Karpathy 风格 AI 编码指南
获得简洁精准、减少过度工程的 AI 编码输出。
使用前,准备这些
输入素材:代码库、技术环境与任务验收条件
- 在下方原始文档中确认支持的 AI 工具、模型和运行依赖。
- 打开原仓库,按作者的安装说明完成配置;模型 API 或外部服务可能需要单独开通。
- 用自己的素材试运行,再检查事实、版式与输出文件。
许可证:未明确声明,请查看原仓库的使用条件。
来源快照:2026-09-15。README ↗
可以这样开始
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-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
Using 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