Use BEFORE training any model, to build correct train/val/test splits and hunt data leakage - the #1 cause of fake-high accuracy. Covers group/patient/subject splits, temporal splits, official-benchmark splits, label correctness, class balance, and preprocessing parity. Triggers on 'split the data', 'train/test split', 'cross validation', 'why is accuracy suspiciously high', 'data leakage', 'per-patient'.
How to install data-rigor-and-leakage?
mxslr/mlcraft/data-rigor-and-leakage
$npx -y skills add mxslr/mlcraft --skill data-rigor-and-leakageInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/mxslr/mlcraft" --skill "mxslr/mlcraft/data-rigor-and-leakage"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/mxslr/mlcraft" that are relevant to the current task. Run `npx skills add "https://github.com/mxslr/mlcraft"` and select the relevant skills, then follow their instructions.
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