ml-debug

Name: ml-debug
Rating: 5 (2 reviews)
Author: giacomogaglione

Debug ML issues systematically -- tensor shapes, NaN propagation, gradient flow, device mismatches, dtype errors. Use when encountering training failures, unexpected outputs, or numerical issues.

2stars

Updated 5 months ago

View on GitHub ↗License: MIT

How to add

/plugin marketplace add giacomogaglione/claude-awesome-stack

The exact command may vary by repository. Check the README on GitHub.

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ML Debugging Skill

When debugging an ML issue, work through these checks in order. Stop at the first check that reveals the problem.

1. Shape Verification

Trace tensor shapes through the computation:

Print shapes at every function boundary: print(f"input: {x.shape}, output: {y.shape}")
Verify batch dimension is preserved through all operations
Check that reshape/view operations don't silently permute data
Verify attention mask shapes match query/key dimensions

ml-debug

How to add

Drop this on your repo README

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ML Debugging Skill

1. Shape Verification

2. Dtype and

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