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 for specific one-time data queries -- see game-lookup or nl-to-query. Do not use for the mechanics of model building -- see feature-engineering or model-building directly.
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.
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 outperform their season SV%'. Includes a self-correction loop -- retries with adjusted parameters if the first query returns unexpected results. Do not use for specific known queries where the right tool is obvious -- just call game-lookup, team-analysis, or goalie-analysis directly. Do not use for model building -- see feature-engineering.
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 finding edges on today's slate -- see edge-detection. Do not use for model training -- see model-building.
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 not use for finding today's bets -- see daily-card or edge-detection. Do not use for historical backtesting before live betting -- see backtesting. Do not use for model training or retraining -- see model-building.
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 for tracking bets placed -- see bet-tracker.
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 game-lookup or puckapi-tool directly. Do not use for building the model itself -- see model-building. Do not use for backtesting historical performance -- see backtesting.
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 backtesting. Do not use for converting odds formats or computing implied probability -- see odds-analysis.