DanielTomaro13

15 mods across 2 repositories, 17 stars between them.

sportsdata

01

DanielTomaro13/sportsdata-mcp

MCP server Claude CodeCodexCursor +2

MCP server for sports-data APIs (bookmakers, league data, aggregators). Capability-tag system enables cross-provider composition. Runs locally from the sportsdata-mcp Python package.

11 4d ago A tokens not measured original MIT

sportsdata-mcp

02

DanielTomaro13/sportsdata-mcp

MCP server Claude CodeCodexCursor +2

MCP server for sports-data APIs (bookmakers, league data, aggregators). Capability-tag system enables cross-provider composition. Runs locally from the sportsdata-mcp Python package.

11 4d ago A tokens not measured original MIT

backtest_design

03

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

How to design and read backtests honestly — lookahead/leakage traps, point-in-time discipline, multiple comparisons, CLV vs ROI, sample size.

6 yesterday A 36 tokens original MIT

build_a_h2h_model

05

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

Worked example of modeldevelopment for match-winner (h2h/moneyline) markets — ratings + logistic regression, home advantage, draw handling.

6 yesterday A 38 tokens original MIT

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

Worked example of modeldevelopment for totals (over/under) markets — scoring-process baseline, pace adjustment, holdout evaluation.

6 yesterday A 32 tokens original MIT

compare_odds

08

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

Cross-bookmaker comparison workflow — find the same selection at every book, surface the best price, and flag value.

6 yesterday A 27 tokens original MIT

dfs_lineup_building

09

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

DFS lineup workflow — site rules first, projections with stated sources, deterministic optimisation via optimizelineup, stacking/ownership as explicit judgment.

6 yesterday A 33 tokens original MIT

model_development

10

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

The general model-building method for ANY market — framing, sample-size discipline, feature selection (user priors + data-driven), baselines, leakage-safe validation, calibration, persistence.

6 yesterday A 40 tokens original MIT

prediction_markets

11

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

How to read prediction markets (Kalshi, Polymarket) — price-as-probability, resolution rules before any comparison, fees/spread, and comparing exchange contracts to book odds honestly.

6 yesterday A 43 tokens original MIT

quant_concepts

12

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

Working definitions of the core quant concepts — Brier, log-loss, calibration, logistic regression, gradient boosting/XGBoost, regularization, walk-forward CV, CLV — and when each tool fits.

6 yesterday A 45 tokens original MIT

reading_a_racecard

13

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

How to read a racecard honestly — barrier/weight/form/scratchings, converting prices to fair probability (overround/vig), cross-book best price, and exotics.

6 yesterday A 42 tokens original MIT

DanielTomaro13/sportsdata-agents

Skill Claude CodeCodex

Interpreting captured line movement — steam vs drift, why the close is the benchmark, and how movement context changes a value report.

6 yesterday A 32 tokens original MIT