andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
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- 22.3k forks
- 137 issues abiertos
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Instalación
git clone https://github.com/multica-ai/andrej-karpathy-skills- Clone the repository.
- Follow the README for installation and usage instructions.
Nota editorial
This repository provides a single CLAUDE.md file containing four behavioral guidelines for Claude Code, distilled from Andrej Karpathy's public observations about recurring LLM coding failures. The four principles are Think Before Coding (surface assumptions and ambiguities rather than silently guessing), Simplicity First (write the minimum code that solves the problem, avoiding unrequested abstractions), Surgical Changes (modify only what the task requires and leave adjacent code untouched), and Goal-Driven Execution (frame tasks as verifiable success criteria with test-first loops). Installation works either as a Claude Code plugin via the marketplace command or by downloading the file directly into any project directory. A Cursor project rule is also included for teams using that editor. The standout design choice is the Goal-Driven Execution table, which reframes vague imperatives like "fix the bug" into test-reproducible targets, exploiting Claude's ability to iterate autonomously against specific criteria. Developers looking to reduce noisy diffs, over-engineered pull requests, and silent assumption errors are the primary audience.
Resumen de Tools
Karpathy-Inspired Claude Code Guidelines
Check out my new project Multica — an open-source platform for running and managing coding agents with reusable skills.
Follow me on X: https://x.com/jiayuan_jy
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
English | 简体中文
The Problems
From Andrej's post:
"The models make wrong assumptions on your behalf and just run along with them without checking. They don't manage their confusion, don't seek clarifications, don't surface inconsistencies, don't present tradeoffs, don't push back when they should."
"They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do."
"They still sometimes change/remove comments and code they don't sufficiently understand as side effects, even if orthogonal to the task."
The Solution
Four principles in one file that directly address these issues:
| Principle | Addresses |
|---|---|
| Think Before Coding | Wrong assumptions, hidden confusion, missing tradeoffs |
| Simplicity First | Overcomplication, bloated abstractions |
| Surgical Changes | Orthogonal edits, touching code you shouldn't |
| Goal-Driven Execution | Leverage through tests-first, verifiable success criteria |
The Four Principles in Detail
1. Think Before Coding
Don't assume. Don't hide confusion. Surface tradeoffs.
LLMs often pick an interpretation silently and run with it. This principle forces explicit reasoning:
State assumptions explicitly — If uncertain, ask rather than guess
Present multiple interpretations — Don't pick silently when ambiguity exists
Push back when warranted — If a simpler approach exists, say so
Stop when confused — Name what's unclear and ask for clarification
2. Simplicity First
Minimum code that solves the problem. Nothing speculative.
Combat the tendency toward overengineering:
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 200 lines could be 50, rewrite it
The test: 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 imperative tasks into verifiable goals:
| Instead of... | Transform to... |
|---|---|
| "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 the LLM loop independently. Weak criteria ("make it work") require constant clarification.
Install
Option A: Claude Code Plugin (recommended)
From within Claude Code, first add the marketplace:
/plugin marketplace add forrestchang/andrej-karpathy-skillsThen install the plugin:
/plugin install andrej-karpathy-skills@karpathy-skillsThis installs the guidelines as a Claude Code plugin, making the skill available across all your projects.
Option B: CLAUDE.md (per-project)
New project:
curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.mdExisting project (append):
echo "" >> CLAUDE.md
curl https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md >> CLAUDE.mdUsing with Cursor
This repository includes a committed Cursor project rule (.cursor/rules/karpathy-guidelines.mdc) so the same guidelines apply when you open the project in Cursor. See CURSOR.md for setup, using the rule in other projects, and how this relates to Claude Code.
Key Insight
From Andrej:
"LLMs are exceptionally good at looping until they meet specific goals... Don't tell it what to do, give it success criteria and watch it go."
The "Goal-Driven Execution" principle captures this: transform imperative instructions into declarative goals with verification loops.
How to Know It's Working
These guidelines are working if you see:
Fewer unnecessary changes in diffs — Only requested changes appear
Fewer rewrites due to overcomplication — Code is simple the first time
Clarifying questions come before implementation — Not after mistakes
Clean, minimal PRs — No drive-by refactoring or "improvements"
Customization
These guidelines are designed to be merged with project-specific instructions. Add them to your existing CLAUDE.md or create a new one.
For project-specific rules, add sections like:
## Project-Specific Guidelines
- Use TypeScript strict mode
- All API endpoints must have tests
- Follow the existing error handling patterns in `src/utils/errors.ts`Tradeoff Note
These guidelines bias toward caution over speed. For trivial tasks (simple typo fixes, obvious one-liners), use judgment — not every change needs the full rigor.
The goal is reducing costly mistakes on non-trivial work, not slowing down simple tasks.
License
MIT
Lo que la gente pregunta sobre andrej-karpathy-skills
¿Qué es multica-ai/andrej-karpathy-skills?
multica-ai/andrej-karpathy-skills es tools para el ecosistema de Claude AI. A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. Tiene 222.7k estrellas en GitHub y su última actualización registrada es del 2026-04-20.
¿Cómo se instala andrej-karpathy-skills?
Puedes instalar andrej-karpathy-skills clonando el repositorio (https://github.com/multica-ai/andrej-karpathy-skills) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar multica-ai/andrej-karpathy-skills?
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¿Quién mantiene multica-ai/andrej-karpathy-skills?
multica-ai/andrej-karpathy-skills es mantenido por multica-ai. La última actividad registrada en GitHub es del 2026-04-20, con 137 issues abiertos.
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