rag-patterns

rag-patterns is a RAG & retrieval skill for AI agents, published by Shubchynskyi in the garda-agent-orchestrator repository on GitHub. Skillspace rates rag-patterns D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Production patterns for retrieval-augmented generation systems: corpus boundary definition, ingestion quality, chunking trade-offs, embedding pipeline design, vector store configuration, retrieval ranking and reranking, context packing within token budgets, grounding and citation enforcement, staleness management, and evaluation of retrieval quality vs answer quality. Use when a task creates or modifies chunking logic, embedding pipelines, vector store configuration, retriever or reranker components, ingestion jobs, prompt templates that assemble retrieved context, or evaluation harnesses that measure retrieval recall/precision. Trigger phrases: rag pipeline, vector search, semantic retrieval, chunking strategy, embedding pipeline, reranker, citation grounding, ingestion job, knowledge base, document index. Do NOT use for general prompt engineering without retrieval (use llm-app-basics), multi-step tool orchestration (use tool-calling-patterns), or evaluation framework design unrelated…

Grade

D

Quality

68 / 100

Safety

Passed

GitHub stars

24

Unproven: rated by static analysis only, not yet compared head-to-head.

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