A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
A single CLAUDE.md guideline file distilling Karpathy's LLM coding-pitfalls observations into four principles.
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Documented (README)
- !No license declared
{
"mcpServers": {
"andrej-karpathy-skills": {
"command": "node",
"args": ["/path/to/andrej-karpathy-skills/dist/index.js"]
}
}
}~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows).<placeholder> values with your API keys or paths.Tools overview
# Karpathy-Inspired Claude Code Guidelines
> Check out my new project [Multica](https://github.com/multica-ai/multica) — an open-source platform for running and managing coding agents with reusable skills.
>
> Follow me on X: [https://x.com/jiayuan_jy](https://x.com/jiayuan_jy)
A single `CLAUDE.md` file to improve Claude Code behavior, derived from [Andrej Karpathy's observations](https://x.com/karpathy/status/2015883857489522876) on LLM coding pitfalls.
English | [简体中文](./README.zh.md)
## 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:
```bash
curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md
```
Existing project (append):
```bash
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`](.cursor/rules/karpathy-guidelines.mdc)) so the same guidelines apply when you open the project in Cursor. See **[CURSOR.md](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:
```markdown
## 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
What people ask about andrej-karpathy-skills
What is forrestchang/andrej-karpathy-skills?
+
forrestchang/andrej-karpathy-skills is tools for the Claude AI ecosystem. A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. It has 92.5k GitHub stars and was last updated 7d ago.
How do I install andrej-karpathy-skills?
+
You can install andrej-karpathy-skills by cloning the repository (https://github.com/forrestchang/andrej-karpathy-skills) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is forrestchang/andrej-karpathy-skills safe to use?
+
Our security agent has analyzed forrestchang/andrej-karpathy-skills and assigned a Trust Score of 71/100 (tier: OK). See the full breakdown of passed checks and flags on this page.
Who maintains forrestchang/andrej-karpathy-skills?
+
forrestchang/andrej-karpathy-skills is maintained by forrestchang. The last recorded GitHub activity is from 7d ago, with 73 open issues.
Are there alternatives to andrej-karpathy-skills?
+
Yes. On ClaudeWave you can browse similar tools at /categories/tools, sorted by popularity or recent activity.
Deploy andrej-karpathy-skills to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/forrestchang-andrej-karpathy-skills)<a href="https://claudewave.com/repo/forrestchang-andrej-karpathy-skills"><img src="https://claudewave.com/api/badge/forrestchang-andrej-karpathy-skills" alt="Featured on ClaudeWave — forrestchang/andrej-karpathy-skills" width="320" height="64" /></a>More Tools
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
An AI SKILL that provide design intelligence for building professional UI/UX multiple platforms
A light-weight and powerful meta-prompting, context engineering and spec-driven development system for Claude Code by TÂCHES.
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
一款 AI 驱动的低代码平台,提供"零代码"与"代码生成"双模式——零代码模式一句话搭建系统,代码生成模式自动输出前后端代码与建表 SQL,生成即可运行。平台内置 AI 聊天助手、AI大模型、知识库、AI流程编排、MCP 与插件体系,兼容主流大模型,支持一句话生成流程图、设计表单、聊天式业务操作,解决 Java 项目 80% 重复工作,高效且不失灵活。
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]