npx skills add https://github.com/machina-sports/sports-skills --skill betting在编写查询之前,请查阅 references/api-reference.md 以了解赔率格式、命令参数和关键概念。
sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160
Python SDK:
from sports_skills import betting
betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)
重要提示:在调用任何分析命令之前,请验证:
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
devig 命令去除抽水。nba get_scoreboard 获取):主队:-150,客队:+1300.52)devig --odds=-150,+130 --format=american → 公平概率:主队 57.9%,客队 42.1%find_edge --fair_prob=0.579 --market_prob=0.52 → 优势:5.9%,期望值:11.3%evaluate_bet --book_odds=-150,+130 --market_prob=0.52find_arbitrage --market_probs=0.48,0.49 --labels=home,awayparlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600line_movement --open_odds=-140 --close_odds=-160示例 1:使用 ESPN 和 Polymarket 价格检查优势 用户说:"湖人队的比赛有优势吗?ESPN 赔率是 -150,Polymarket 价格是 52 美分" 操作:
devig(odds="-150,+130", format="american") → 主队公平概率约 58%find_edge(fair_prob=0.58, market_prob=0.52) → 优势约 6%,期望值为正kelly_criterion(fair_prob=0.58, market_prob=0.52) → 最优投注比例
结果:呈现优势百分比、每美元期望值以及建议的投注金额占资金的比例示例 2:套利机会检测 用户说:"我能套利吗?Polymarket 主队 48 美分,Kalshi 客队 49 美分" 操作:
find_arbitrage(market_probs="0.48,0.49", labels="home,away")arbitrage_found
结果:如果存在套利:呈现分配百分比和保证的投资回报率。如果不存在:呈现超额概率并解释没有保证利润示例 3:串关注评估 用户说:"这个 +600 的 3 选项串关注值得投吗?" 操作:
parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
结果:呈现组合公平概率、优势、期望值、+EV 或 -EV 的判断以及凯利分数示例 4:盘口变动解读 用户说:"盘口从 -140 移动到 -160,这意味着什么?" 操作:
line_movement(open_odds=-140, close_odds=-160)
结果:呈现概率变化、方向、幅度以及分类(专业资金动向、大众资金推动等)示例 5:为标准的让分盘去除抽水 用户说:"这个让分盘的真实赔率是多少?两边都是 -110" 操作:
devig(odds="-110,-110", format="american")
结果:呈现每边 50% 的公平概率,抽水约 4.5%示例 6:赔率格式转换 用户说:"将 -200 转换为隐含概率" 操作:
convert_odds(odds=-200, from_format="american")
结果:呈现 66.7% 的隐含概率和 1.50 的十进制赔率get_oddscalculate_evfind_edge 或 evaluate_bet。compare_marketsmarkets 技能进行跨平台比较。如果某个命令未在 references/api-reference.md 中列出,则它不存在。
错误:调用 convert_odds 时出现 ValueError: unknown format
原因:from_format 参数不是 american、decimal 或 probability 之一
解决方案:准确使用 american、decimal 或 probability 作为格式字符串
错误:find_edge 在预期为正优势时返回负期望值
原因:公平概率和市场概率可能颠倒了,或者跳过了去除抽水步骤
解决方案:首先对博彩公司赔率运行 devig,然后将去除抽水后的 fair_prob 传递给 find_edge
错误:即使价格看起来很低,find_arbitrage 也显示没有套利机会
原因:当正确包含所有结果时,价格总和可能超过 1.0
解决方案:验证您是否使用了所有结果的正确概率;检查结果中的 total_implied
错误:凯利分数非常高(大于 0.5)
原因:优势估计非常大 —— 通常是由于公平概率计算错误
解决方案:使用半凯利或四分之一凯利进行保守的资金分配。通过 devig 重新验证公平概率
每周安装量
140
代码库
GitHub 星标
55
首次出现
2026年2月26日
安全审计
安装于
codex140
opencode140
gemini-cli139
github-copilot139
cline138
kimi-cli138
Before writing queries, consult references/api-reference.md for odds formats, command parameters, and key concepts.
sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160
Python SDK:
from sports_skills import betting
betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)
CRITICAL: Before calling any analysis command, verify:
devig before computing edge vs prediction market prices.nba get_scoreboard): Home: -150, Away: +1300.52)devig --odds=-150,+130 --format=american → Fair: Home 57.9%, Away 42.1%find_edge --fair_prob=0.579 --market_prob=0.52 → Edge: 5.9%, EV: 11.3%evaluate_bet --book_odds=-150,+130 --market_prob=0.52find_arbitrage --market_probs=0.48,0.49 --labels=home,awayparlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600line_movement --open_odds=-140 --close_odds=-160Example 1: Edge check using ESPN and Polymarket prices User says: "Is there edge on the Lakers game? ESPN has them at -150 and Polymarket has them at 52 cents" Actions:
devig(odds="-150,+130", format="american") → fair home probability ~58%find_edge(fair_prob=0.58, market_prob=0.52) → edge ~6%, positive EVkelly_criterion(fair_prob=0.58, market_prob=0.52) → optimal bet fraction Result: Present edge percentage, EV per dollar, and recommended bet size as % of bankrollExample 2: Arbitrage opportunity detection User says: "Can I arb this? Polymarket has home at 48 cents and Kalshi has away at 49 cents" Actions:
find_arbitrage(market_probs="0.48,0.49", labels="home,away")arbitrage_found in result Result: If arbitrage: present allocation percentages and guaranteed ROI. If not: present overround and explain no guaranteed profitExample 3: Parlay evaluation User says: "Is this 3-leg parlay at +600 worth it?" Actions:
parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600) Result: Present combined fair probability, edge, EV, +EV or -EV verdict, and Kelly fractionExample 4: Line movement interpretation User says: "The line moved from -140 to -160, what does that mean?" Actions:
line_movement(open_odds=-140, close_odds=-160) Result: Present probability shift, direction, magnitude, and classification (sharp action, steam move, etc.)Example 5: De-vig a standard spread User says: "What are the true odds for this spread? Both sides are -110" Actions:
devig(odds="-110,-110", format="american") Result: Present each side as 50% fair probability, vig is ~4.5%Example 6: Odds format conversion User says: "Convert -200 to implied probability" Actions:
convert_odds(odds=-200, from_format="american") Result: Present 66.7% implied probability and 1.50 decimal oddsget_oddscalculate_evfind_edge or evaluate_bet instead.compare_marketsmarkets skill for cross-platform comparison.If a command is not listed in references/api-reference.md, it does not exist.
Error: ValueError: unknown format when calling convert_odds Cause: The from_format parameter is not one of american, decimal, or probability Solution: Use exactly american, decimal, or probability as the format string
Error: find_edge returns negative EV when a positive edge is expected Cause: Fair probability and market probability may be reversed, or de-vigging was skipped Solution: Run devig on sportsbook odds first, then pass the de-vigged fair_prob to find_edge
Error: find_arbitrage shows no arbitrage even when prices seem low Cause: Prices may sum to more than 1.0 when all outcomes are correctly included Solution: Verify you are using the correct probabilities for all outcomes; check total_implied in the result
Error: Kelly fraction is very high (greater than 0.5) Cause: Edge estimate is very large — often from a miscalculated fair probability Solution: Use half-Kelly or quarter-Kelly for conservative sizing. Re-verify fair probability via devig
Weekly Installs
140
Repository
GitHub Stars
55
First Seen
Feb 26, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
codex140
opencode140
gemini-cli139
github-copilot139
cline138
kimi-cli138
DOCX文件创建、编辑与分析完整指南 - 使用docx-js、Pandoc和Python脚本
52,800 周安装
LobeHub Modal命令式API指南:React模态框实现与最佳实践
544 周安装
Accord Project 智能合同模板生成器 - 自动化创建法律合同与协议模板
558 周安装
GitHub Release自动化发布工具 - 安全检查与版本发布工作流
546 周安装
ess-dev技能:MCP Hub代理技能目录,每周安装量69+,支持多平台开发工具
69 周安装
remind-me 自然语言提醒工具 - 使用 cron 和 Markdown 设置自动化提醒
542 周安装
GitHub PR讲解视频生成工具 - 为拉取请求自动创建代码变更讲解视频
69 周安装