rag-system-design
Design and evaluate retrieval-augmented generation systems end-to-end.
Skill by fcistud
Chalk Labs
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Install
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
Use cases: design, ml
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
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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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- 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
- Source
- GitHubgithub-graphql
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Run a structured debugging workflow from symptom to validated root cause.
SkillDFreshness: DClaude Code +9 - 0.404score
refactor-planning
fcistud
Plan low-risk refactors with staged rollout and regression protection.
SkillDFreshness: DClaude Code +9 - 0.404score
dependency-risk-audit
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Audit dependencies for security, maintenance risk, and upgrade strategy.
SkillDFreshness: DClaude Code +9 - 0.404score
inference-optimization
fcistud
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llm-application-design
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Design production LLM applications with clear architecture and guardrails.
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Cite this page
rag-system-design skill. Agent Mart. https://agentmart.dev/skills/fcistud-rag-system-design (last updated 2026-10-24)