karpathy-skills

Provide coding commandments, debugging workflows, and checklists for LLM-assisted software development.

1|1|Updated Apr 9, 2026
One-click install
npx skills add https://github.com/gaoqiongxie/skills-ai --skill karpathy-skills-gaoqiongxie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: karpathy-skills
Source: https://github.com/gaoqiongxie/skills-ai/tree/main/karpathy-skills
Command: npx skills add https://github.com/gaoqiongxie/skills-ai --skill karpathy-skills-gaoqiongxie

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the common issues developers face when using large language models for coding tasks, such as over-abstracted code, skipped tests, misread error messages, and unreliable AI-generated code, which lead to wasted development time and poor code quality.

Core Features & Use Cases

  • Ten Core Coding Commandments: Clear do's and don'ts to avoid common LLM coding pitfalls like over-engineering and ignoring error logs.
  • Structured Debugging Workflow: Step-by-step process to efficiently resolve coding errors without repeatedly querying AI assistants.
  • Customizable Question Templates: Pre-built templates for bug reports, code review requests, and refactoring tasks to get accurate responses from AI coding tools.
  • AI-Friendly Code Checklist: Quick reference to ensure AI-generated code meets basic quality standards before deployment.
  • Use Case: A developer using an AI assistant to build a new feature can use the ten commandments to avoid over-designing the system architecture, follow the debugging workflow to fix a runtime error in minutes instead of hours, and use the checklist to verify code quality before merging.

Quick Start

Ask the karpathy-skills skill to provide the LLM coding ten commandments and debugging workflow when you start an AI-assisted coding task to avoid common mistakes.

Frequently Asked Questions about karpathy-skills

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

FAQPage Schema
How do I avoid common coding pitfalls when using an AI coding assistant?▼

To avoid common coding pitfalls with an AI coding assistant, apply ten core coding commandments that prevent over-engineering and skipped tests. These structured best practices ensure AI-generated code remains reliable and efficient for software development tasks.

What is the best way to debug runtime errors caused by LLM programming?▼

The best way to debug runtime errors caused by LLM programming is to follow a structured debugging workflow. This step-by-step process helps you efficiently resolve coding errors without repeatedly querying your AI assistant for fixes.

Why does AI-assisted code generation skip tests and misread error logs?▼

AI-assisted code generation skips tests and misreads error logs due to improper use of large language models. Applying structured best practices and an AI-friendly code checklist ensures generated code meets basic quality standards before deployment.

How do I get accurate responses for code review and refactoring from an AI assistant?▼

To get accurate responses for code review and refactoring from an AI assistant, use customizable question templates. These pre-built templates for bug reports and refactoring tasks structure your prompts to eliminate over-abstracted code and wasted development time.

Can I use this LLM programming guide for individual developer workflows?▼

Yes, you can use this LLM programming guide for individual developer workflows. It applies to AI-assisted code generation, debugging, code review, and refactoring scenarios for individual developers and engineering teams alike.

What should I not do when using AI for software development tasks?▼

When using AI for software development tasks, you should not ignore error logs or over-design system architecture. Following clear do's and don'ts prevents over-engineering and ensures AI-generated code meets basic quality standards.