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Debug:Pytorch

Checked
snakeo·vlatest·ai-ml

Identifies and suggests fixes for common errors in PyTorch code, streamlining development and improving model performance.

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 debug: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

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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.

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    Reads no keys, tokens, keychains or credential files.

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  • Pinned to a commit: passed

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

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Commit
e2e79df88e
Manifest
plugins/debug-and-refactor/skills/debug-pytorch/SKILL.md
Scanned
1 file, 660 lines
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What each check looks for →

About this skill

What it does

This skill assists in debugging PyTorch code. It can analyze error messages, identify potential causes of issues within your PyTorch models and training loops, and suggest fixes or troubleshooting steps. The tool aims to accelerate the debugging process for machine learning engineers working with PyTorch.

When to use it

  • You're encountering cryptic error messages during PyTorch model training.
  • Your model is producing unexpected results despite seemingly correct code.
  • You need help understanding a complex stack trace involving PyTorch operations.
  • You want suggestions for improving the efficiency or correctness of your PyTorch data loading pipeline.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Troubleshooting suggestions
  • Data loading pipeline optimization

Example prompts

  • "I'm getting a CUDA out-of-memory error in my PyTorch training loop. Can you help me diagnose the problem?"
  • "My model isn’t converging. Here's the code and the loss curve; what might be going wrong?"
  • "I have this PyTorch data loader, but it seems to be very slow. Any ideas on how I can make it faster?"

Tips & gotchas

  • Provide as much context as possible, including relevant code snippets and error messages, for more accurate debugging assistance.
  • The skill's effectiveness depends on the clarity and completeness of the information provided in the prompt.