rag-architect
Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
Profile →npx skills add alirezarezvani/claude-skills --skill rag-architect --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 `retrieval_evaluator.py` numbers is a hypothesis, not a deliverable. 3. **Chunking is corpus-driven.** Run `chunking_optimizer.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`, CodeBE
- Hard rules
- Embedding model tiers (pattern, not price list)
- Workflow
- 1. Analyze the corpus and pick chunking
- 2. Design the pipeline from requirements
- 3. Evaluate retrieval quality
- 4. Verification loop
- References
python3 chunking_optimizer.py /path/to/docs --extensions .md .txt -o chunking.json python3 rag_pipeline_designer.py requirements.json -o design.json python3 retrieval_evaluator.py queries.json /path/to/docs ground_truth.json --k-values 3 5 10 -o eval.json
What does the rag-architect skill do?
Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill rag-architect --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.