walrusquant · Agent Skills

14 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.

walrusquant

Community

baseline-models

Design and evaluate simple sports prediction baselines before accepting more complex models. Use for constant-rate, home-advantage, logistic, Elo-style, or market-reference comparisons.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

NOASSERTION5 File

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walrusquant

Community

calibration-check

Evaluate whether sports-model probabilities match observed frequencies. Use for Brier score, log loss, reliability bins, ECE, segment checks, and recalibration decisions.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

NOASSERTION6 File

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walrusquant

Community

eda-sports

Exploratory data analysis for user-provided sports data: grain, key integrity, coverage, missingness, entity balance, base rates, outliers, structural breaks, and leakage red flags. Use before feature engineering or model fitting.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT6 File

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walrusquant

Community

environment-setup

Create and verify a portable Python environment for sports analysis. Use for machine setup, onboarding, dependency diagnosis, or reproducibility checks.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT4 File

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walrusquant

Community

feature-rules

Define, review, and document point-in-time legal sports-model features. Use when creating rolling form, rest, matchup, rating, roster, injury, or contextual predictors.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

NOASSERTION7 File

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walrusquant

Community

model-card

Write a durable sports model card covering identity, intended use, target, decision time, data, features, baselines, validation, results, limits, maintenance, and kill conditions. Use when freezing or sharing a model.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT4 File

Vedi dettagli →

walrusquant

Community

nflreadpy

Load NFL schedules, play-by-play, rosters, and player or team statistics directly from nflverse with nflreadpy. Use for NFL acquisition, schema review, bounded snapshots, and preparing user-owned analysis artifacts.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT6 File

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walrusquant

Community

predictive-modeling

Build and evaluate predictive sports models from user-provided modeling data. Use for binary outcome models, feature/model selection, chronological backtests, probability scoring, and comparison to baselines.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT7 File

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walrusquant

Community

pybaseball

Load MLB Statcast, batting, pitching, standings, and player lookup data directly with pybaseball. Use for bounded pitch-level pulls, season tables, schema checks, and user-owned baseball data artifacts.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT4 File

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walrusquant

Community

results-reporting

Report sports analysis and modeling results with the question, data, methods, validation, baselines, metrics, interpretation, limits, figures, and reproduction pointers. Use for research notes, reports, and final answers.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT4 File

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walrusquant

Community

simulation-sports

Simulate game and season outcomes from user-supplied probabilities or ratings, summarize uncertainty, and test sensitivity to assumptions. Use for standings, win totals, matchup distributions, and scenario analysis.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT4 File

Vedi dettagli →

walrusquant

Community

sports-modeling-doctrine

Define a sports analysis or prediction question, grain, decision time, baselines, primary metrics, validation, and acceptance criteria before choosing algorithms. Use at the start of any sports modeling project.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT5 File

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walrusquant

Community

sports-visualization

Create honest sports-analysis figures from user-owned data, including distributions, rates, rating trajectories, calibration plots, and walk-forward metric comparisons. Use for exploration and communication.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

MIT6 File

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walrusquant

Community

time-series-sports

Engineer and compare time-safe sports form features. Use for rolling windows, EWMA, rest, schedule gaps, early-season handling, and chronological evaluation.

installazioni

1

GitHub Stars

49

Aggiornato

9 set

NOASSERTION6 File

Vedi dettagli →