Win Football Predictor

胜负彩预测模型，基于Pi-Rating评分系统 + CatBoost/XGBoost/Dixon-Coles三模型融合，覆盖710期9940场历史数据，支持联赛专项微调

This package appears to be a local football prediction toolkit and is internally coherent, but review before running. Key points to consider: - The code will execute as Python when you run the commands in SKILL.md; run it in an isolated environment (virtualenv, container) first. - There are many coding errors and incomplete/truncated sections (see examples below). Fix or audit these before relying on outputs. - The code may try to load pretrained models from a saved_models directory; ensure you trust any model files you add and that none are downloaded from untrusted sources. - The skill makes no network calls in the provided files, but comments reference external data sources — if you or a future maintainer add network fetching, re-audit for endpoints and credentials. - This is for entertainment/analysis only (the SKILL.md includes the same warning); do not treat predictions as betting advice. Specific issues found you may want to address before running: - scripts/data_fetcher/lottery_data.py: typos and bugs (e.g., PERIOD_PREFIXS vs PERIOD_PREFIXES, incorrect tuple parentheses in teams_db, use of random in top-level functions without importing it globally) — these will cause runtime exceptions. - scripts/models/catboost_xgb_pirating.py and other model files: some files are truncated/contain undefined variables (e.g., truncated section with "market_win = odd"), inconsistent field names (h2h vs head_to_head), and various exception-suppression patterns that can hide errors. - predict_engine.py duplicates Pi-Rating implementation and assumes presence of local saved models; it also includes heuristic fallback code rather than robust model-loading checks. If you intend to use this skill: run static linters and tests, execute in a sandbox, correct the obvious bugs, and only add external data/model downloads from trusted sources.

Type: OpenClaw Skill Name: win-football-predictor Version: 1.0.0 The skill bundle is a comprehensive football prediction tool implementing multiple statistical models including Pi-Rating, Dixon-Coles, and ensemble methods (CatBoost/XGBoost). The code in scripts/predict_engine.py and scripts/models/ handles feature engineering and prediction logic consistent with the stated purpose in SKILL.md. No evidence of data exfiltration, malicious execution, or prompt injection was found; the scripts primarily perform mathematical calculations and local file I/O within the expected workspace.