Skills publicadas
Mostrando 12 de 28game-preview
Complete pre-game analysis for a single NHL (or other sport) matchup: team records, goalie matchup, key stats, odds snapshot, model edge, and head-to-head history in one shareable report. Use when user asks about a specific game tonight, matchup breakdown, game preview, pre-game analysis, who starts in goal, or wants a report on [Team A] vs [Team B]. Do not use for full slate analysis -- see daily
odds-analysis
Converts betting odds between formats and removes bookmaker margin to find true implied probabilities. Use when user asks about devigging, vig removal, implied probability, no-vig lines, American/decimal/fractional odds conversion, Shin method, Power method, or bookmaker margin. Prerequisite for edge detection. Do not use for viewing current odds -- see odds-explorer instead. Do not use for model
game-lookup
Finds games by date, team, matchup, or season -- past results, tonight's slate, upcoming schedule, head-to-head history. Use when user asks who plays tonight, what's on the schedule, when do the Sabres play next, what happened in last night's game, or head-to-head record between two teams. Do not use for team performance evaluation -- see team-analysis. Do not use for odds or betting lines -- see
xg-model-building
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
visualization
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
war-gar-decomposition
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
player-scouting
Searches players and surfaces season stats, per-game rates, multi-season comparisons, roster snapshots, and prospect NHLe translations. Use when user asks how a player is performing, wants to compare two skaters, needs a team's top scorers, is evaluating a prospect from another league, or needs stats for fantasy or DFS. Do not use for goalie stats -- see goalie-analysis. Do not use for team-level
prop-modeling
Builds player prop prediction models for NHL player stats -- points, shots on goal, saves, blocked shots, and power play points. Use when user asks about prop modeling, player prop predictions, anytime goal scorer odds, shots on goal props, save props, DFS player projections, or player stat projections. Do not use for team-level game prediction -- see model-building. Do not use for player comparis
feature-engineering
Transforms raw game, player, and goalie data into model-ready features with built-in leakage detection. Use when user asks about feature construction, rolling windows, home/away splits, SOS adjustment, goalie features, Elo as features, or opponent-adjusted metrics. Strictly enforces .shift(1) before any rolling calculation. Do not use for raw data exploration -- see game-lookup or team-analysis. D
probability-calibration
Verifies and corrects model probability outputs so predicted win percentages match actual win rates. Use when user asks about calibration, reliability diagrams, Brier score, Platt scaling, isotonic regression, probability quality, or whether model probabilities are accurate. Also use when user has a trained model and wants to know if outputs can be trusted for betting. Do not use for odds math or
puckapi-tool
Default data source for PuckAPI Skills. Routes all MCP tool calls for NHL games, schedules, team standings, player stats, goalie performance, and betting odds. Use when any other skill needs data. Do not invoke directly -- other skills call this. Not for data analysis, modeling, or bet recommendations -- see hockey-analytics, model-building, or edge-detection instead.
data-pipeline
Builds automated sports analytics pipelines -- daily data pulls, scheduled prediction generation, model versioning, prediction tracking, and drift alerting. Use when user asks how to automate their model, how to run predictions every morning, how to set up a cron job for sports data, how to track model performance over time, or how to version their model. Do not use for one-time data pulls -- use