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rag-system-design

Design and evaluate retrieval-augmented generation systems end-to-end.

Skill by fcistud

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/fcistud-rag-system-design

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Design and evaluate retrieval-augmented generation systems end-to-end.

Use cases: design, ml

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/rag-system-design

What's inside

Skill name
rag-system-design
Description
Design and evaluate retrieval-augmented generation systems end-to-end.
License
MIT
Instructions
28 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Rag System Design

## When To Use
Use this skill when the task requires this exact workflow and you need repeatable, high-confidence outputs.

## Required Inputs
- Problem statement with objective and constraints
- Relevant artifacts (code, docs, metrics, logs, datasets)
- Success criteria and timeline

## Workflow
1. Clarify scope, assumptions, and non-goals.
2. Build a quick baseline snapshot of the current state.
3. Prioritize the top risks/opportunities using impact and feasibility.
4. Execute a minimal, testable improvement plan.
5. Produce artifacts that justify decisions and support handoff.

## Output Checklist
- Decision log with rationale and tradeoffs
- Action plan with owner, sequence, and rollback path
- Validation evidence (tests, metrics, or review checks)

## Quality Bar
- Recommendations are actionable, not generic.
- Claims are tied to evidence.
- Risks and failure modes are explicitly called out.

From fcistud/awesome-ai-technical-skills, MIT.

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Last commit (this folder)
Feb 26, 2026 (8 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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rag-system-design skill. Agent Mart. https://agentmart.dev/skills/fcistud-rag-system-design (last updated 2026-10-24)