rag-infrastructure
De la autor
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
Instalare
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill rag-infrastructure -a claude-code
Se instalează în .claude/skills/rag-infrastructure/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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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
- 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
- rag-design-docmohitagw15856/pm-claude-skillsDesign a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & reranking, the generation prompt, grounding/citations, evaluation, and failure modes with mitigations.Design★ 1.5K
- google-cloud-solution-rag-enterprise-search-gke-sqldbgoogle/skillsDiscovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.OficialDevOps★ 21.5K
- rag-observability-evalsBagelHole/DevOps-Security-Agent-SkillsMonitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and continuous regression testing.DevOps★ 1.2K
- 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
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