rag-retrievallisted

RAG & retrieval engineering capability pack. Gives AI agents the judgment rules a senior retrieval engineer applies automatically — chunking strategy selection, embedding model choice, vector database routing, hybrid search with Reciprocal Rank Fusion, two-stage cross-encoder reranking, GraphRAG, and reference-based + LLM-as-judge RAG evaluation. Research-grounded rules with specific numbers from chunking benchmarks, embedding/reranker/vector-DB comparisons, and Ragas-style evaluation. Use for any RAG pipeline design, retrieval quality debugging, chunking/embedding/vector-DB selection, hybrid search fusion, reranker selection, or RAG eval task.
Sheldon-92/TAD · ★ 3 · AI & Automation · score 79

Install: claude install-skill Sheldon-92/TAD

**CONSUMES**: User RAG/retrieval task + corpus description (size, format, language, domain) + optional existing pipeline config (chunker, embedder, vector DB, reranker, eval suite) **PRODUCES**: Applied retrieval judgment rules + chunking strategy decision + embedding model selection + vector DB routing + hybrid-search/RRF config + reranker selection + RAG eval suite with target thresholds # RAG & Retrieval Engineering Capability Pack **Version**: 0.1.0 **Compatibility**: Claude Code (Phase 1); Codex / Cursor / Gemini in Phase 3 **License**: Apache 2.0 --- ## What This Pack Does AI agents build RAG pipelines by copying a tutorial: fixed-size chunks, `text-embedding-3-small`, Chroma, top-k similarity, and a prompt. They reach for semantic chunking because it sounds advanced (it benchmarked **< 55%** vs recursive-512's **69%**). They fuse BM25 and vector scores by adding them directly (mathematically invalid — BM25 is unbounded). They rerank the top-200 (paying latency for ~10% of the accuracy gain). They never separate retrieval evaluation from generation evaluation, and they report a single blended "RAG score" with no domain-calibrated Faithfulness gate — so they can't tell whether the retriever or the generator is the problem, or whether the answer is even grounded. This pack embeds the judgment rules retrieval engineers apply automatically — rules grounded in 2026 chunking benchmarks, embedding/reranker/vector-DB comparisons, and Ragas-style evaluation, with the speci