Rag agents

Skill Goodnight77/rag-skills/skills/rag-agents

Route agentic RAG work across query routing, tool-use retrieval, and multi-agent orchestration.From its SKILL.md

Install

npx -y skills add Goodnight77/rag-skills --skill rag-agents

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SKILL.md

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RAG Agents

Overview

Use this parent skill when a single retrieve-then-generate pass is not enough and the system must decide what to retrieve, which tools to call, or how to split work across agents. Route to the child skill that matches whether the problem is routing decisions or multi-agent orchestration.

Problem Statement

Static RAG pipelines answer every query the same way: one retrieval, one generation. Complex questions need conditional retrieval, multiple sources, tool calls, or decomposition into subtasks. Bolting this onto a linear pipeline produces brittle, hard-to-debug systems.

Key Concepts

Routing: Deciding whether and where to retrieve before answering.
Tool use: Treating retrievers, search, and calculators as callable tools.
Decomposition: Breaking a complex query into ordered subtasks.
Orchestration: Coordinating specialized agents and merging their outputs.

Implementation Guide

Step 1: Decide If You Need an Agent at All

Add agentic control only when queries genuinely vary in required steps or sources. A fixed pipeline is cheaper and easier to evaluate.

Step 2: Start with Routing, Not Multi-Agent

Add a router that chooses retrieval strategy or tool per query before reaching for multiple coordinated agents.

Step 3: Introduce Orchestration Only When Justified

Move to planner and worker agents when subtasks are genuinely independent or need specialized handling, and keep each agent narrowly scoped.

When to Use This Skill

Queries need conditional retrieval, multiple sources, or tool calls
A single retrieval pass produces incomplete or shallow answers
Work naturally decomposes into specialized subtasks

When NOT to Use This Skill

A fixed retrieve-then-generate pipeline already answers well
Latency and cost budgets cannot absorb extra LLM calls
The real bottleneck is retrieval quality, not control flow

Related Skills

Metrics & Success Criteria

Agentic control measurably improves answers over a fixed pipeline
Each added LLM call earns its latency and cost
Routing and agent decisions are traceable and debuggable