Experiment Debugger

Fix engineering failures that prevent valid experiment results from being produced. This skill separates bugs from science: if the code is broken, wrong, or non-reproducible, fix the code first before interpreting results.

Use this skill when:

• training crashes or produces NaN loss, NaN gradients, or inf values
• GPU OOM errors block training or reduce batch size unexpectedly
• training is significantly slower than expected
• data loading is a bottleneck or produces corrupted batches
• metric values are impossible, unstable, or inconsistent across runs
• a run cannot be reproduced despite fixing the seed
• a config or checkpoint was changed silently between runs
• tensorboard, wandb, or logging output is missing or misaligned

Do not use this skill for interpreting surprising but valid results — use result-diagnosis. Do not use this skill for choosing baselines or experiment designs — use baseline-selection-audit and experiment-design-planner.

Pair this skill with: