puckapi-skills

Plugin puckapi-skills from marketplace puckapi in PuckAPI/claude-sports-analytics. Commands read from the repo's manifest — today.

Skills (28)

• ai-hockey-workflow

  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 per

• backtesting

  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 t

• bet-tracker

  Track predictions vs actuals in production: log bets, compute CLV, monitor ROI, detect edge decay, and run significance tests. Use when user asks about tracking bets, bet logging, closing line value, CLV, ROI tracking, win rate, drawdown monitoring, or whether their model has an edge right now. Do n

• daily-card

  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

• 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 mo

• dispatch

  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.

• edge-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 odd

• elo-engineering

  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 c

• 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

• 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 us

• game-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

• goalie-analysis

  Goalie-specific analysis for NHL: leaderboard rankings, workload tracking, starter identification, tandem splits, and matchup history. Returns xG-adjusted metrics -- GSAX, high-danger save%, rolling form -- straight from the API, which predict future performance better than raw save percentage. Use

• hockey-analytics

  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 f

• model-building

  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 met

• nl-to-query

  Translates natural language hockey queries into structured data filters and executes them via puckapi-tool. Use when user asks a research question in plain English: 'show me games where the home team was outshot but won', 'find all back-to-back losses', 'how often do goalies with 2 days rest outperf

• 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 edg

• odds-explorer

  Multi-book odds comparison, best price identification, vig calculation, line shopping, and line movement analysis for NHL. Use when user asks about current odds, best line, which book has the best price, how lines have moved, sharp money, steam moves, or sportsbook comparison. Do not use for deviggi

• 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 stat

• playoff-simulation

  Monte Carlo playoff and season simulator for NHL. Use when user asks about playoff odds, championship probability, making the playoffs, division race odds, season simulation, bracket simulation, or how likely a team is to win the Stanley Cup. Do not use for single game prediction -- see model-buildi

• 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

• 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 projecti

• 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 recommendatio

• team-analysis

  Evaluates teams via standings, stats, division/conference rankings, season trends, and strength of schedule. Use when user asks how a team is doing, where they stand in the division, what their record is, how they compare to other teams, or wants to understand conference/playoff positioning. Do not

• totals-modeling

  Builds over/under prediction models for NHL game totals using pace, special teams, goalie matchup, and contextual features. Use when user asks about totals modeling, over/under prediction, predicting total goals, goal scoring rates, or NHL scoring trends. Do not use for moneyline or spread predictio

• 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 t

• walk-forward-validation

  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-

• 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 evaluatio

• 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 value

MCP servers (1)

• puckapi

  connects to: https://mcp.puckapi.com/mcp

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