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Field-validated in 12+ OSS projects.","https://web-olive-one-89.vercel.app",[401,402,403,404,405,406,407,393,408,409,410,411,412,413,414,415,416,417,211,418],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","machine-learning","optimization","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","linear-programming","llm-tools","model-context-protocol","pagerank",{"classifiedAt":420,"discoverAt":421,"extractAt":422,"githubAt":422,"updatedAt":420},1778698837409,1778698831609,1778698835357,[216,214,211,215,213,212],{"evaluatedAt":425,"extractAt":426,"updatedAt":241},1778699039921,1778698837670,[],[429,459,489,519,542,572],{"_creationTime":430,"_id":431,"community":432,"display":433,"identity":439,"providers":443,"relations":453,"tags":455,"workflow":456},1778688112811.7527,"k17enr6rktmxh0enswrmze6et186mq12",{"reviewCount":8},{"description":434,"installMethods":435,"name":437,"sourceUrl":438},"Model best-case, worst-case, and likely revenue scenarios with sensitivity analysis for strategic planning. Use when: building financial forecasts; presenting board scenarios; planning headcount around revenue uncertainty; modeling pricing changes impact; preparing investor updates with upside/downside ranges",{"claudeCode":436},"guia-matthieu/clawfu-skills","forecast-scenarios","https://github.com/guia-matthieu/clawfu-skills",{"basePath":440,"githubOwner":441,"githubRepo":442,"locale":274,"slug":437,"type":246},"skills/revops/forecast-scenarios","guia-matthieu","clawfu-skills",{"evaluate":444,"extract":452},{"promptVersionExtension":204,"promptVersionScoring":205,"score":445,"tags":446,"targetMarket":251,"tier":217},100,[216,214,447,448,449,450,451],"revenue","planning","strategy","sensitivity-analysis","mckinsey",{"commitSha":253},{"repoId":454},"kd72qvzyvm658ya7pbyh5ey47h86md53",[216,214,451,448,447,450,449],{"evaluatedAt":457,"extractAt":458,"updatedAt":457},1778690475880,1778688112811,{"_creationTime":460,"_id":461,"community":462,"display":463,"identity":469,"providers":474,"relations":482,"tags":485,"workflow":486},1778675056600.2537,"k17ask0fam6yfypdvf5562p15986m925",{"reviewCount":8},{"description":464,"installMethods":465,"name":467,"sourceUrl":468},"Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.",{"claudeCode":466},"alirezarezvani/claude-skills","Financial Analyst","https://github.com/alirezarezvani/claude-skills",{"basePath":470,"githubOwner":471,"githubRepo":472,"locale":274,"slug":473,"type":246},"finance/skills/financial-analyst","alirezarezvani","claude-skills","financial-analyst",{"evaluate":475,"extract":481},{"promptVersionExtension":204,"promptVersionScoring":205,"score":445,"tags":476,"targetMarket":251,"tier":217},[216,477,478,214,479,480],"analysis","valuation","budgeting","python",{"commitSha":253,"license":238},{"parentExtensionId":483,"repoId":484},"k174nmf7jahgcsdnzenmdxfcbh86m85y","kd7ff9s1w43mfyy1n7hf87816186m6px",[477,479,216,214,480,478],{"evaluatedAt":487,"extractAt":488,"updatedAt":487},1778683964036,1778675056600,{"_creationTime":490,"_id":491,"community":492,"display":493,"identity":499,"providers":503,"relations":512,"tags":515,"workflow":516},1778695548458.402,"k179k5vddwcqrrr23r6hfavx5n86mf81",{"reviewCount":8},{"description":494,"installMethods":495,"name":497,"sourceUrl":498},"Simulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.\n",{"claudeCode":496},"pjt222/agent-almanac","simulate-stochastic-process","https://github.com/pjt222/agent-almanac",{"basePath":500,"githubOwner":501,"githubRepo":502,"locale":274,"slug":497,"type":246},"skills/simulate-stochastic-process","pjt222","agent-almanac",{"evaluate":504,"extract":511},{"promptVersionExtension":204,"promptVersionScoring":205,"score":505,"tags":506,"targetMarket":251,"tier":217},97,[507,212,508,211,509,510],"stochastic-processes","mcmc","statistics","numerical-methods",{"commitSha":253},{"parentExtensionId":513,"repoId":514},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[508,211,510,212,509,507],{"evaluatedAt":517,"extractAt":518,"updatedAt":517},1778701677011,1778695548458,{"_creationTime":520,"_id":521,"community":522,"display":523,"identity":527,"providers":530,"relations":538,"tags":539,"workflow":540},1778695548458.3777,"k1720hbr4h69f4xs06yz2j8cd586mfam",{"reviewCount":8},{"description":524,"installMethods":525,"name":526,"sourceUrl":498},"Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.\n",{"claudeCode":496},"Model Markov Chain",{"basePath":528,"githubOwner":501,"githubRepo":502,"locale":274,"slug":529,"type":246},"skills/model-markov-chain","model-markov-chain",{"evaluate":531,"extract":537},{"promptVersionExtension":204,"promptVersionScoring":205,"score":505,"tags":532,"targetMarket":251,"tier":217},[533,534,535,536,215,212],"stochastic","markov-chain","transition-matrix","stationary-distribution",{"commitSha":253,"license":238},{"parentExtensionId":513,"repoId":514},[534,215,212,536,533,535],{"evaluatedAt":541,"extractAt":518,"updatedAt":541},1778699521772,{"_creationTime":543,"_id":544,"community":545,"display":546,"identity":552,"providers":557,"relations":565,"tags":568,"workflow":569},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":547,"installMethods":548,"name":550,"sourceUrl":551},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":549},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":553,"githubOwner":554,"githubRepo":555,"locale":274,"slug":556,"type":246},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":558,"extract":564},{"promptVersionExtension":204,"promptVersionScoring":205,"score":445,"tags":559,"targetMarket":251,"tier":217},[216,560,561,562,409,563],"trading","market-analysis","ai","cli",{"commitSha":253,"license":238},{"parentExtensionId":566,"repoId":567},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[562,563,216,561,560,409],{"evaluatedAt":570,"extractAt":571,"updatedAt":570},1778701108877,1778696691708,{"_creationTime":573,"_id":574,"community":575,"display":576,"identity":582,"providers":586,"relations":591,"tags":595,"workflow":596},1778693819389.531,"k174n8dznk7k8dr9drb7fwx01586nm5t",{"reviewCount":8},{"description":577,"installMethods":578,"name":580,"sourceUrl":581},"AI交易记忆的领域知识 — 结果加权记忆 (OWM) 架构、5种记忆类型、回忆评分和行为分析。用于记录交易、回忆相似的上下文、分析性能或检查行为漂移。在 \"record trade\"、\"remember trade\"、\"recall\"、\"similar trades\"、\"performance\"、\"behavioral\"、\"disposition\"、\"affective state\"、\"confidence\" 时触发。",{"claudeCode":579},"mnemox-ai/tradememory-protocol","trading-memory","https://github.com/mnemox-ai/tradememory-protocol",{"basePath":583,"githubOwner":584,"githubRepo":585,"locale":18,"slug":580,"type":246},"tradememory-plugin/skills/trading-memory","mnemox-ai","tradememory-protocol",{"evaluate":587,"extract":590},{"promptVersionExtension":204,"promptVersionScoring":205,"score":445,"tags":588,"targetMarket":251,"tier":217},[560,562,589,216,480],"memory",{"commitSha":253},{"parentExtensionId":592,"repoId":593,"translatedFrom":594},"k170vxkqee48k2xq1v55a025nh86nzn7","kd73z11kfekksxyrs8ds0snacs86ncdy","k173a67a16bpq0e29wjd85v71986nx03",[562,216,589,480,560],{"evaluatedAt":597,"extractAt":598,"updatedAt":599},1778693719816,1778693539593,1778693819389]