Rag agent pipeline

Skill m00kk/agent-skills-playbook/skills/rag-agent-pipeline

15 production Agent Skills — MCP, LangGraph, RAG, security, Cursor SDK. MIT licensed.

Install

npx -y skills add m00kk/agent-skills-playbook --skill rag-agent-pipeline

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One thing to look at

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What its author says it does

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Builds retrieval-augmented agent pipelines: document ingest, chunking, embedding, vector store, and grounded answers with citations. Use for RAG agents, knowledge bases, pgvector, Chroma, or enterprise doc Q&A.

SKILL.md

1.8 KB, as published. Nobody here has run it

RAG Agent Pipeline

Pipeline stages

Ingest → Clean → Chunk → Embed → Index → Retrieve → Generate (with citations)

Workflow

- [ ] Choose sources (PDF, MD, HTML, tickets, APIs)
- [ ] Chunk: 512–1024 tokens, overlap 10–15%, preserve headings
- [ ] Embed: one model for index + query (same dimensions)
- [ ] Store: metadata filters (product, date, ACL)
- [ ] Retrieve: hybrid search (vector + keyword) when possible
- [ ] Generate: require citations; refuse if no relevant chunks

Chunking rules

Split on headings first, then size
Attach metadata: source, page, updated_at, acl_group
Do not chunk tables blindly — use row-aware or CSV path

Retrieval

Top-k 5–8 for synthesis; rerank to 3 if reranker available
Score threshold: below threshold → "I don't have enough context"
Filter by user ACL in metadata before LLM sees chunks

Agent tool surface

Expose to agent as MCP or native tools:

search_knowledge(query, filters?) → ranked chunks + scores
get_document(doc_id) → full section when needed

Avoid one tool that dumps entire index.

Evaluation

Golden questions with expected citation sources
Metrics: recall@k, faithfulness, answer relevance
Run eval set on every index or prompt change (see evaluate-agent-quality)

Stack hints (pick one per project)

Store	When
pgvector	Already on Postgres
Chroma / Qdrant	Greenfield vector DB
Managed (Pinecone, etc.)	Ops offload

Do not mix embedding models across index versions without re-embed.