Explore skills

Generate shareable visual outputs for sports analytics: calibration curves, equity curves, radar charts, matchup cards, probability histograms, and player cards. Use when user asks to visualize, chart, plot, graph, show, display, generate a visual, make a shareable image, or wants to post analysis to social media. Do not use for raw data exploration -- see game-lookup or nl-to-query. Do not use fo

The correct evaluation methodology for time-series sports prediction models. Use when user asks about model validation, cross-validation, train/test split, accuracy evaluation, overfitting detection, or statistical significance of sports model results. Refuses to run k-fold cross-validation on time-series data -- always redirects to walk-forward. Do not use for backtesting betting strategies with

Builds WAR (Wins Above Replacement) and GAR (Goals Above Replacement) from scratch using RAPM ridge regression on shift-level data. Use when user asks about WAR, GAR, RAPM, player value metrics, wins above replacement, contract surplus value, JFresh-style player cards, or all-in-one player evaluation. Do not use for simple player stats lookup -- see player-scouting. Do not use for goalie evaluatio

Builds expected goals (xG) models from NHL play-by-play shot event data using XGBoost or LightGBM. Use when user asks about expected goals, xG model, shot quality, building an xG model, xGF%, xGA, rebound detection, rush shot detection, or shot probability. Do not use for applying pre-built xG values to team or game analysis -- see team-analysis or hockey-analytics. Do not use for general game pre