Trace PyTorch tensors to diagnose runtime errors and propose fixes.
npx skills add https://github.com/michaelrizvi/claude-config --skill pytorch-debug
Please help me install this Agent Skill. Skill: pytorch-debug Source: https://github.com/michaelrizvi/claude-config/tree/main/skills/pytorch-debug Command: npx skills add https://github.com/michaelrizvi/claude-config --skill pytorch-debug
Debug PyTorch runtime errors by tracing tensors from source to error points and proposing actionable fixes.
Trace the full data flow of a failing PyTorch tensor and propose an initial diagnosis.
High-intent search queries and answers about installing and using this skill.
Debug PyTorch runtime errors by tracing tensors from the dataloader to model outputs to locate where dtype, device, shape, gradient, or memory issues originate, then apply structured diagnostic steps to resolve them.
Trace PyTorch tensor data flow by validating tensor transformations from source to error points, pinpointing the exact layer where shape mismatches occur and providing lightweight code snippets to guide fixes.
Out of memory errors in PyTorch training pipelines occur during tensor transformations and can be diagnosed by enforcing a trace-based debugging workflow that validates data flow and provides actionable diagnostics to guide memory fixes.
Yes, you can diagnose gradient problems in model development workflows by applying a trace-based debugging workflow that validates tensor data flow and detects gradient issues alongside dtype, device, and shape problems.
Yes, this approach provides structured diagnostic steps and lightweight code snippets to guide fixes for dtype mismatches, device placement, shape issues, gradient problems, and OOM errors in typical ML workflows.
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