karpathy-review
SkillAI & modelsReview code or a plan against Andrej Karpathy's LLM coding principles — catch over-engineering, silent assumptions, unnecessary abstractions, and scope creep before they ship. Use when the user wants a sanity check on code, a plan, or a feature spec.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the karpathy-review skill
What this skill tells your AI
The instructions your AI receives, as published by hamzafarooq/multi-agent-course in .claude/skills/karpathy-review/SKILL.md and read by ahel’s review.
Review the provided code, plan, or spec against Karpathy's four principles. Credit: guidelines distilled from Andrej Karpathy's January 2026 observations, adapted by Forrest Chang.
Ask the user: "Paste the code, plan, or spec you want reviewed."
Then score it against each principle:
Karpathy Review
1. Think Before Coding — were assumptions stated?
- Did the solution state its assumptions explicitly, or pick silently between interpretations?
- Are there any unclear requirements that should have triggered a clarifying question?
- Finding: PASS / FLAG — [specific observation]
2. Simplicity First — is this the minimal solution?
- Does it add features beyond what was asked?
- Are there abstractions written for single-use code?
- Is there "flexibility" or "configurability" that wasn't requested?
- Could this be meaningfully shorter?
- Finding: PASS / FLAG — [specific observation, e.g. "This 180-line class could be a 20-line function"]
3. Surgical Changes — does it stay in its lane?
- Does it modify adjacent code, formatting, or comments that weren't part of the task?
- Does it refactor things that weren't broken?
- Does it delete pre-existing code that wasn't asked to be removed?
- Finding: PASS / FLAG — [specific observation]
4. Goal-Driven Execution — is success verifiable?
- Is there a clear success criterion?
- For multi-step work: was a plan stated with checkpoints?
- Is there a way to verify the output without running it?
- Finding: PASS / FLAG — [specific observation]
Verdict
| Principle | Result |
|---|---|
| Think before coding | ✅ PASS / ⚠️ FLAG |
| Simplicity first | ✅ PASS / ⚠️ FLAG |
| Surgical changes | ✅ PASS / ⚠️ FLAG |
| Goal-driven execution | ✅ PASS / ⚠️ FLAG |
One thing to fix: [If any flags, name the single most important change to make]
Signals
- GitHub stars
- 84
- Forks
- 70
- Last commit
- Sep 2026
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karpathy-review-hamzafarooq- Source
- github.com/hamzafarooq/multi-agent-course