The andrej-karpathy-skills repository is a focused project that provides a single CLAUDE.md file designed to improve how Claude Code behaves when writing and modifying code. The guidelines are derived from observations made by Andrej Karpathy about common pitfalls that large language models encounter when generating code, specifically addressing issues like making unfounded assumptions, overcomplicating solutions, and making unnecessary changes to existing code.
The repository identifies four core problems that LLMs face in coding tasks. Models tend to make wrong assumptions without verification or seeking clarification, they overcomplicate code and create bloated abstractions when simpler solutions would suffice, they sometimes modify or remove comments and code they don't fully understand as side effects of other changes, and they struggle with managing confusion and presenting tradeoffs to users. These observations form the foundation for the solution provided.
The core solution consists of four principles designed to directly address these identified problems. The first principle, Think Before Coding, combats wrong assumptions and hidden confusion by requiring explicit reasoning, stating assumptions clearly, presenting multiple interpretations when ambiguity exists, and asking for clarification rather than guessing. The second principle, Simplicity First, fights against overengineering by enforcing minimum code that solves the problem without speculative features, unnecessary abstractions, or unasked-for flexibility. The third principle, Surgical Changes, ensures that edits only touch what is necessary, matching existing code style and removing only code that the current changes made unused rather than pre-existing dead code. The fourth principle, Goal-Driven Execution, transforms imperative tasks into verifiable goals with success criteria, allowing the model to loop independently until objectives are met.
The repository provides multiple installation options for users. The recommended approach uses Claude Code plugins through a marketplace, making the guidelines available across all projects. Alternatively, users can add the CLAUDE.md file directly to new or existing projects. The repository also includes support for Cursor users through a committed project rule file, with detailed setup instructions provided in a separate CURSOR.md document.
According to GitGenius activity tracking, the repository has shown modest but consistent growth, with stargazers increasing by five between the tracking period ending July 4, 2026 and the previous check, growing from 187,552 to 187,557 stars. The repository is classified across multiple domains including Machine Learning, Deep Learning, Neural Networks, Large Language Models, Computer Vision, AI Engineering, Technical Skills, Knowledge Curation, AI Concepts, and Learning Resource, reflecting its position as a knowledge curation resource for AI engineering practices.
The repository emphasizes a key insight from Karpathy that LLMs excel at looping until they meet specific goals, suggesting that providing success criteria and verification loops is more effective than imperative instructions. The guidelines are designed to reduce costly mistakes on non-trivial work while acknowledging that not every change requires full rigor, particularly for simple tasks like typo fixes. The project is intentionally minimal in scope, consisting of a single configuration file rather than code libraries or complex tooling, making it easy to integrate into existing projects and customize with project-specific instructions.
