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(1256b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/numpyconfig.h (7466b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/random/bitgen.h (488b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/random/distributions.h (9861b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/random/libdivide.h (80138b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/random/LICENSE.txt (1018b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/ufuncobject.h (11780b), venv/lib/python3.12/site-packages/numpy/_core/include/numpy/utils.h (1185b), venv/lib/python3.12/site-packages/numpy/_core/lib/npy-pkg-config/mlib.ini (147b) (+606 more)\n\nFile v1.0.0:SKILL.md\n\n---\nname: sports-betting-analyzer\ndescription: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。\nlicense: mit\nversion: 1.0.0\n---\n\n# 体育彩票分析助手\n\n智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。\n\n## 核心功能\n\n### 1. 数据采集与分析\n- 收集比赛基础数据（历史战绩、近期走势、主客场表现）\n- 赔率监测（多平台对比、异常检测）\n- 伤病信息跟踪\n- 历史数据存储与分析\n\n### 2. 智能预测模型\n- 多维度特征提取\n- 简单机器学习预测\n- 概率计算与置信度评估\n- 价值投注识别\n\n### 3. 辅助决策\n- 风险评估与提示\n- 投注建议生成\n- 可解释性分析\n- 资金管理建议\n\n## 支持赛事\n\n### NBA\n- 季后赛分析\n- 主客场优势评估\n- 热门球队vs冷门球队分析\n- 四阶段预测（首轮、次轮、分区决赛、总决赛）\n\n### 足球\n- 欧洲五大联赛\n- 世界杯赛事\n- 欧亚指数分析\n- 进球数预测\n\n## 使用方法\n\n### 分析一场比赛\n\n```\n分析 NBA 比赛 湖人 vs 勇士\n分析足球比赛 巴萨 vs 皇马\n```\n\n### 查看历史记录\n\n```\n查看最近的分析记录\n我的投注历史\n```\n\n### 获取预测建议\n\n```\nNBA 今晚有什么值得投注的比赛\n足球周末推荐\n```\n\n## 数据来源\n\n使用开源数据源：\n- 体育官方数据（NBA官网、足球官方）\n- 免费数据API\n- 历史比赛记录\n\n## 预测方法论\n\n### 1. 基础统计\n- 历史交锋记录\n- 近期胜率（近5-10场）\n- 主客场表现\n- 进球失球数据\n\n### 2. 特征工程\n- 球队实力指数\n- 近期状态指数\n- 主客场优势系数\n- 伤病影响因子\n\n### 3. 概率模型\n- 逻辑回归基础预测\n- 蒙特卡洛模拟（简单版）\n- 集成多因素权重\n\n### 4. 风险控制\n- 置信度评估\n- 赔率价值判断\n- 建议投注比例\n\n## 重要提示\n\n⚠️ **本工具仅提供辅助分析，不保证预测准确率**\n- 体育比赛存在不确定性\n- 建议结合自身判断\n- 理性投注，控制风险\n- 不要依赖单一工具决策\n\n## 配置\n\n创建配置文件 `config/sports-betting.json`：\n\n```json\n{\n  \"risk_level\": \"conservative\",\n  \"default_bet_percentage\": 2,\n  \"preferred_leagues\": [\"NBA\", \"Premier League\", \"La Liga\"],\n  \"data_sources\": [\"official\", \"free_api\"]\n}\n```\n\n## 输出格式\n\n分析报告包含：\n- 📊 数据概览\n- 🎯 预测结果（含概率）\n- 💡 投注建议\n- ⚠️ 风险提示\n- 📝 可解释性说明\n\n## 模板\n\n报告模板：`templates/analysis_report.md`\n\n## 更新日志\n\n### v1.0.0 (2026-04-02)\n- MVP版本发布\n- 支持NBA和足球基础分析\n- 简单预测模型\n- 辅助决策功能\n\nFile v1.0.0:README.md\n\n# 体育彩票分析助手\n\n🏆 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具\n\n## 功能特点\n\n- 📊 **数据收集**: 自动收集比赛基础数据、赔率、伤病信息\n- 🎯 **智能预测**: 基于多维度特征的简单机器学习模型\n- 💡 **辅助决策**: 提供风险评估和投注建议\n- 📝 **可解释性**: 详细解释预测依据和各因素影响\n- 🛡️ **风险控制**: 内置风险提示和资金管理建议\n\n## 支持赛事\n\n### 🏀 NBA\n- 季后赛分析\n- 主客场优势评估\n- 四阶段预测\n- 让分和大小球预测\n\n### ⚽ 足球\n- 欧洲五大联赛\n- 世界杯赛事\n- 欧亚指数分析\n- 进球数预测\n\n## 安装\n\n### 依赖要求\n\n```bash\nPython 3.7+\nnumpy\n```\n\n### 安装步骤\n\n1. 克隆或下载此 skill 到 OpenClaw skills 目录\n\n2. 安装 Python 依赖\n\n```bash\ncd ~/.openclaw/workspace/skills/sports-betting-analyzer\npip install -r requirements.txt\n```\n\n3. 配置设置（可选）\n\n编辑 `config/sports-betting.json` 根据需要调整参数\n\n## 使用方法\n\n### 分析比赛\n\n```bash\n# NBA比赛\npython scripts/analyze.py NBA 湖人 勇士\n\n# 足球比赛\npython scripts/analyze.py football 巴萨 皇马\n\n# 世界杯比赛\npython scripts/analyze.py football 巴西 德国\n```\n\n### 获取今日推荐\n\n```bash\n# 所有运动\npython scripts/analyze.py today\n\n# 只看NBA\npython scripts/analyze.py today NBA\n\n# 只看足球\npython scripts/analyze.py today football\n```\n\n### 查看历史记录\n\n```bash\n# 查看最近10条\npython scripts/analyze.py history\n\n# 查看最近20条\npython scripts/analyze.py history 20\n```\n\n### 在OpenClaw中使用\n\n```\n分析 NBA 比赛 湖人 vs 勇士\n分析足球比赛 巴萨 vs 皇马\nNBA 今晚有什么值得投注的比赛\n足球周末推荐\n查看我的分析历史\n```\n\n## 输出示例\n\n```\n============================================================\n📊 体育彩票分析报告\n============================================================\n\n🏆 NBA 比赛分析：湖人 vs 勇士\n\n🎯 预测结果：湖人 胜\n📊 置信度：72.5%\n⚠️ 风险等级：中\n\n💡 建议投注：2%\n\n============================================================\n📋 数据概览\n============================================================\n\n【主队】湖人\n- 赛季胜率：62.5%\n- 近5场：W L W W L\n- 核心球员状态：active active\n\n【客队】勇士\n- 赛季胜率：55.0%\n- 近5场：L W L W W\n- 核心球员状态：active injured\n\n【历史对战】\n- 总场次：10\n- 主队胜：6\n- 客队胜：4\n- 平局：0\n\n============================================================\n🎯 预测结果\n============================================================\n\n结果：team1\n置信度：72.5%\n\n概率分布：\n  湖人: 72.5%\n  勇士: 27.5%\n\n============================================================\n💡 投注建议\n============================================================\n\n建议：推荐投注\n投注比例：2%\n理由：价值投注机会，建议把握\n\n风险等级：moderate\n价值投注：是\n\n============================================================\n📊 因素分析\n============================================================\n\n【实力对比】（重要性：高）\n  数值：+7.50%\n  说明：基于赛季胜率、积分排名等综合实力评估\n\n【主客场优势】（重要性：高）\n  数值：+10.00%\n  说明：主场球队通常有统计优势\n\n【近期状态】（重要性：中）\n  数值：+5.00%\n  说明：基于最近5场比赛的表现\n\n============================================================\n⚠️ 免责声明\n============================================================\n\n⚠️ 重要提示：\n\n1. 本分析仅供参考，不构成投注建议\n2. 体育比赛存在不确定性，预测不保证准确\n3. 请理性投注，控制风险，量力而行\n4. 不要过度依赖单一分析工具\n5. 请遵守当地法律法规\n\n============================================================\n```\n\n## 预测方法论\n\n### 数据维度\n\n1. **实力对比**: 赛季胜率、积分排名、历史表现\n2. **主客场优势**: 统计学上的主场优势系数\n3. **近期状态**: 最近5-10场比赛的表现\n4. **历史对战**: 两队历史交锋记录\n5. **伤病影响**: 关键球员伤病情况\n\n### 模型算法\n\n- **基础模型**: 逻辑回归（简化版）\n- **特征加权**: 多因素加权计算\n- **概率转换**: Sigmoid函数转换为概率\n- **集成预测**: 支持多模型集成（可选）\n- **蒙特卡洛**: 支持模拟验证（可选）\n\n### 风险评估\n\n- **置信度**: 模型对预测结果的信心程度\n- **风险等级**: low / moderate / high\n- **价值投注**: 比较隐含概率与模型概率\n- **投注比例**: 根据风险动态调整\n\n## 配置说明\n\n### config/sports-betting.json\n\n```json\n{\n  \"risk_level\": \"moderate\",          // 风险偏好: conservative/moderate/aggressive\n  \"default_bet_percentage\": 2,       // 默认投注比例(%)\n  \"preferred_leagues\": [...],        // 优先关注的联赛\n  \"min_confidence\": 0.6,            // 最低置信度阈值\n  \"max_daily_bets\": 3,               // 每日最大投注数\n  \"model_settings\": {\n    \"use_ensemble\": false,           // 是否使用集成模型\n    \"use_monte_carlo\": false         // 是否使用蒙特卡洛模拟\n  }\n}\n```\n\n## 重要提示\n\n⚠️ **本工具仅提供辅助分析，不保证预测准确率**\n\n- 体育比赛存在不确定性\n- 建议结合自身判断\n- 理性投注，控制风险\n- 不要依赖单一工具决策\n- 遵守当地法律法规\n\n## 数据来源\n\n**MVP版本**: 使用模拟数据\n\n**后续版本计划**:\n- NBA官方API\n- 足球官方数据源\n- 免费数据API\n- 赔率聚合平台\n\n## 未来规划\n\n- [ ] 接入真实数据源\n- [ ] 增强模型复杂度\n- [ ] 添加更多赛事\n- [ ] 实时赔率监控\n- [ ] 历史准确率追踪\n- [ ] 自动推送推荐\n- [ ] 资金管理工具\n- [ ] 社区分享功能\n\n## 许可证\n\nMIT License\n\n## 免责声明\n\n本工具仅供学习和娱乐使用，不构成任何投资或投注建议。使用本工具产生的任何后果，开发者不承担责任。请理性对待体育博彩，遵守当地法律法规。\n\n---\n\n**开发者**: 黄杨\n**版本**: 1.0.0\n**发布日期**: 2026-04-02\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7a3c73ea0a3f1hcrd0ccweas835fpx\",\n  \"slug\": \"sports-betting-analyzer\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1775066448046\n}\n\nFile v1.0.0:docs/DEVELOPMENT.md\n\n# Sports Betting Analyzer - 开发文档\n\n## 项目结构\n\n```\nsports-betting-analyzer/\n├── SKILL.md                    # OpenClaw Skill 定义\n├── README.md                   # 用户指南\n├── requirements.txt            # Python 依赖\n├── _meta.json                  # Skill 元数据\n├── config/\n│   └── sports-betting.json     # 配置文件\n├── scripts/\n│   ├── analyze.py              # 主分析脚本\n│   ├── data_collector.py       # 数据收集模块\n│   ├── prediction_model.py     # 预测模型模块\n│   ├── report_generator.py     # 报告生成模块\n│   └── runner.sh               # OpenClaw 集成脚本\n├── data/                       # 数据存储目录（运行时生成）\n│   ├── analysis_history.json   # 分析历史记录\n│   └── match_*.json            # 比赛数据\n└── docs/                       # 文档目录\n    └── DEVELOPMENT.md          # 本开发文档\n```\n\n## 核心模块\n\n### 1. DataCollector (data_collector.py)\n\n**职责**:\n- 收集比赛基础数据\n- 提取特征\n- 存储历史数据\n\n**方法**:\n- `collect_match_data()`: 收集比赛数据\n- `extract_features()`: 提取预测特征\n- `_collect_team_stats()`: 收集球队统计\n- `_collect_head_to_head()`: 收集历史对战\n- `_collect_odds()`: 收集赔率数据\n- `_collect_injuries()`: 收集伤病信息\n\n**数据来源**（当前版本）:\n- 模拟数据生成\n- 随机值模拟真实分布\n\n**数据来源**（未来版本）:\n- NBA官方API\n- 足球官方数据源\n- 免费数据API\n- 赔率聚合平台\n\n### 2. PredictionModel (prediction_model.py)\n\n**职责**:\n- 生成比赛预测\n- 计算概率\n- 评估置信度\n\n**方法**:\n- `predict()`: 主预测方法\n- `_predict_nba()`: NBA预测\n- `_predict_football()`: 足球预测\n- `_sigmoid()`: Sigmoid激活函数\n- `_predict_spread()`: 预测让分\n- `_predict_total()`: 预测总分\n- `_predict_over_under()`: 预测大小球\n- `ensemble_predict()`: 集成预测\n- `monte_carlo_simulation()`: 蒙特卡洛模拟\n\n**算法**:\n- 简化的逻辑回归\n- 多特征加权\n- Sigmoid概率转换\n- 支持集成学习（可选）\n\n### 3. ReportGenerator (report_generator.py)\n\n**职责**:\n- 生成分析报告\n- 格式化输出\n- 风险提示\n\n**方法**:\n- `generate_report()`: 生成完整报告\n- `_generate_summary()`: 生成摘要\n- `_generate_data_overview()`: 生成数据概览\n- `_format_prediction()`: 格式化预测结果\n- `_generate_betting_recommendation()`: 生成投注建议\n- `_generate_explanation()`: 生成解释性说明\n- `_analyze_factors()`: 分析各因素\n- `format_as_text()`: 格式化为文本\n- `save_report()`: 保存报告\n\n### 4. SportsBettingAnalyzer (analyze.py)\n\n**职责**:\n- 主控制器\n- 协调各模块\n- 命令行接口\n\n**方法**:\n- `analyze_match()`: 分析比赛\n- `_assess_risk()`: 评估风险\n- `_is_value_bet()`: 判断价值投注\n- `_calculate_bet_percentage()`: 计算投注比例\n- `get_today_recommendations()`: 获取今日推荐\n- `show_analysis_history()`: 显示分析历史\n\n## 特征工程\n\n### NBA 特征\n\n1. **实力对比** (strength_diff)\n   - 赛季胜率差异\n   - 积分排名差异\n   - 历史表现\n\n2. **主客场优势** (home_advantage)\n   - 主场胜率 vs 客场胜率\n   - NBA主场优势约10-15%\n\n3. **近期状态** (recent_form)\n   - 最近5场比赛表现\n   - 胜/负统计\n   - 动态趋势\n\n4. **历史对战** (h2h_advantage)\n   - 历史交锋记录\n   - 心理优势\n\n5. **伤病影响** (injury_impact)\n   - 关键球员缺阵\n   - 影响系数\n\n### 足球特征\n\n1. **实力对比** (strength_diff)\n   - 赛季胜率\n   - 积分排名\n   - 进球失球\n\n2. **主客场优势** (home_advantage)\n   - 足球主场优势约8-10%\n   - 欧洲联赛主场明显\n\n3. **近期状态** (recent_form)\n   - 最近5场表现\n   - 胜/平/负统计\n\n4. **历史对战** (h2h_advantage)\n   - 历史交锋\n   - 风格克制\n\n5. **伤病影响** (injury_impact)\n   - 核心球员状态\n\n## 预测模型\n\n### 基础模型\n\n```\nscore = w1*strength_diff + w2*home_advantage +\n        w3*recent_form + w4*h2h + w5*injury_impact\n\nprobability = sigmoid(score)\n```\n\n### 权重配置\n\n**NBA**:\n- strength: 0.3\n- home_advantage: 0.2\n- recent_form: 0.15\n- h2h: 0.15\n- injuries: -0.1\n\n**足球**:\n- strength: 0.25\n- home_advantage: 0.2\n- recent_form: 0.15\n- h2h: 0.15\n- injuries: -0.1\n\n### 概率转换\n\n使用 Sigmoid 函数将得分转换为概率:\n\n```\nsigmoid(x) = 1 / (1 + e^(-x))\n```\n\n### 三分式概率（足球）\n\n足球需要考虑平局:\n\n```\nP(team1) = sigmoid(score + offset)\nP(team2) = sigmoid(-score + offset)\nP(draw) = 1 - P(team1) - P(team2)\n```\n\n## 风险评估\n\n### 置信度分级\n\n- **高置信度** (> 0.75): 低风险\n- **中置信度** (0.65 - 0.75): 中风险\n- **低置信度** (< 0.65): 高风险\n\n### 价值投注判断\n\n比较模型概率与市场赔率隐含概率:\n\n```\nif model_prob > market_prob + threshold:\n    value_bet = True\n```\n\n### 投注比例计算\n\n根据置信度和风险等级动态调整:\n\n```\nbase_percentage = config.default_bet_percentage\n\nif confidence > 0.8:\n    bet_percentage = base * 1.5\nelif confidence > 0.7:\n    bet_percentage = base\nelse:\n    bet_percentage = base * 0.5\n```\n\n## 配置说明\n\n### config/sports-betting.json\n\n```json\n{\n  \"risk_level\": \"moderate\",        // 风险偏好\n  \"default_bet_percentage\": 2,     // 默认投注比例(%)\n  \"preferred_leagues\": [...],      // 优先联赛\n  \"min_confidence\": 0.6,          // 最低置信度\n  \"max_daily_bets\": 3,             // 每日最大投注数\n  \"model_settings\": {\n    \"use_ensemble\": false,         // 集成模型\n    \"num_ensemble_models\": 3,\n    \"use_monte_carlo\": false       // 蒙特卡洛模拟\n  }\n}\n```\n\n## 扩展方向\n\n### 短期\n\n1. **真实数据源接入**\n   - NBA Stats API\n   - Football-Data.org\n   - TheSportsDB\n\n2. **模型优化**\n   - 调整权重参数\n   - 添加更多特征\n   - 历史数据回测\n\n3. **功能增强**\n   - 实时赔率监控\n   - 历史准确率追踪\n   - 自动推送推荐\n\n### 长期\n\n1. **高级模型**\n   - 深度学习\n   - 随机森林\n   - XGBoost\n\n2. **实时功能**\n   - 比赛直播分析\n   - 盘口变动跟踪\n   - 套利机会检测\n\n3. **社区功能**\n   - 分享分析结果\n   - 用户评论\n   - 排行榜\n\n## 测试\n\n### 单元测试\n\n```bash\npython3 -m pytest tests/\n```\n\n### 集成测试\n\n```bash\n# 测试NBA分析\n./scripts/runner.sh NBA 湖人 勇士\n\n# 测试足球分析\n./scripts/runner.sh football 巴萨 皇马\n\n# 测试今日推荐\n./scripts/runner.sh today NBA\n```\n\n### 性能测试\n\n```bash\ntime ./scripts/runner.sh NBA 测试队1 测试队2\n```\n\n## 已知限制\n\n1. **数据准确性**: 当前使用模拟数据\n2. **模型简单**: 仅使用简化逻辑回归\n3. **无实时性**: 不支持实时数据更新\n4. **覆盖率**: 仅支持NBA和部分足球联赛\n5. **准确率**: 未经过历史数据验证\n\n## 免责声明\n\n本工具仅供学习和娱乐使用，不构成任何投资或投注建议。\n\n使用本工具产生的任何后果，开发者不承担责任。\n\n请理性对待体育博彩，遵守当地法律法规。\n\n---\n\n**开发者**: 黄杨\n**版本**: 1.0.0\n**最后更新**: 2026-04-02\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nSports Betting Analyzer helps an agent produce sports betting analysis for NBA and football matches using basic statistics, simple prediction models, risk assessment, and explanatory reports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[nopedijah](https://clawhub.ai/user/nopedijah)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to request match analysis, recommendation-style betting reports, recent-history summaries, and command-line runs for supported NBA and football scenarios. Outputs should be treated as decision support only, not verified wagering advice.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can present gambling recommendations based on simulated or random data rather than verified live sports data.\n\nMitigation: Review outputs as entertainment or decision-support text only, verify data independently, and do not rely on generated percentages or recommendations for wagering.\n\nRisk: The bundled scripts can store betting-analysis history and reports locally.\n\nMitigation: Review or disable local history and report saving before use when betting interests or team names are sensitive.\n\nRisk: The runner can auto-install an unpinned dependency.\n\nMitigation: Pin and review dependencies before running the installer path in managed or production environments.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/nopedijah/skills/sports-betting-analyzer)\n- [README](artifact/README.md)\n- [Development Notes](artifact/docs/DEVELOPMENT.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown and terminal-oriented text reports with inline shell commands and JSON configuration examples]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May save local analysis history and optional report files when the bundled scripts are run.]\n\n## Skill Version(s):\n\n1.0.0 (source: release metadata and frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/npysort/x86-simd-sort/LICENSE.md\n\nBSD 3-Clause License\n\nCopyright (c) 2022, Intel. All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n   list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n   this list of conditions and the following disclaimer in the documentation\n   and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n   contributors may be used to endorse or promote products derived from\n   this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/fft/pocketfft/LICENSE.md\n\nCopyright (C) 2010-2018 Max-Planck-Society\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification,\nare permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n* Redistributions in binary form must reproduce the above copyright notice, this\n  list of conditions and the following disclaimer in the documentation and/or\n  other materials provided with the distribution.\n* Neither the name of the copyright holder nor the names of its contributors may\n  be used to endorse or promote products derived from this software without\n  specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\nANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/LICENSE.md\n\n**This software is dual-licensed under the The University of Illinois/NCSA\nOpen Source License (NCSA) and The 3-Clause BSD License**\n\n# NCSA Open Source License\n**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**\n\nDeveloped by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,\n<kevin.k.sheppard@gmail.com>)\n[http://www.kevinsheppard.com](http://www.kevinsheppard.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal with\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\nof the Software, and to permit persons to whom the Software is furnished to do\nso, subject to the following conditions:\n\nRedistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimers.\n\nRedistributions in binary form must reproduce the above copyright notice, this\nlist of conditions and the following disclaimers in the documentation and/or\nother materials provided with the distribution.\n\nNeither the names of Kevin Sheppard, nor the names of any contributors may be\nused to endorse or promote products derived from this Software without specific\nprior written permission.\n\n**THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nCONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH\nTHE SOFTWARE.**\n\n\n# 3-Clause BSD License\n**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice,\n   this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n   this list of conditions and the following disclaimer in the documentation\n   and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its contributors\n   may be used to endorse or promote products derived from this software\n   without specific prior written permission.\n\n**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\nARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\nLIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\nSUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\nCONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\nARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\nTHE POSSIBILITY OF SUCH DAMAGE.**\n\n# Components\n\nMany parts of this module have been derived from original sources, \noften the algorithm's designer. Component licenses are located with \nthe component code.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/distributions/LICENSE.md\n\n## NumPy\n\nCopyright (c) 2005-2017, NumPy Developers.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are\nmet:\n\n* Redistributions of source code must retain the above copyright\n   notice, this list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above\n   copyright notice, this list of conditions and the following\n   disclaimer in the documentation and/or other materials provided\n   with the distribution.\n\n* Neither the name of the NumPy Developers nor the names of any\n   contributors may be used to endorse or promote products derived\n   from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\nA PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nOWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\nLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\nDATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\nTHEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\n## Julia\n\nThe ziggurat methods were derived from Julia.\n\nCopyright (c) 2009-2019: Jeff Bezanson, Stefan Karpinski, Viral B. Shah,\nand other contributors:\n\nhttps://github.com/JuliaLang/julia/contributors\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\nNONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE\nLIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\nOF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\nWITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/mt19937/LICENSE.md\n\n# MT19937\n\nCopyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)\n\nThe rk_random and rk_seed functions algorithms and the original design of\nthe Mersenne Twister RNG:\n\n  Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,\n  All rights reserved.\n\n  Redistribution and use in source and binary forms, with or without\n  modification, are permitted provided that the following conditions\n  are met:\n\n  1. Redistributions of source code must retain the above copyright\n  notice, this list of conditions and the following disclaimer.\n\n  2. Redistributions in binary form must reproduce the above copyright\n  notice, this list of conditions and the following disclaimer in the\n  documentation and/or other materials provided with the distribution.\n\n  3. The names of its contributors may not be used to endorse or promote\n  products derived from this software without specific prior written\n  permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\nA PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER\nOR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\nEXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\nPROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\nPROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\nLIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\nNEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nOriginal algorithm for the implementation of rk_interval function from\nRichard J. Wagner's implementation of the Mersenne Twister RNG, optimised by\nMagnus Jonsson.\n\nConstants used in the rk_double implementation by Isaku Wada.\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be included\nin all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\nOR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/pcg64/LICENSE.md\n\n# PCG64\n\n## The MIT License\n\nPCG Random Number Generation for C.\n\nCopyright 2014 Melissa O'Neill <oneill@pcg-random.org>\n\nPermission is hereby granted, free of charge, to any person obtaining \na copy of this software and associated documentation files (the \"Software\"), \nto deal in the Software without restriction, including without limitation \nthe rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in \nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR \nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS \nFOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR \nCOPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER \nIN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN \nCONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\nFile v1.0.0:venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/philox/LICENSE.md\n\n# PHILOX\n\nCopyright 2010-2012, D. E. Shaw Research.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are\nmet:\n\n* Redistributions of source code must retain the above copyright\n  notice, this list of conditions, and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright\n  notice, this list of conditions, and the following disclaimer in the\n  documentation and/or other materials provided with the distribution.\n\n* Neither the name of D. E. Shaw Research nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\nA PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nOWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\nLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\nDATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\nTHEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","readmeExcerpt":"Skill: Sports Betting Analyzer Owner: nopedijah Summary: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-01T18:00:48.046Z | user Initial MVP release - AI-powered sports betting analysis assistant for NBA and football with data collection, prediction models, and risk assessment Archive index: Archive v1.0.0: 1006 files, 44","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"分析 NBA 比赛 湖人 vs 勇士\n分析足球比赛 巴萨 vs 皇马"},{"language":"text","snippet":"查看最近的分析记录\n我的投注历史"},{"language":"text","snippet":"NBA 今晚有什么值得投注的比赛\n足球周末推荐"},{"language":"json","snippet":"{\n  \"risk_level\": \"conservative\",\n  \"default_bet_percentage\": 2,\n  \"preferred_leagues\": [\"NBA\", \"Premier League\", \"La Liga\"],\n  \"data_sources\": [\"official\", \"free_api\"]\n}"},{"language":"bash","snippet":"Python 3.7+\nnumpy"},{"language":"bash","snippet":"cd ~/.openclaw/workspace/skills/sports-betting-analyzer\npip install -r requirements.txt"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: sports-betting-analyzer\ndescription: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。\nlicense: mit\nversion: 1.0.0\n---\n\n# 体育彩票分析助手\n\n智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。\n\n## 核心功能\n\n### 1. 数据采集与分析\n- 收集比赛基础数据（历史战绩、近期走势、主客场表现）\n- 赔率监测（多平台对比、异常检测）\n- 伤病信息跟踪\n- 历史数据存储与分析\n\n### 2. 智能预测模型\n- 多维度特征提取\n- 简单机器学习预测\n- 概率计算与置信度评估\n- 价值投注识别\n\n### 3. 辅助决策\n- 风险评估与提示\n- 投注建议生成\n- 可解释性分析\n- 资金管理建议\n\n## 支持赛事\n\n### NBA\n- 季后赛分析\n- 主客场优势评估\n- 热门球队vs冷门球队分析\n- 四阶段预测（首轮、次轮、分区决赛、总决赛）\n\n### 足球\n- 欧洲五大联赛\n- 世界杯赛事\n- 欧亚指数分析\n- 进球数预测\n\n## 使用方法\n\n### 分析一场比赛\n\n```\n分析 NBA 比赛 湖人 vs 勇士\n分析足球比赛 巴萨 vs 皇马\n```\n\n### 查看历史记录\n\n```\n查看最近的分析记录\n我的投注历史\n```\n\n### 获取预测建议\n\n```\nNBA 今晚有什么值得投注的比赛\n足球周末推荐\n```\n\n## 数据来源\n\n使用开源数据源：\n- 体育官方数据（NBA官网、足球官方）\n- 免费数据API\n- 历史比赛记录\n\n## 预测方法论\n\n### 1. 基础统计\n- 历史交锋记录\n- 近期胜率（近5-10场）\n- 主客场表现\n- 进球失球数据\n\n### 2. 特征工程\n- 球队实力指数\n- 近期状态指数\n- 主客场优势系数\n- 伤病影响因子\n\n### 3. 概率模型\n- 逻辑回归基础预测\n- 蒙特卡洛模拟（简单版）\n- 集成多因素权重\n\n### 4. 风险控制\n- 置信度评估\n- 赔率价值判断\n- 建议投注比例\n\n## 重要提示\n\n⚠️ **本工具仅提供辅助分析，不保证预测准确率**\n- 体育比赛存在不确定性\n- 建议结合自身判断\n- 理性投注，控制风险\n- 不要依赖单一工具决策\n\n## 配置\n\n创建配置文件 `config/sports-betting.json`：\n\n```json\n{\n  \"risk_level\": \"conservative\",\n  \"default_bet_percentage\": 2,\n  \"preferred_leagues\": [\"NBA\", \"Premier League\", \"La Liga\"],\n  \"data_sources\": [\"official\", \"free_api\"]\n}\n```\n\n## 输出格式\n\n分析报告包含：\n- 📊 数据概览\n- 🎯 预测结果（含概率）\n- 💡 投注建议\n- ⚠️ 风险提示\n- 📝 可解释性说明\n\n## 模板\n\n报告模板：`templates/analysis_report.md`\n\n## 更新日志\n\n### v1.0.0 (2026-04-02)\n- MVP版本发布\n- 支持NBA和足球基础分析\n- 简单预测模型\n- 辅助决策功能"},{"path":"README.md","content":"# 体育彩票分析助手\n\n🏆 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具\n\n## 功能特点\n\n- 📊 **数据收集**: 自动收集比赛基础数据、赔率、伤病信息\n- 🎯 **智能预测**: 基于多维度特征的简单机器学习模型\n- 💡 **辅助决策**: 提供风险评估和投注建议\n- 📝 **可解释性**: 详细解释预测依据和各因素影响\n- 🛡️ **风险控制**: 内置风险提示和资金管理建议\n\n## 支持赛事\n\n### 🏀 NBA\n- 季后赛分析\n- 主客场优势评估\n- 四阶段预测\n- 让分和大小球预测\n\n### ⚽ 足球\n- 欧洲五大联赛\n- 世界杯赛事\n- 欧亚指数分析\n- 进球数预测\n\n## 安装\n\n### 依赖要求\n\n```bash\nPython 3.7+\nnumpy\n```\n\n### 安装步骤\n\n1. 克隆或下载此 skill 到 OpenClaw skills 目录\n\n2. 安装 Python 依赖\n\n```bash\ncd ~/.openclaw/workspace/skills/sports-betting-analyzer\npip install -r requirements.txt\n```\n\n3. 配置设置（可选）\n\n编辑 `config/sports-betting.json` 根据需要调整参数\n\n## 使用方法\n\n### 分析比赛\n\n```bash\n# NBA比赛\npython scripts/analyze.py NBA 湖人 勇士\n\n# 足球比赛\npython scripts/analyze.py football 巴萨 皇马\n\n# 世界杯比赛\npython scripts/analyze.py football 巴西 德国\n```\n\n### 获取今日推荐\n\n```bash\n# 所有运动\npython scripts/analyze.py today\n\n# 只看NBA\npython scripts/analyze.py today NBA\n\n# 只看足球\npython scripts/analyze.py today football\n```\n\n### 查看历史记录\n\n```bash\n# 查看最近10条\npython scripts/analyze.py history\n\n# 查看最近20条\npython scripts/analyze.py history 20\n```\n\n### 在OpenClaw中使用\n\n```\n分析 NBA 比赛 湖人 vs 勇士\n分析足球比赛 巴萨 vs 皇马\nNBA 今晚有什么值得投注的比赛\n足球周末推荐\n查看我的分析历史\n```\n\n## 输出示例\n\n```\n============================================================\n📊 体育彩票分析报告\n============================================================\n\n🏆 NBA 比赛分析：湖人 vs 勇士\n\n🎯 预测结果：湖人 胜\n📊 置信度：72.5%\n⚠️ 风险等级：中\n\n💡 建议投注：2%\n\n============================================================\n📋 数据概览\n============================================================\n\n【主队】湖人\n- 赛季胜率：62.5%\n- 近5场：W L W W L\n- 核心球员状态：active active\n\n【客队】勇士\n- 赛季胜率：55.0%\n- 近5场：L W L W W\n- 核心球员状态：active injured\n\n【历史对战】\n- 总场次：10\n- 主队胜：6\n- 客队胜：4\n- 平局：0\n\n============================================================\n🎯 预测结果\n============================================================\n\n结果：team1\n置信度：72.5%\n\n概率分布：\n  湖人: 72.5%\n  勇士: 27.5%\n\n============================================================\n💡 投注建议\n============================================================\n\n建议：推荐投注\n投注比例：2%\n理由：价值投注机会，建议把握\n\n风险等级：moderate\n价值投注：是\n\n============================================================\n📊 因素分析\n============================================================\n\n【实力对比】（重要性：高）\n  数值：+7.50%\n  说明：基于赛季胜率、积分排名等综合实力评估\n\n【主客场优势】（重要性：高）\n  数值：+10.00%\n  说明：主场球队通常有统计优势\n\n【近期状态】（重要性：中）\n  数值：+5.00%\n  说明：基于最近5场比赛的表现\n\n============================================================\n⚠️ 免责声明\n============================================================\n\n⚠️ 重要提示：\n\n1. 本分析仅供参考，不构成投注建议\n2. 体育比赛存在不确定性，预测不保证准确\n3. 请理性投注，控制风险，量力而行\n4. 不要过度依赖单一分析工具\n5. 请遵守当地法律法规\n\n============================================================\n```\n\n## 预测方法论\n\n### 数据维度\n\n1. **实力对比**: 赛季胜率、积分排名、历史表现\n2. **主客场优势**: 统计学上的主场优势系数\n3. **近期状态**: 最近5-10场比赛的表现\n4. **历史对战**: 两队历史交锋记录\n5. **伤病影响**: 关键球员伤病情况\n\n### 模型算法\n\n- **基础模型**: 逻辑回归（简化版）\n- **特征加权**: 多因素加权计算\n- **概率转换**: Sigmoid函数转换为概率\n- **集成预测**: 支持多模型集成（可选）\n- **蒙特卡洛**: 支持模拟验证（可选）\n\n### 风险评估\n\n- **置信度**: 模型对预测结果的信心程度\n- **风险等级**: low / moderate / high\n- **价值投注**: 比较隐含概率与模型概率\n- **投注比例**: 根据风险动态调整\n\n## 配置说明\n\n### config/sports-betting.j"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7a3c73ea0a3f1hcrd0ccweas835fpx\",\n  \"slug\": \"sports-betting-analyzer\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1775066448046\n}"},{"path":"docs/DEVELOPMENT.md","content":"# Sports Betting Analyzer - 开发文档\n\n## 项目结构\n\n```\nsports-betting-analyzer/\n├── SKILL.md                    # OpenClaw Skill 定义\n├── README.md                   # 用户指南\n├── requirements.txt            # Python 依赖\n├── _meta.json                  # Skill 元数据\n├── config/\n│   └── sports-betting.json     # 配置文件\n├── scripts/\n│   ├── analyze.py              # 主分析脚本\n│   ├── data_collector.py       # 数据收集模块\n│   ├── prediction_model.py     # 预测模型模块\n│   ├── report_generator.py     # 报告生成模块\n│   └── runner.sh               # OpenClaw 集成脚本\n├── data/                       # 数据存储目录（运行时生成）\n│   ├── analysis_history.json   # 分析历史记录\n│   └── match_*.json            # 比赛数据\n└── docs/                       # 文档目录\n    └── DEVELOPMENT.md          # 本开发文档\n```\n\n## 核心模块\n\n### 1. DataCollector (data_collector.py)\n\n**职责**:\n- 收集比赛基础数据\n- 提取特征\n- 存储历史数据\n\n**方法**:\n- `collect_match_data()`: 收集比赛数据\n- `extract_features()`: 提取预测特征\n- `_collect_team_stats()`: 收集球队统计\n- `_collect_head_to_head()`: 收集历史对战\n- `_collect_odds()`: 收集赔率数据\n- `_collect_injuries()`: 收集伤病信息\n\n**数据来源**（当前版本）:\n- 模拟数据生成\n- 随机值模拟真实分布\n\n**数据来源**（未来版本）:\n- NBA官方API\n- 足球官方数据源\n- 免费数据API\n- 赔率聚合平台\n\n### 2. PredictionModel (prediction_model.py)\n\n**职责**:\n- 生成比赛预测\n- 计算概率\n- 评估置信度\n\n**方法**:\n- `predict()`: 主预测方法\n- `_predict_nba()`: NBA预测\n- `_predict_football()`: 足球预测\n- `_sigmoid()`: Sigmoid激活函数\n- `_predict_spread()`: 预测让分\n- `_predict_total()`: 预测总分\n- `_predict_over_under()`: 预测大小球\n- `ensemble_predict()`: 集成预测\n- `monte_carlo_simulation()`: 蒙特卡洛模拟\n\n**算法**:\n- 简化的逻辑回归\n- 多特征加权\n- Sigmoid概率转换\n- 支持集成学习（可选）\n\n### 3. ReportGenerator (report_generator.py)\n\n**职责**:\n- 生成分析报告\n- 格式化输出\n- 风险提示\n\n**方法**:\n- `generate_report()`: 生成完整报告\n- `_generate_summary()`: 生成摘要\n- `_generate_data_overview()`: 生成数据概览\n- `_format_prediction()`: 格式化预测结果\n- `_generate_betting_recommendation()`: 生成投注建议\n- `_generate_explanation()`: 生成解释性说明\n- `_analyze_factors()`: 分析各因素\n- `format_as_text()`: 格式化为文本\n- `save_report()`: 保存报告\n\n### 4. SportsBettingAnalyzer (analyze.py)\n\n**职责**:\n- 主控制器\n- 协调各模块\n- 命令行接口\n\n**方法**:\n- `analyze_match()`: 分析比赛\n- `_assess_risk()`: 评估风险\n- `_is_value_bet()`: 判断价值投注\n- `_calculate_bet_percentage()`: 计算投注比例\n- `get_today_recommendations()`: 获取今日推荐\n- `show_analysis_history()`: 显示分析历史\n\n## 特征工程\n\n### NBA 特征\n\n1. **实力对比** (strength_diff)\n   - 赛季胜率差异\n   - 积分排名差异\n   - 历史表现\n\n2. **主客场优势** (home_advantage)\n   - 主场胜率 vs 客场胜率\n   - NBA主场优势约10-15%\n\n3. **近期状态** (recent_form)\n   - 最近5场比赛表现\n   - 胜/负统计\n   - 动态趋势\n\n4. **历史对战** (h2h_advantage)\n   - 历史交锋记录\n   - 心理优势\n\n5. **伤病影响** (injury_impact)\n   - 关键球员缺阵\n   - 影响系数\n\n### 足球特征\n\n1. **实力对比** (strength_diff)\n   - 赛季胜率\n   - 积分排名\n   - 进球失球\n\n2. **主客场优势** (home_advantage)\n   - 足球主场优势约8-10%\n   - 欧洲联赛主场明显\n\n3. **近期状态** (recent_form)\n   - 最近5场表现\n   - 胜/平/负统计\n\n4. **历史对战** (h2h_advantage)\n   - 历史交锋\n   - 风格克制\n\n5. **伤病影响** (injury_impact)\n   - 核心球员状态\n\n## 预测模型\n\n### 基础模型\n\n```\nscore = w1*strength_diff + w2*home_advantage +\n        w3*recent_form + w4*h2h + w5*injury_impact\n\nprobability = sigmoid(score)\n```\n\n### 权重配置\n\n**NBA**:\n- strength: 0.3\n- home_advantage: 0.2\n- rece"},{"path":"skill-card.md","content":"## Description:\n\nSports Betting Analyzer helps an agent produce sports betting analysis for NBA and football matches using basic statistics, simple prediction models, risk assessment, and explanatory reports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[nopedijah](https://clawhub.ai/user/nopedijah)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to request match analysis, recommendation-style betting reports, recent-history summaries, and command-line runs for supported NBA and football scenarios. Outputs should be treated as decision support only, not verified wagering advice.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can present gambling recommendations based on simulated or random data rather than verified live sports data.\n\nMitigation: Review outputs as entertainment or decision-support text only, verify data independently, and do not rely on generated percentages or recommendations for wagering.\n\nRisk: The bundled scripts can store betting-analysis history and reports locally.\n\nMitigation: Review or disable local history and report saving before use when betting interests or team names are sensitive.\n\nRisk: The runner can auto-install an unpinned dependency.\n\nMitigation: Pin and review dependencies before running the installer path in managed or production environments.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/nopedijah/skills/sports-betting-analyzer)\n- [README](artifact/README.md)\n- [Development Notes](artifact/docs/DEVELOPMENT.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown and terminal-oriented text reports with inline shell commands and JSON configuration examples]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May save local analysis history and optional report files when the bundled scripts are run.]\n\n## Skill Version(s):\n\n1.0.0 (source: release metadata and frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。 Skill: Sports Betting Analyzer Owner: nopedijah Summary: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-01T18:00:48.046Z | user Initial MVP release - AI-powered sports betting analysis assistant for NBA and football with data collection, prediction models, and risk assessment Archive index: Archive v1.0.0: 1006 files, 44","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":856,"uniquenessScore":57,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T04:50:48.205Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-09T04:50:48.205Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:15:01.195Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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