domains / families / data / rag-architect
Designs 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. RAG Architect - POWERFUL
The two copies overlap on the RAG lifecycle (chunking, embeddings, vector stores, retrieval, evaluation) but each contains material the other lacks. Jeffallan/claude-skills provides a concrete, executable implementation path with Python examples, checkpoints, hybrid search, reranking, and evaluation code. LeoYeAI/openclaw-master-skills provides a broader reference catalog of chunking strategies, embedding model trade-offs, and vector database comparisons, but lacks the end-to-end implementation steps and validation checkpoints. Crowning one would hide either the practical pipeline or the selection guidance.