How do I install the AI RAG And Agents skill?

Run npx skillmds@latest add ebarti/ai-rag-and-agents in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI RAG And Agents safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI RAG And Agents?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI RAG And Agents free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI RAG And Agents?

ebarti (@ebarti) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI RAG And Agents by ebarti — Agent Skill

Name: AI RAG And Agents
Author: ebarti

AI RAG And Agents

Practical knowledge for building RAG (Retrieval-Augmented Generation) systems and AI agents. Covers RAG architecture and retrieval algorithms (term-based, embedding-based, hybrid), retrieval optimization (chunking, reranking, query rewriting), multimodal RAG, agent design with tools and planning, agent failure modes, and memory systems. Use this skill when: - Building a RAG system from scratch or improving an existing one - Choosing retrieval algorithms (BM25, dense vectors, hybrid) - Optimizing retrieval (chunking strategy, reranker selection) - Designing an AI agent (tools, planning, error correction) - Debugging agent failures - Implementing memory for conversational systems

ebarti Updated Sep 17, 2026

File contents

AI RAG and Agents

Knowledge from "AI Engineering" by Chip Huyen (Chapter 6). Practical guidance for building retrieval-augmented and agentic systems.

Quick Start

Check guidelines.md to find which files to load for your task
Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
Apply guidance to your work

References

Category	Purpose
`rag-architecture`	RAG overview, architecture, when to use RAG
`retrieval-algorithms`	Term-based (BM25, TF-IDF), embedding-based, hybrid retrieval
`retrieval-optimization`	Chunking strategies, reranking, query rewriting, contextual retrieval, multimodal RAG, tabular RAG
`agent-overview`	Agent definition, tools (knowledge augmentation, capability extension, write actions)
`agent-planning`	Plan generation, reflection, error correction, tool selection
`agent-failures`	Planning failures, tool failures, efficiency issues, evaluation
`agent-memory`	Memory systems for agents and conversations

Workflows

Task	Workflow
Build a RAG system end-to-end	`workflows/build-rag.md`
Build an agent end-to-end	`workflows/build-agent.md`
Diagnose and fix agent failures	`workflows/debug-agent.md`

Guidelines

See guidelines.md for task-based file selection.

---name: ai-rag-and-agentsdescription: Practical knowledge for building RAG (Retrieval-Augmented Generation) systems and AI agents. Covers RAG architecture and retrieval algorithms (term-based, embedding-based, hybrid), retrieval optimization (chunking, reranking, query rewriting), multimodal RAG, agent design with tools and planning, agent failure modes, and memory systems. Use this skill when: - Building a RAG system from scratch or improving an existing one - Choosing retrieval algorithms (BM25, dense vectors, hybrid) - Optimizing retrieval (chunking strategy, reranker selection) - Designing an AI agent (tools, planning, error correction) - Debugging agent failures - Implementing memory for conversational systems---# AI RAG and AgentsKnowledge from "AI Engineering" by Chip Huyen (Chapter 6). Practical guidance for building retrieval-augmented and agentic systems.## Quick Start1. Check `guidelines.md` to find which files to load for your task2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)3. Apply guidance to your work## Contents### References| Category | Purpose ||----------|---------|| `rag-architecture` | RAG overview, architecture, when to use RAG || `retrieval-algorithms` | Term-based (BM25, TF-IDF), embedding-based, hybrid retrieval || `retrieval-optimization` | Chunking strategies, reranking, query rewriting, contextual retrieval, multimodal RAG, tabular RAG || `agent-overview` | Agent definition, tools (knowledge augmentation, capability extension, write actions) || `agent-planning` | Plan generation, reflection, error correction, tool selection || `agent-failures` | Planning failures, tool failures, efficiency issues, evaluation || `agent-memory` | Memory systems for agents and conversations |### Workflows| Task | Workflow ||------|----------|| Build a RAG system end-to-end | `workflows/build-rag.md` || Build an agent end-to-end | `workflows/build-agent.md` || Diagnose and fix agent failures | `workflows/debug-agent.md` |## GuidelinesSee `guidelines.md` for task-based file selection.

Frequently asked questions