Agent skill · software engineering · borghei
rag-architect
Design RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
Why this skill is useful
Adds 3 executable Python scripts for optimizing chunking strategies, evaluating retrieval quality, and designing RAG pipelines that are not commonly found in public documentation.
What it needs
Requires python installed locally. About 4k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
RAG Architect The agent designs, implements, and optimizes production-grade RAG pipelines, from document chunking through evaluation. Core Capabilities Chunking strategy selection — match corpus characteristics to fixed-size, sentence, paragraph, semantic, recursive, or document-aware chunking with sized parameters. Embedding & vector-DB choice — pick an embedding model (local vs API) and vector store (Pinecone, Weaviate, Qdrant, Chroma, pgvector) by scale, latency, and cost. Retrieval design — dense, sparse (BM25), or hybrid retrieval with Reciprocal Rank Fusion plus cross-encoder reranking when precision must exceed 0.85. Query transformations — HyDE, multi-query, and step-back techniques for style mismatch and ambiguous queries. Guardrails — PII detection, hallucination/NLI checks, source attribution, confidence scoring, and injection prevention. Evaluation — RAGAS faithfulness/relevance plus IR metrics (Precision@K, Recall@K, MRR, NDCG) with failure analysis. Production patterns — caching, streaming, fallbacks, incremental re-indexing, and cost control. When to Use Building a RAG system end to end. Selecting a chunking strategy or choosing a vector database. Optimizing retrieval quality or adding reranking. Evaluating a pipeline with RAGAS or IR metrics. Clarify First Before designing the pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Corpus characteristics — size, document structure, and domain (drives the chunking-strategy selection and parameters) [ ] Scale / latency / cost constraints — query volume and budget (selects the embedding model and vector DB) [ ] Retrieval precision target — the accuracy bar (precision >0.85 forces hybrid retrieval + cross-encoder reranking) [ ] Query types — ambiguous, multi-hop, or style-mismatched (decides which query transforms: HyDE / multi-query / step-back) Stop rule: ask only the 2-3 that most change the output. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/rag-architect