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Karpathy Guidelines

Checked
forrestchang·vlatest·ai-ml

Applies Andrej Karpathy's principles for effective coding & project management to improve output quality.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

forrestchang

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned forrestchang/andrej-karpathy-skills at 2c60614193.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
2c60614193
Manifest
skills/karpathy-guidelines/SKILL.md
Scanned
1 file, 66 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The karpathy-guidelines skill provides practical, hands-on advice and best practices for training neural networks, particularly in the context of deep learning and AI development. It draws from Andrej Karpathy’s expertise, offering insights into model architecture, optimization techniques, and debugging strategies.

When to use it

  • You're designing a new neural network and need guidance on layer configurations or hyperparameter tuning.
  • You're encountering training instability or poor convergence in your deep learning models.
  • You want to improve the efficiency of your AI agent's learning process through optimized training routines.

Key capabilities

  • Practical advice for debugging neural networks
  • Insights into model architecture design
  • Optimization techniques for training efficiency

Example prompts

  • "How can I stabilize my neural network’s training?"
  • "What are some best practices for designing a deep learning model?"
  • "Can you explain how to debug exploding gradients in my AI agent's model?"

Tips & gotchas

  • This skill is most effective when used alongside specific technical problems or model architectures.
  • While it provides general guidance, results may vary depending on the exact use case and data being used.