Agent skill · research science · alirezarezvani
rag-architect
Design a RAG pipeline: chunk size, embedding model, vector database and retrieval quality metrics. Use whenever the user builds search over their docs, picks a vector store or finds answers miss the right context.
Why this skill is useful
Adds executable scripts for corpus analysis, pipeline design, and retrieval evaluation that enhance the AI's capabilities in designing RAG systems.
What it needs
Requires python3 installed locally. About 2k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
RAG Architect Design, tune, and evaluate production RAG pipelines with three deterministic tools. Run the tools against the actual corpus and requirements — do not pick chunk sizes or databases by intuition. Hard rules 1. Never present model names or vendor prices as current facts. Embedding models and vector-DB pricing rot in months. Recommend a tier (see table below), name a current-generation candidate, and tell the user to verify against the provider's live pricing page. 2. Every design ends with an evaluation run. A RAG design without retrievalevaluator.py numbers is a hypothesis, not a deliverable. 3. Chunking is corpus-driven. Run chunkingoptimizer.py on the real documents before choosing a strategy. Embedding model tiers (pattern, not price list) Tier Current-generation examples (verify before use) When --- --- --- Fast / self-hosted all-MiniLM-L6-v2, bge-small Cost-sensitive, small scale, real-time Balanced open all-mpnet-base-v2, bge-large, e5-large Quality without API dependency Quality API text-embedding-3-large, voyage-3-large Accuracy-priority general retrieval Code voyage-code-3, CodeBERT-family Code search corpora Pricing discipline: build the cost model with a placeholder table — columns model $/1M tokens (verify) dims as-of date — and have the user fill in live numbers. Same for vector DBs (Pinecone/Weaviate/Qdrant/Chroma/pgvector): the selection criteria (managed vs self-hosted, scale, filtering, existing Postgres) are durable; the dollar figures are not. Workflow All paths relative to this skill folder. Outputs chain: corpus analysis → design → evaluation. 1. Analyze the corpus and pick chunking Emits chunking.json with corpusinfo, per-strategy strategyresults, a recommendation, and samplechunks. Use recommendation.strategy and its config; show the user 2-3 samplechunks so they can sanity-check boundaries. 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/rag-architect--76ac30