karpathy-review

SkillAI & models

Review 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.

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

PrincipleResult
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
Advanced
Catalog kind
skill
Gateway key
karpathy-review-hamzafarooq
Source
github.com/hamzafarooq/multi-agent-course