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Refactor:Pytorch

Checked
snakeo·vlatest·ai-ml

Automatically refactors PyTorch code for improved readability, efficiency, and adherence to best practices.

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 snakeo/claude-debug-and-refactor-skills-plugin --skill refactor:pytorch -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

snakeo

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 snakeo/claude-debug-and-refactor-skills-plugin at e2e79df88e.

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

Commit
e2e79df88e
Manifest
plugins/debug-and-refactor/skills/refactor-pytorch/SKILL.md
Scanned
1 file, 782 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

This skill refactors PyTorch code to improve readability, efficiency, and maintainability. It can identify areas for optimization such as redundant calculations or inefficient data structures. The skill aims to produce cleaner, more performant PyTorch implementations while preserving the original functionality.

When to use it

  • You have a large, complex PyTorch model that is difficult to understand and debug.
  • Your PyTorch code is running slowly and you suspect inefficiencies in the implementation.
  • You want to modernize older PyTorch codebases to leverage newer features or best practices.
  • A colleague wrote some PyTorch code and you need help understanding it quickly.

Key capabilities

  • Code refactoring for improved readability
  • Optimization of redundant calculations
  • Identification of inefficient data structures
  • Preservation of original functionality during refactoring

Example prompts

  • "Refactor this PyTorch model to improve its efficiency." (followed by the code)
  • "Can you make this PyTorch function more readable?" (followed by the code)
  • "Identify any potential performance bottlenecks in this PyTorch script." (followed by the code)

Tips & gotchas

  • The skill is most effective with well-structured and documented PyTorch code. Providing clear context helps improve refactoring quality.