# Build Your RAG Skill

> Create your RAG skill in one prompt, then learn to improve it throughout the chapter

- Skill: `diegosouzapw/build-your-rag-skill` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/build-your-rag-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/build-your-rag-skill/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/diegosouzapw/build-your-rag-skill

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# Build Your RAG Skill

Before learning RAG (Retrieval-Augmented Generation)—the architecture that gives AI agents access to your private data—you will **own** a RAG skill.

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## Step 1: Get the Skills Lab

1. Go to [github.com/panaversity/claude-code-skills-lab](https://github.com/panaversity/claude-code-skills-lab)
2. Click the green **Code** button
3. Select **Download ZIP**
4. Extract the ZIP file
5. Open the extracted folder in your terminal

```bash
cd claude-code-skills-lab
claude
```

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## Step 2: Create Your Skill

Copy and paste this prompt:

```
Using your skill creator skill create a new skill for RAG (Retrieval-Augmented
Generation) systems. I will use it to build production RAG pipelines with
LangChain and Qdrant vector database - from simple semantic search to advanced
patterns like HyDE, CRAG, and Agentic RAG. Use context7 skill to study official
LangChain and Qdrant documentation and then build it so no self assumed knowledge.
```

Claude will:
1. Fetch official LangChain and Qdrant documentation via Context7
2. Ask you clarifying questions (chunking strategies, embedding models, retrieval patterns)
3. Create the complete skill with ingestion pipelines, retrieval patterns, and evaluation templates

Your skill appears at `.claude/skills/rag-deployment/`.

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## Done

You now own a RAG skill built from official documentation. The rest of this chapter teaches you what it knows—and how to make it better.

**Next: Lesson 1 — Understanding RAG Architecture**