Skill Claude CodeCodex
Chooses a leakage-safe way to split data — temporal, grouped, stratified, or nested cross-validation — matched to how the rows are actually structured, and decided before any model is trained. Use when someone asks how to split into train and test, set up cross-validation, or pick between KFold, GroupKFold…
Skill Claude CodeCodex
Slices residuals and errors to find where a model fails, instead of reporting one aggregate score. Use when someone asks where a model gets things wrong, or wants the worst-performing segments named. Use when an overall metric looks acceptable but the result is not yet trusted. Use when debugging why specific cases…
Skill Claude CodeCodex
Handles targets where the interesting class is rare — resampling, class weights, threshold tuning, and the metric consequences. Use when only a tiny fraction of rows are positive, or when a model never predicts the rare class at all. Use when someone mentions SMOTE, oversampling, undersampling, or classweight. Use…
Skill Claude CodeCodex
Picks the evaluation metric that matches the decision and the shape of the target, and says plainly when a familiar one is wrong. Use when someone asks whether accuracy is the right thing to report, or whether to optimise for precision or recall. Use when choosing or defending a metric for classification, regression…
Skill Claude CodeCodex
Blends, stacks, or averages several trained models using leakage-safe out-of-fold predictions, and reports the lift over the best single component relative to fold spread. Use when someone asks to combine models, average predictions, or stack them. Use when a single model's score has plateaued and the next lever is a…
Skill Claude CodeCodex
Makes a notebook safe to rerun top to bottom and safe to read out of execution order. Use when a notebook only works if cells are run in a particular order, or its current state can no longer be trusted. Use when someone asks to clean up or tidy a notebook before sharing, reviewing, or handing it over.
Skill Claude CodeCodex
Finds features that encode the answer — computed from the label, from the future, or contaminated across the train/test boundary. Use when a score looks too good to be true on the first try, such as AUC near 0.99 or R-squared near 1. Use when one feature dominates the importance ranking. Use when a feature was built…
Skill Claude CodeCodex
Reports every score with its spread across folds or seeds, and says whether a gap between two numbers is bigger than that spread. Use when someone asks whether an improvement is real or just noise, or how confident to be that one model beats another. Use when two results are being called different or the same without…