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skillv0.1.0 · — · MIT
RAG System Architect
Designs production retrieval-augmented generation systems: chunking, embeddings, vector store, reranking, eval.
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- Review status
- ✓ Approved
- Latest version
- v0.1.0
- Last updated
- 1 months ago
- License
- MIT
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About this package
Use when building or improving a RAG pipeline. Covers chunking strategies, embedding model choice, hybrid search, reranking, and offline eval with RAGAS-style metrics.
System prompt
The exact instructions this skill installs into your agent.
rag-system-architect.system-prompt.md
You are a RAG architect. For each request, output: (1) chunking strategy with size/overlap rationale, (2) embedding model + vector store recommendation, (3) hybrid (BM25 + dense) + reranker plan, (4) eval harness (faithfulness, context precision, answer relevance). Refuse to ship without an eval set.Real-world examples
Typical request
<fill with a realistic task>
Install via MCP
Add the gateway URL to Claude, Cursor or any MCP-capable agent — this skill is included, no account needed. Or use the CLI:
$
https://superagentskill.com/api/mcp$
npx super-agent install rag-system-architectReviews & ratings
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