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andrej-karpathy

Guides code writing to avoid common mistakes. It helps simplify code and define success criteria.

What it does

This guide helps AI write better code by avoiding common mistakes. It focuses on making small, clear changes, thinking before acting, and ensuring the code works exactly as intended.

When to use it

  • ✓When asking the AI to write new code.
  • ✓When asking the AI to review or improve existing code.
  • ✓When you want the AI to avoid overcomplicating a simple task.

Example of what to ask your AI

“AI, write a simple function to add two numbers, but think about how to do it in the clearest and most direct way possible, without adding extra things.”

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/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/andrej-karpathy/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/andrej-karpathy/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI follows steps like thinking before writing, focusing on the minimum needed to solve the problem, making only the requested changes, and defining how to know if the result is correct.

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 sickn33/agentic-awesome-skills --skill andrej-karpathy

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/andrej-karpathy/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.

When to Use This Skill

  • Use when writing, reviewing, or refactoring code with an LLM.
  • Use when a change needs to stay surgical and avoid speculative abstractions.
  • Use when assumptions, tradeoffs, and verification criteria should be made explicit.
  • Use when code has become overcomplicated and needs to be simplified.

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 such as "make it work" require constant clarification.

Examples

User request: "Add validation to this form."

Better response:
1. Assumption: validation should run before submit and show inline errors.
2. Plan: add a small validator, add tests for invalid inputs, then verify existing submit behavior.
3. Scope: only touch the form component and its test file.
User request: "Refactor this service."

Better response:
1. Ask what behavior must remain unchanged.
2. Identify a concrete smell, such as duplicated parsing logic.
3. Make the smallest refactor and run the existing service tests.

Limitations

  • These guidelines are behavioral guardrails, not a replacement for project-specific architecture or style rules.
  • For emergency fixes, prioritize the smallest verified correction over extensive planning.
  • For exploratory prototypes, some caution can be relaxed, but assumptions and verification should still be explicit.
FAQ

Why should the AI think before coding?

To avoid guessing, hiding confusion, or creating solutions that are more complicated than necessary. It's better to ask and clarify.

What does 'surgical changes' mean?

It means touching only what is strictly necessary to fulfill the request, without altering other parts of the code that were not asked for.

How does the AI know if the code is good?

The AI defines clear criteria to know if the goal has been met. For example, instead of 'fix the bug,' it might define 'create a test that shows the bug and 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: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2126
License
MIT
Updated
10/15/2026

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