Explorar skills

Walk-forward historical testing of betting strategies to determine whether a model's edge is real. Use when user asks about backtesting, historical simulation, ROI over a season, whether a strategy works, bootstrap confidence intervals, edge compression, or model audit over time. Refuses in-sample testing -- walk-forward only. Do not use for live bet tracking -- see bet-tracker. Do not use for fin

Full slate analysis for tonight's games with edge rankings, odds across books, and recommended stakes. Use when user asks about tonight's slate, best bets tonight, daily card, full slate, tonight's games, which games are worth betting, or wants all games ranked by edge. Do not use for a single game breakdown -- see game-preview. Do not use for historical backtesting -- see backtesting. Do not use

Routes hockey analytics requests to the right 2-3 skills. Load this FIRST when a hockey data or analytics request comes in and you are unsure which skills to activate. Prevents loading all 28 skills when only 2-3 are needed. Also handles first-use persona detection.

Compares model probabilities against market odds to find positive expected value bets. Use when user asks about EV calculation, edge magnitude, Kelly criterion, bankroll sizing, CLV tracking, closing line value, fractional Kelly, simultaneous bets, or which bets are worth placing. Do not use for odds exploration without a model -- see odds-explorer. Do not use for validating model accuracy -- see

Builds multi-variant Elo rating systems for sports prediction. Use when user asks about Elo ratings, rating systems, team strength, K-factor, home field advantage, season carryover, margin-of-victory adjustments, or mentions 'Fading Elo', 'Form Elo', 'Component Elo'. Do not use for general feature construction -- see feature-engineering instead. Do not use for team stats without ratings -- see tea

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

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

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

Goalie-specific analysis for NHL: leaderboard rankings, workload tracking, starter identification, tandem splits, and matchup history. Includes xG-adjusted metrics -- GSAA, xSV%, HDSA% -- that predict future performance. Use when user asks about goalie stats, save percentage, GAA, quality starts, goalie fatigue, back-to-back starts, or which goalie is starting tonight. Do not use for skater stats

Teaches hockey analytics metrics -- Corsi, Fenwick, PDO, xG, zone entries, high-danger chances, RAPM, WAR -- with real data context. Use when user asks what Corsi means, how to interpret PDO, what xG tells you, why Fenwick matters, or asks to explain advanced stats for a team or player. Do not use for betting concepts or odds interpretation -- see odds-analysis. Do not use for building prediction

Trains and validates prediction models for sports game outcomes. Use when user asks about building a prediction model, training a classifier, choosing between logistic regression or XGBoost, model selection, hyperparameter tuning, feature importance, or ensemble methods. Always uses walk-forward methodology -- refuses k-fold. Do not use for feature construction -- see feature-engineering. Do not u

Teaches how to use Claude and MCP tools effectively for hockey analytics -- exploratory analysis, hypothesis testing, model iteration, and report generation. Use when user asks how to analyze hockey data with AI, how to structure an analysis session, how to test a hypothesis about team or player performance, how to improve a model systematically, or how to generate a performance report. Do not use