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).
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill rag-architect- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads rag-architect automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- 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.
- Every design ends with an evaluation run. A RAG design without
retrieval_evaluator.pynumbers is a hypothesis, not a deliverable. - Chunking is corpus-driven. Run
chunking_optimizer.pyon 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
python3 chunking_optimizer.py /path/to/docs --extensions .md .txt -o chunking.json
Emits chunking.json with corpus_info, per-strategy strategy_results, a recommendation, and sample_chunks. Use recommendation.strategy and its config; show the user 2-3 sample_chunks so they can sanity-check boundaries.
2. Design the pipeline from requirements
Write a requirements JSON with these keys (all required): document_types[], document_count, avg_document_size (chars), queries_per_day, query_patterns[], latency_requirement, budget_monthly, accuracy_priority (0-1), cost_priority (0-1), maintenance_complexity.
python3 rag_pipeline_designer.py requirements.json -o design.json
Emits design.json with chunking, embedding, vector_db, retrieval, reranking, evaluation, total_cost, architecture_diagram (mermaid), and config_templates. Present the diagram; label every cost_monthly figure as an estimate to verify (rule 1).
3. Evaluate retrieval quality
Prepare queries.json (list of {id, text} or {"queries": [...]}) and ground_truth.json ({query_id: [relevant_doc_ids]}), then:
python3 retrieval_evaluator.py queries.json /path/to/docs ground_truth.json --k-values 3 5 10 -o eval.json
Reports precision@k, recall@k, MRR, NDCG@k, plus poor_precision_examples / poor_recall_examples for failure analysis.
4. Verification loop
The design is done only when:
eval.jsonmeets targets — typical floors: precision@5 ≥ 0.8, recall@10 ≥ 0.85 (set per use case with the user).- If below target: inspect the poor-example lists, then change one variable (chunking strategy → re-run step 1; embedding tier; add reranking; hybrid retrieval) and re-run step 3. Repeat.
- Every recommended model/price in the deliverable carries a "verify current pricing/model availability" note with an as-of date.
References
references/chunking_strategies_comparison.md— strategy trade-offs the optimizer implementsreferences/embedding_model_benchmark.md— benchmark methodology (dated snapshot; staleness warning at top)references/rag_evaluation_framework.md— metric definitions (faithfulness, relevance, precision/recall/NDCG)
FAQ
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 rag-architect?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill rag-architect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-architect/ (personal) or .claude/skills/rag-architect/ (project).
Which AI agents can use rag-architect?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is rag-architect safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains rag-architect?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 630 recorded installs.
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