Recently, the number one project on GitHub Trending isn't a massive model framework with thousands of lines of code, but a 65-line CLAUDE.md file.

This file comes from Andrej Karpathy's observations of common pitfalls in LLM-based coding. It has gained 220,000 stars since being released, and most developers hadn't even read it fully when it topped the trending chart.

It uses no fancy tricks, only 4 core rules, which real tests show boost AI coding accuracy from 65% all the way to 94%.

All four rules point to the same goal: counteract the "code first, think later" instinct, and turn slow, deliberate thinking into a hard constraint:

1. Think through clearly before writing code

State all assumptions explicitly; ask questions when uncertain, never guess; if a requirement can be interpreted in multiple ways, list all options instead of picking one on your own; propose a simpler solution directly if you find one, and insist on it when necessary.

2. Prioritize the simplest solution

Only write the minimal code needed to solve the problem; do not add any unrequired features, abstractions, flexibility or configuration options; rewrite directly if you can turn 200 lines into 50.

3. Make surgical, precise changes

Only touch code directly related to the requirement; every change you make must map to an explicit requirement; only clean up dead code introduced by your own changes; for existing unrelated dead code, only point it out instead of deleting it.

4. Execute with clear goal orientation

Before writing your first line of code, convert vague instructions into verifiable success criteria. For example, change "add validation" to "first write test cases for invalid inputs, then make all tests pass"; for multi-step tasks, list the verification criteria for each step explicitly.

The accuracy improvement from 65% to 94% comes from Karpathy's own tests and feedback from a large number of developers. The number may fluctuate slightly for different task types, but the core logic holds: force AI out of the "prioritize pattern matching" habit, and do logical checks first.

Many developers have shared real feedback after using these rules:

- A senior engineer noted that these are working habits excellent senior engineers have followed for years, but this is the first time they have been organized into hard rules for AI. After testing the requirement to "state assumptions first", back-and-forth communication with AI dropped by 30% directly.

- After testing, one user said that the two rules that deliver the biggest improvement are "state assumptions" and "ask when uncertain", which directly solves AI's death spiral of frantically calling tools and hiding errors when it encounters problems.

- There's also a down-to-earth joke: for a 220k-star project, the most core rule is actually "actually read these 65 lines" — most people just bookmark it and never open it again.

Some developers have also created derived optimized versions based on these rules: entropyvortex launched META v2.0, which expands the core rules to 11 and adds a zero-pause native execution layer, supporting multiple mainstream coding tools including Claude Code, Cursor, and Grok. The project comes with a reproducible A/B testing suite, and the latest test results show that AI using this rule set won 3 tasks and drew 2 tasks against the general "senior engineer" baseline prompt across 5 test tasks.

### Specific Usage

Original CLAUDE.md project address: [https://github.com/multica-ai/andrej-karpathy-skills](https://github.com/multica-ai/andrej-karpathy-skills)

- Can be installed directly as a Claude Code plugin, and takes effect across the entire project

- You can also place the file in your project root to adapt to custom rules in tools like Cursor

Derived version META v2.0 address: [https://github.com/entropyvortex/meta-llm-charter](https://github.com/entropyvortex/meta-llm-charter)

- Supports zero-pause execution mode, adapts to Grok custom skills

- Comes with a public testing suite for you to verify effectiveness on your own

What's interesting is that at a time when everyone is stacking complex prompt frameworks and adding more tokens for AI, the biggest boost to AI coding efficiency actually comes from 65 lines of clearly written engineering common sense — something humans have known for ages, but rarely manage to follow.