Skip to content
Skillpedia
General coding220k · #313

karpathy-guidelines

Helps your agent write better code by avoiding common mistakes. It's useful for ensuring code is simple, clear, and easy to verify.

What it does

This guide helps AI write better code by avoiding common mistakes. It teaches the AI to think before coding, keep code simple, make only necessary changes, and clearly define what a good result looks like.

When to use it

  • ✓When the AI is writing new code.
  • ✓When the AI needs to fix or improve existing code.
  • ✓When you want the AI to avoid unnecessary complications.

Example of what to ask your AI

“AI, write a function to calculate the average of a list of numbers, but think carefully before starting and only add what's essential.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/multica-ai/andrej-karpathy-skills/blob/main/skills/karpathy-guidelines/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/multica-ai/andrej-karpathy-skills/blob/main/skills/karpathy-guidelines/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI follows steps: think before writing, focus on the minimum needed, change only what's necessary, and define how to know when the job is done.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines

Or download just the file:

curl -L https://raw.githubusercontent.com/multica-ai/andrej-karpathy-skills/main/skills/karpathy-guidelines/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Karpathy Guidelines

Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing:

  • State your assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them - don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No "flexibility" or "configurability" that wasn't requested.
  • No error handling for impossible scenarios.
  • If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

  • Don't "improve" adjacent code, comments, or formatting.
  • Don't refactor things that aren't broken.
  • Match existing style, even if you'd do it differently.
  • If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:

  • Remove imports/variables/functions that YOUR changes made unused.
  • Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

  • "Add validation" → "Write tests for invalid inputs, then make them pass"
  • "Fix the bug" → "Write a test that reproduces it, then make it pass"
  • "Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

FAQ

Why should the AI think before coding?

To avoid mistakes, fully understand the problem, and not create solutions that are more complicated than needed.

What does 'simple code' mean?

Writing the least amount of code possible to solve the problem, without adding extra features or unnecessary complexity.

How does the AI know if the code is good?

By setting clear, verifiable goals. For example, instead of 'fix the bug,' the goal is 'write a test that shows the bug, then make the test pass'.

Details

Original description by the author

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

repo: multica-ai/andrej-karpathy-skills

Popularity

GitHub stars
220k
Ranking position
#313
License
—
Updated
4/20/2026

Is this skill useful?

0

Sign in to vote.

Comments

  • …

Similar skills