[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-BrainBlend-AI-create-atomic-schema-en":3,"guides-for-BrainBlend-AI-create-atomic-schema":515,"similar-k170nd3h8zeayffrxe6pscxyy986m7em-en":516},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":250,"isFallback":245,"parentExtension":255,"providers":312,"relations":316,"repo":317,"tags":512,"workflow":513},1778683220959.3254,"k170nd3h8zeayffrxe6pscxyy986m7em",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Design and write a `BaseIOSchema` input/output pair for an Atomic Agents agent or tool — docstrings, field descriptions, validators, error variants. 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Use when the user asks to \"create an agent\", \"add another agent\", \"build an `AtomicAgent`\", \"wire up an agent\", \"make a planner/router/extractor agent\", or runs `/atomic-agents:create-atomic-agent`.","create-atomic-agent",{"claudeCode":12},"SKILL.md frontmatter at claude-plugin/atomic-agents/skills/create-atomic-agent/SKILL.md",[377],{"path":369,"priority":329},{"basePath":379,"description":380,"displayName":381,"installMethods":382,"rationale":383,"selectedPaths":384,"source":335,"sourceLanguage":18,"type":254},"claude-plugin/atomic-agents/skills/create-atomic-context-provider","Build a `BaseDynamicContextProvider` that injects a named, titled block into an agent's system prompt at every `run()` — current time, user identity, retrieved RAG docs, session state, cached DB schema. Use when the user asks to \"add a context provider\", \"inject X into the prompt\", \"give the agent dynamic context\", \"wire up RAG\", \"make a `BaseDynamicContextProvider`\", or runs `/atomic-agents:create-atomic-context-provider`.","create-atomic-context-provider",{"claudeCode":12},"SKILL.md frontmatter at claude-plugin/atomic-agents/skills/create-atomic-context-provider/SKILL.md",[385],{"path":369,"priority":329},{"basePath":251,"description":10,"displayName":253,"installMethods":387,"rationale":388,"selectedPaths":389,"source":335,"sourceLanguage":18,"type":254},{"claudeCode":12},"SKILL.md frontmatter at claude-plugin/atomic-agents/skills/create-atomic-schema/SKILL.md",[390],{"path":369,"priority":329},{"basePath":392,"description":393,"displayName":394,"installMethods":395,"rationale":396,"selectedPaths":397,"source":335,"sourceLanguage":18,"type":254},"claude-plugin/atomic-agents/skills/create-atomic-tool","Build a `BaseTool[InSchema, OutSchema]` subclass — input/output schemas, `BaseToolConfig`, `run()` (and optional `run_async()`), env-driven secrets, typed failure outputs. 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battle-tested structured output library",{"claudeCode":544},"Orchestra-Research/AI-Research-SKILLs","Instructor","https://github.com/Orchestra-Research/AI-Research-SKILLs",{"basePath":548,"githubOwner":549,"githubRepo":550,"locale":18,"slug":217,"type":254},"16-prompt-engineering/instructor","Orchestra-Research","AI-Research-SKILLs",{"evaluate":552,"extract":561},{"promptVersionExtension":208,"promptVersionScoring":209,"score":212,"tags":553,"targetMarket":220,"tier":221},[554,217,555,216,556,557,558,559,560],"prompt-engineering","structured-output","data-extraction","json-parsing","type-safety","validation","streaming",{"commitSha":285,"license":246},{"parentExtensionId":563,"repoId":564},"k17155ws9qc0hw7a568bg79sfd86max8","kd70hj1y80mhra5xm5g188j5n586mg18",[556,217,557,554,216,560,555,558,559],{"evaluatedAt":567,"extractAt":568,"updatedAt":567},1778697010260,1778695116697,{"_creationTime":570,"_id":571,"community":572,"display":573,"identity":577,"providers":582,"relations":588,"tags":590,"workflow":591},1778685991755.7273,"k175b8b0kpk0gsz87sd34639z586nn67",{"reviewCount":8},{"description":542,"installMethods":574,"name":217,"sourceUrl":576},{"claudeCode":575},"davila7/claude-code-templates","https://github.com/davila7/claude-code-templates",{"basePath":578,"githubOwner":579,"githubRepo":580,"locale":18,"slug":581,"type":254},"cli-tool/components/skills/ai-research/prompt-engineering-instructor","davila7","claude-code-templates","prompt-engineering-instructor",{"evaluate":583,"extract":587},{"promptVersionExtension":208,"promptVersionScoring":209,"score":584,"tags":585,"targetMarket":220,"tier":586},75,[554,217,555,216,556,282],"community",{"commitSha":285},{"repoId":589},"kd71fzn4s7r0269fkw47wt670n86ndz0",[556,217,282,554,216,555],{"evaluatedAt":592,"extractAt":593,"updatedAt":592},1778688700350,1778685991755,{"_creationTime":595,"_id":596,"community":597,"display":598,"identity":604,"providers":608,"relations":618,"tags":621,"workflow":622},1778699018122.8064,"k178yxvt3g9djb8ph907q3tv1186n8ex",{"reviewCount":8},{"description":599,"installMethods":600,"name":602,"sourceUrl":603},"Select 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":601},"wshobson/agents","embedding-strategies","https://github.com/wshobson/agents",{"basePath":605,"githubOwner":606,"githubRepo":607,"locale":18,"slug":602,"type":254},"plugins/llm-application-dev/skills/embedding-strategies","wshobson","agents",{"evaluate":609,"extract":617},{"promptVersionExtension":208,"promptVersionScoring":209,"score":610,"tags":611,"targetMarket":220,"tier":221},100,[612,613,614,615,616,219],"embeddings","rag","semantic-search","vector-databases","llm-applications",{"commitSha":285},{"parentExtensionId":619,"repoId":620},"k1719fyk9jrke6aq23wbyf8ej586n3af","kd74de64zj0axtg5b8t7eqqe2x86nske",[612,616,219,613,614,615],{"evaluatedAt":623,"extractAt":624,"updatedAt":623},1778701750946,1778699018122,{"_creationTime":626,"_id":627,"community":628,"display":629,"identity":635,"providers":639,"relations":649,"tags":652,"workflow":653},1778699647844.0183,"k174bzyyax9v1t5bm0m98bfqyh86m8v8",{"reviewCount":8},{"description":630,"installMethods":631,"name":633,"sourceUrl":634},"AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.",{"claudeCode":632},"zxkane/aws-skills","aws-cdk-development","https://github.com/zxkane/aws-skills",{"basePath":636,"githubOwner":637,"githubRepo":638,"locale":18,"slug":633,"type":254},"plugins/aws-cdk/skills/aws-cdk-development","zxkane","aws-skills",{"evaluate":640,"extract":648},{"promptVersionExtension":208,"promptVersionScoring":209,"score":610,"tags":641,"targetMarket":220,"tier":221},[642,643,644,219,645,646,647],"aws","cdk","typescript","iac","cloudformation","infrastructure",{"commitSha":285},{"parentExtensionId":650,"repoId":651},"k177paz2fgaa1r1kfhgb2esr1n86my7m","kd7708aervxaq6vqq9tdf93s2586mcqy",[642,643,646,645,647,219,644],{"evaluatedAt":654,"extractAt":655,"updatedAt":656},1778699774404,1778699647844,1778699908774,{"_creationTime":658,"_id":659,"community":660,"display":661,"identity":667,"providers":671,"relations":679,"tags":682,"workflow":683},1778695548458.3613,"k17dx6tyy2yb3z5pp1vgmg46ad86nm18",{"reviewCount":8},{"description":662,"installMethods":663,"name":665,"sourceUrl":666},"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":664},"pjt222/agent-almanac","fit-drift-diffusion-model","https://github.com/pjt222/agent-almanac",{"basePath":668,"githubOwner":669,"githubRepo":670,"locale":18,"slug":665,"type":254},"skills/fit-drift-diffusion-model","pjt222","agent-almanac",{"evaluate":672,"extract":678},{"promptVersionExtension":208,"promptVersionScoring":209,"score":610,"tags":673,"targetMarket":220,"tier":221},[674,675,676,219,677],"cognitive-science","modeling","statistics","data-analysis",{"commitSha":285},{"parentExtensionId":680,"repoId":681},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[674,677,675,219,676],{"evaluatedAt":684,"extractAt":685,"updatedAt":684},1778698191612,1778695548458]