii
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Discuție
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