andrej-karpathy
Guides code writing to avoid common mistakes. It helps simplify code and define success criteria.
What it does
This guide helps AI write better code by avoiding common mistakes. It focuses on making small, clear changes, thinking before acting, and ensuring the code works exactly as intended.
When to use it
- ✓When asking the AI to write new code.
- ✓When asking the AI to review or improve existing code.
- ✓When you want the AI to avoid overcomplicating a simple task.
Example of what to ask your AI
“AI, write a simple function to add two numbers, but think about how to do it in the clearest and most direct way possible, without adding extra things.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI follows steps like thinking before writing, focusing on the minimum needed to solve the problem, making only the requested changes, and defining how to know if the result is correct.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill andrej-karpathyOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/andrej-karpathy/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Karpathy Guidelines
Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.
When to Use This Skill
- Use when writing, reviewing, or refactoring code with an LLM.
- Use when a change needs to stay surgical and avoid speculative abstractions.
- Use when assumptions, tradeoffs, and verification criteria should be made explicit.
- Use when code has become overcomplicated and needs to be simplified.
1. Think Before Coding
Don't assume. Don't hide confusion. Surface tradeoffs.
Before implementing:
- State your assumptions explicitly. If uncertain, ask.
- If multiple interpretations exist, present them - don't pick silently.
- If a simpler approach exists, say so. Push back when warranted.
- If something is unclear, stop. Name what's confusing. Ask.
2. Simplicity First
Minimum code that solves the problem. Nothing speculative.
- 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 you write 200 lines and it could be 50, rewrite it.
Ask yourself: "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 tasks into verifiable goals:
- "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 you loop independently. Weak criteria such as "make it work" require constant clarification.
Examples
User request: "Add validation to this form."
Better response:
1. Assumption: validation should run before submit and show inline errors.
2. Plan: add a small validator, add tests for invalid inputs, then verify existing submit behavior.
3. Scope: only touch the form component and its test file.
User request: "Refactor this service."
Better response:
1. Ask what behavior must remain unchanged.
2. Identify a concrete smell, such as duplicated parsing logic.
3. Make the smallest refactor and run the existing service tests.
Limitations
- These guidelines are behavioral guardrails, not a replacement for project-specific architecture or style rules.
- For emergency fixes, prioritize the smallest verified correction over extensive planning.
- For exploratory prototypes, some caution can be relaxed, but assumptions and verification should still be explicit.
FAQ
Why should the AI think before coding?
To avoid guessing, hiding confusion, or creating solutions that are more complicated than necessary. It's better to ask and clarify.
What does 'surgical changes' mean?
It means touching only what is strictly necessary to fulfill the request, without altering other parts of the code that were not asked for.
How does the AI know if the code is good?
The AI defines clear criteria to know if the goal has been met. For example, instead of 'fix the bug,' it might define 'create a test that shows the bug and then make the test pass.'
Details
Original description by the author
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2126
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
benchmark-optimization-loop
Has your agent improve something's speed in a measured way. It tests multiple versions and chooses the fastest one that still works correctly.
affaan-m/ECC
change-impact
Helps your agent analyze the impact of code changes before making them. It compares the planned vs. actual results afterward, covering paths, data, tests, and documentation. Useful for risky or system-wide changes.
affaan-m/ECC
click-path-audit
Lets your agent trace all user buttons and interactions to find bugs. It finds issues where functions seem to work alone but fail together. Useful when bugs persist even after debugging.
affaan-m/ECC