karpathy-guidelines

Provides behavioral best practices for LLM coding and refactoring workflows.

2|1|Updated Feb 27, 2026

Search Tags:#best practices #refactoring #prompt engineering #LLM #guidelines

Ready to run

npx skills add https://github.com/dahatake/HypervelocityEngineering --skill karpathy-guidelines-dahatake

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: karpathy-guidelines
Source: https://github.com/dahatake/HypervelocityEngineering/tree/main/.github/skills/karpathy-guidelines
Command: npx skills add https://github.com/dahatake/HypervelocityEngineering --skill karpathy-guidelines-dahatake

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides clear behavioral guidelines to help developers minimize common mistakes in large language model coding, reducing errors and overcomplication.

Core Features & Use Cases

Guidelines for Better Coding: Offers best practices to avoid overengineering, surface assumptions, and improve review processes.
Use Case: When refactoring or reviewing LLM prompts or code, consult these guidelines to ensure clarity, safety, and simplicity.
Practical Advice: Helps teams implement surgical changes and goal-driven execution for robust AI development.

Quick Start

Ask the AI to review your code or prompts using the karpathy-guidelines to ensure best practices and avoid common pitfalls.

Frequently Asked Questions about karpathy-guidelines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What are the best practices for LLM coding and refactoring?▼

Best practices for LLM coding emphasize simplicity, safety, and goal-oriented execution. They involve avoiding overengineering, surfacing hidden assumptions, and making surgical changes to minimize errors during AI prompt engineering and code reviews.

How do I avoid overengineering when using LLMs for code generation?▼

To avoid overengineering in LLM code generation, apply behavioral guidelines that enforce goal-driven execution and surgical changes. This approach keeps modifications focused and prevents the AI from introducing unnecessary complexity into your codebase.

Can I use these guidelines for reviewing AI prompts and code?▼

Yes, you can use these guidelines for reviewing AI prompts and code. They provide behavioral best practices to ensure clarity and safety, helping developers identify common mistakes and validate assumptions during refactoring workflows.

Why does my LLM make overly complex code changes?▼

Your LLM makes overly complex code changes when it lacks strict behavioral guidelines for simplicity. Without rules enforcing surgical modifications and goal-oriented execution, the model tends to overengineer solutions and obscure underlying assumptions.

Do I need specific frameworks to apply these LLM coding guidelines?▼

No specific frameworks are required to apply these LLM coding guidelines. They are behavioral best practices focused on prompt engineering and code review processes, making them applicable across any development environment or AI model.

What is the best way to ensure safety in AI prompt engineering?▼

The best way to ensure safety in AI prompt engineering is to follow guidelines that surface assumptions and enforce surgical changes. This goal-oriented approach reduces errors and prevents overcomplication during developer tasks and code reviews.