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个字符内清楚地总结了核心功能和用例。",{"category":30,"check":31,"severity":24,"summary":32},"Documentation","Concise Body","SKILL.md 简洁，并将更深入的内容委托给单独的文件，将主正文保持在合理的长度内。",{"category":34,"check":35,"severity":24,"summary":36},"Context","Progressive Disclosure","SKILL.md 概述了流程并适当地引用了外部细节，避免了过多的内联数据。",{"category":34,"check":38,"severity":39,"summary":40},"Forked exploration","not_applicable","此技能不适用于会淹没对话的深度探索或研究，因此“context: fork”不适用。",{"category":42,"check":43,"severity":24,"summary":44},"Practical Utility","Usage examples","为主要功能提供了充足的端到端示例，展示了输入、调用和可观察的结果，并且这些示例看起来是合理的。",{"category":42,"check":46,"severity":24,"summary":47},"Edge cases","该技能处理边缘情况和限制，记录了具有可观察症状和恢复步骤的失败模式。",{"category":49,"check":50,"severity":24,"summary":51},"Code Execution","Tool Fallback","该技能列出了所需的 MCP 并指出它是可选的，并带有备用方案，符合文档最佳实践。",{"category":53,"check":54,"severity":24,"summary":55},"Portability","Stack assumptions","该技能清楚地说明了其堆栈假设，包括运行时环境（通过 npx 的 Node.js）和适用的最低版本，并且声明了依赖项。",{"category":57,"check":58,"severity":24,"summary":59},"Safety","Halt on unexpected state","该技能列出了先决条件并暗示在意外状态下停止，确保了安全的工作流程。",{"category":53,"check":61,"severity":24,"summary":62},"Cross-skill coupling","该技能是独立的，不隐式依赖其他技能，并具有明确的交叉链接以关联相关功能。",{"category":42,"check":64,"severity":24,"summary":65},"Problem relevance","描述清楚地指出了用户在需要 AI 驱动的优化来做出选择和进行 A/B 测试而无需复杂数据仓库方面的痛点。",{"category":42,"check":67,"severity":24,"summary":68},"Unique selling proposition","该扩展通过为 AI 代理提供确定性的数学优化算法（老虎机、LinUCB）来提供独特的卖点，超越了简单的提示，并提供了低延迟、无 token 的计算。",{"category":42,"check":70,"severity":24,"summary":71},"Production readiness","该扩展已准备好投入生产，提供了多种集成方法（MCP 服务器、REST API、SDK），并涵盖了优化任务的完整生命周期。",{"category":73,"check":74,"severity":24,"summary":75},"Scope","Single responsibility principle","该扩展专注于使用老虎机算法的优化任务，遵循单一、明确定义的职责。",{"category":73,"check":77,"severity":24,"summary":78},"Description quality","描述准确、简洁、易读，并准确反映了技能的行为和功能。",{"category":22,"check":80,"severity":24,"summary":81},"Scoped 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Supports 6 distribution types.","oraclaw-simulate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[379],{"path":282,"priority":283},{"basePath":381,"description":382,"displayName":383,"installMethods":384,"rationale":385,"selectedPaths":386,"source":284,"sourceLanguage":285,"type":257},"mission-control/packages/clawhub-skills/oraclaw-solver","Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds.","oraclaw-solver",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-solver/SKILL.md",[387],{"path":282,"priority":283},{"basePath":389,"description":390,"displayName":391,"installMethods":392,"license":248,"rationale":393,"selectedPaths":394,"source":284,"sourceLanguage":285,"type":226},"mission-control/packages/mcp-server","OraClaw Decision Intelligence — 17 MCP tools for AI agents (6 premium API-key tools + 11 free). Full input/output schemas + MCP behavior annotations on every tool. Optimization (bandit/CMA-ES/genetic/LP-MIP), simulation (Monte Carlo/scenarios), prediction (ARIMA/Holt-Winters/Bayesian/ensemble), scoring (convergence/calibration), graph analytics, anomaly detection, pathfinding, scheduling.","@oraclaw/mcp-server",{"npm":391},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[395,397,399,401],{"path":396,"priority":283},"server.json",{"path":398,"priority":283},"package.json",{"path":400,"priority":283},"README.md",{"path":402,"priority":403},"src/index.ts","low",{"sources":405},[406],"manual",{"closedIssues90d":241,"description":408,"forks":242,"homepage":409,"license":248,"openIssues90d":8,"pushedAt":244,"readmeSize":239,"stars":245,"topics":410},"Deterministic decision-intelligence MCP server for AI agents — 17 tools, 21 algorithms (LinUCB, HiGHS LP/MIP, PageRank, Monte Carlo, CMA-ES, conformal). Sub-25ms. Zero LLM cost. AAA on Glama. Field-validated in 12+ OSS projects.","https://web-olive-one-89.vercel.app",[411,412,413,414,415,416,225,226,221,417,418,419,420,421,422,423,424,425,426,427],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","linear-programming","llm-tools","model-context-protocol","monte-carlo","pagerank",{"classifiedAt":429,"discoverAt":430,"extractAt":431,"githubAt":431,"updatedAt":429},1778698837409,1778698831609,1778698835357,[222,224,223,225,226,221],{"evaluatedAt":434,"extractAt":435,"updatedAt":251},1778698869396,1778698837670,[],[438,467,496,523,545,573],{"_creationTime":439,"_id":440,"community":441,"display":442,"identity":448,"providers":452,"relations":460,"tags":463,"workflow":464},1778695720086.7703,"k176r34g5a5fjn1z1a4gq6v88186nje0",{"reviewCount":8},{"description":443,"installMethods":444,"name":446,"sourceUrl":447},"Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.",{"claudeCode":445},"product-on-purpose/pm-skills","measure-experiment-design","https://github.com/product-on-purpose/pm-skills",{"basePath":449,"githubOwner":450,"githubRepo":451,"locale":285,"slug":446,"type":257},"skills/measure-experiment-design","product-on-purpose","pm-skills",{"evaluate":453,"extract":459},{"promptVersionExtension":214,"promptVersionScoring":215,"score":454,"tags":455,"targetMarket":262,"tier":227},100,[222,224,456,457,458],"product-management","a-b-testing","data-analysis",{"commitSha":264},{"parentExtensionId":461,"repoId":462},"k1721116hsfj7zg78w03432n8986n6y8","kd78ksv1wjj826ds5j1sh2kqnx86mhqf",[457,222,458,224,456],{"evaluatedAt":465,"extractAt":466,"updatedAt":465},1778696438706,1778695720086,{"_creationTime":468,"_id":469,"community":470,"display":471,"identity":477,"providers":482,"relations":489,"tags":492,"workflow":493},1778686640222.7952,"k178bs4zybvyebq2gnym4jazch86np03",{"reviewCount":8},{"description":472,"installMethods":473,"name":475,"sourceUrl":476},"Run metric-driven iterative optimization loops -- define a measurable goal, run parallel experiments, measure each against hard gates or LLM-as-judge scores, keep improvements, and converge on the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation.",{"claudeCode":474},"EveryInc/compound-engineering-plugin","CE Optimize","https://github.com/EveryInc/compound-engineering-plugin",{"basePath":478,"githubOwner":479,"githubRepo":480,"locale":285,"slug":481,"type":257},"plugins/compound-engineering/skills/ce-optimize","EveryInc","compound-engineering-plugin","ce-optimize",{"evaluate":483,"extract":488},{"promptVersionExtension":214,"promptVersionScoring":215,"score":454,"tags":484,"targetMarket":262,"tier":227},[221,224,485,486,487],"mlops","code-quality","prompt-engineering",{"commitSha":264,"license":248},{"parentExtensionId":490,"repoId":491},"k17d893df4em0e3pn8f55h1dxn86n09e","kd7e40my1b5g70tg0f60qg85ch86nn08",[486,224,485,221,487],{"evaluatedAt":494,"extractAt":495,"updatedAt":494},1778687141592,1778686640222,{"_creationTime":497,"_id":498,"community":499,"display":500,"identity":506,"providers":510,"relations":516,"tags":519,"workflow":520},1778675056600.2664,"k173ggr45da0wr1c32bva99bvh86nj5a",{"reviewCount":8},{"description":501,"installMethods":502,"name":504,"sourceUrl":505},"Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.",{"claudeCode":503},"alirezarezvani/claude-skills","experiment-designer","https://github.com/alirezarezvani/claude-skills",{"basePath":507,"githubOwner":508,"githubRepo":509,"locale":285,"slug":504,"type":257},"product-team/skills/experiment-designer","alirezarezvani","claude-skills",{"evaluate":511,"extract":515},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":512,"targetMarket":262,"tier":227},[224,222,456,513,458,514],"statistics","hypothesis-testing",{"commitSha":264},{"parentExtensionId":517,"repoId":518},"k17104ysr0smp7vvp26mnn1fzh86nynm","kd7ff9s1w43mfyy1n7hf87816186m6px",[222,458,224,514,456,513],{"evaluatedAt":521,"extractAt":522,"updatedAt":521},1778685452060,1778675056600,{"_creationTime":524,"_id":525,"community":526,"display":527,"identity":531,"providers":533,"relations":540,"tags":542,"workflow":543},1778675056600.2542,"k17d6x2qxdeyeegtqjtkz3myqx86mq6m",{"reviewCount":8},{"description":528,"installMethods":529,"name":530,"sourceUrl":505},"When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions \"A/B test,\" \"split test,\" \"experiment,\" \"test this change,\" \"variant copy,\" \"multivariate test,\" \"hypothesis,\" \"conversion experiment,\" \"statistical significance,\" or \"test this.\" For tracking implementation, see analytics-tracking.",{"claudeCode":503},"ab-test-setup",{"basePath":532,"githubOwner":508,"githubRepo":509,"locale":285,"slug":530,"type":257},"marketing-skill/skills/ab-test-setup",{"evaluate":534,"extract":539},{"promptVersionExtension":214,"promptVersionScoring":215,"score":535,"tags":536,"targetMarket":262,"tier":227},98,[222,224,537,538,458],"conversion-optimization","marketing",{"commitSha":264},{"parentExtensionId":541,"repoId":518},"k170sws65f0ebecn36z3q8c2z186m477",[222,537,458,224,538],{"evaluatedAt":544,"extractAt":522,"updatedAt":544},1778684020423,{"_creationTime":546,"_id":547,"community":548,"display":549,"identity":555,"providers":559,"relations":566,"tags":569,"workflow":570},1778695548458.3943,"k170qwsw3sjhsnm2r2nyz0j7jd86n1s7",{"reviewCount":8},{"description":550,"installMethods":551,"name":553,"sourceUrl":554},"Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.\n",{"claudeCode":552},"pjt222/agent-almanac","run-ab-test-models","https://github.com/pjt222/agent-almanac",{"basePath":556,"githubOwner":557,"githubRepo":558,"locale":285,"slug":553,"type":257},"skills/run-ab-test-models","pjt222","agent-almanac",{"evaluate":560,"extract":565},{"promptVersionExtension":214,"promptVersionScoring":215,"score":561,"tags":562,"targetMarket":262,"tier":227},95,[485,222,563,564,224],"canary-deployment","statistical-testing",{"commitSha":264},{"parentExtensionId":567,"repoId":568},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[222,563,224,485,564],{"evaluatedAt":571,"extractAt":572,"updatedAt":571},1778700953339,1778695548458,{"_creationTime":574,"_id":575,"community":576,"display":577,"identity":583,"providers":587,"relations":595,"tags":597,"workflow":598},1778695660274.5056,"k17erc36epeh2pmf9zgrfhe19s86ne9f",{"reviewCount":8},{"description":578,"installMethods":579,"name":581,"sourceUrl":582},"Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics.\nTRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create.\nDO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.",{"claudeCode":580},"PostHog/posthog","creating-experiments","https://github.com/PostHog/posthog",{"basePath":584,"githubOwner":585,"githubRepo":586,"locale":285,"slug":581,"type":257},"products/experiments/skills/creating-experiments","PostHog","posthog",{"evaluate":588,"extract":594},{"promptVersionExtension":214,"promptVersionScoring":215,"score":589,"tags":590,"targetMarket":262,"tier":593},79,[591,222,592,586,223],"experiments","product-analytics","community",{"commitSha":264},{"repoId":596},"kd7f22zf7qb3eschtk9s2qdv4586mfts",[222,591,223,586,592],{"evaluatedAt":599,"extractAt":600,"updatedAt":599},1778697075518,1778695660274]