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and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.",{"claudeCode":606},"wshobson/agents","embedding-strategies","https://github.com/wshobson/agents",{"basePath":610,"githubOwner":611,"githubRepo":612,"locale":263,"slug":607,"type":253},"plugins/llm-application-dev/skills/embedding-strategies","wshobson","agents",{"evaluate":614,"extract":622},{"promptVersionExtension":208,"promptVersionScoring":209,"score":615,"tags":616,"targetMarket":284,"tier":220},100,[617,618,619,620,621,219],"embeddings","rag","semantic-search","vector-databases","llm-applications",{"commitSha":286},{"parentExtensionId":624,"repoId":625},"k1719fyk9jrke6aq23wbyf8ej586n3af","kd74de64zj0axtg5b8t7eqqe2x86nske",[617,621,219,618,619,620],{"evaluatedAt":628,"extractAt":629,"updatedAt":628},1778701750946,1778699018122,{"_creationTime":631,"_id":632,"community":633,"display":634,"identity":640,"providers":644,"relations":654,"tags":658,"workflow":659},1778699887554.4724,"k171fhjffw07a0a511s1v1hqsh86m9gz",{"reviewCount":8},{"description":635,"installMethods":636,"name":638,"sourceUrl":639},"AWS Cloud Development Kit (CDK) 专家，用于使用 TypeScript/Python 构建云基础设施。在创建 CDK 堆栈、定义 CDK 构造、实现基础设施即代码，或当用户提及 CDK、CloudFormation、IaC、cdk synth、cdk deploy，或希望以编程方式定义 AWS 基础设施时使用。涵盖 CDK 应用结构、构造模式、堆栈组合和部署工作流。",{"claudeCode":637},"zxkane/aws-skills","aws-cdk-development","https://github.com/zxkane/aws-skills",{"basePath":641,"githubOwner":642,"githubRepo":643,"locale":18,"slug":638,"type":253},"plugins/aws-cdk/skills/aws-cdk-development","zxkane","aws-skills",{"evaluate":645,"extract":653},{"promptVersionExtension":208,"promptVersionScoring":209,"score":615,"tags":646,"targetMarket":284,"tier":220},[647,648,649,219,650,651,652],"aws","cdk","typescript","iac","cloudformation","infrastructure",{"commitSha":286},{"parentExtensionId":655,"repoId":656,"translatedFrom":657},"k177paz2fgaa1r1kfhgb2esr1n86my7m","kd7708aervxaq6vqq9tdf93s2586mcqy","k174bzyyax9v1t5bm0m98bfqyh86m8v8",[647,648,651,650,652,219,649],{"evaluatedAt":660,"extractAt":661,"updatedAt":662},1778699774404,1778699647844,1778699887554,{"_creationTime":664,"_id":665,"community":666,"display":667,"identity":673,"providers":677,"relations":685,"tags":688,"workflow":689},1778695548458.3613,"k17dx6tyy2yb3z5pp1vgmg46ad86nm18",{"reviewCount":8},{"description":668,"installMethods":669,"name":671,"sourceUrl":672},"Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.\n",{"claudeCode":670},"pjt222/agent-almanac","fit-drift-diffusion-model","https://github.com/pjt222/agent-almanac",{"basePath":674,"githubOwner":675,"githubRepo":676,"locale":263,"slug":671,"type":253},"skills/fit-drift-diffusion-model","pjt222","agent-almanac",{"evaluate":678,"extract":684},{"promptVersionExtension":208,"promptVersionScoring":209,"score":615,"tags":679,"targetMarket":284,"tier":220},[680,681,682,219,683],"cognitive-science","modeling","statistics","data-analysis",{"commitSha":286},{"parentExtensionId":686,"repoId":687},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[680,683,681,219,682],{"evaluatedAt":690,"extractAt":691,"updatedAt":690},1778698191612,1778695548458]