debug:pytorch

SKILL.md

This guide provides systematic approaches to debugging PyTorch models, from common tensor errors to complex training issues.

Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows. Source: snakeo/claude-debug-and-refactor-skills-plugin.

Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/snakeo/claude-debug-and-refactor-skills-plugin --skill debug:pytorch Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor

Security certified for safe and reliable code One-click installation with simplified configuration Compatible with Claude Code, Cursor, and more

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