Andrej Karpathy 推荐的 Claude Code 技能配置文件,通过一个 CLAUDE.md 文件提升 AI 编程助手的代码质量。涵盖最佳实践、常见陷阱规避、代码审查规则等,是使用 Claude Code 的必备配置。
Andrej Karpathy 推荐的 Claude Code 技能配置文件,通过一个 CLAUDE.md 文件提升 AI 编程助手的代码质量。涵盖最佳实践、常见陷阱规避、代码审查规则等,是使用 Claude Code 的必备配置。
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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
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."
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 |
Don't assume. Don't hide confusion. Surface tradeoffs.
LLMs often pick an interpretation silently and run with it. This principle forces explicit reasoning:
Minimum code that solves the problem. Nothing speculative.
Combat the tendency toward overengineering:
The test: Would a senior engineer say this is overcomplicated? If yes, simplify.
Touch only what you must. Clean up only your own mess.
When editing existing code:
When your changes create orphans:
The test: Every changed line should trace directly to the user's request.
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.
Option A: Claude Code Plugin (recommended)
From within Claude Code, first add the marketplace:
/plugin marketplace add forrestchang/andrej-karpathy-skills
Then install the plugin:
/plugin install andrej-karpathy-skills@karpathy-skills
This 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.md
Existing project (append):
echo "" >> CLAUDE.md
curl https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md >> CLAUDE.md
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.
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 Exec
# 克隆仓库
git clone https://github.com/multica-ai/andrej-karpathy-skills
# 进入目录
cd andrej-karpathy-skills
# 查看文档
cat README.md