[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KEQJ64GF66DTFPMDKW1SVR":3,"skill-same:01M6KEQJ64GF66DTFPMDKW1SVR":104},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6KEQJ64GF66DTFPMDKW1SVR","tauri-v2",null,"Tauri v2 cross-platform app development with Rust backend. Use when configuring tauri.conf.json, implementing Rust commands (#[tauri::command]), setting up IPC patterns (invoke, emit, channels), configuring permissions\u002Fcapabilities, troubleshooting build issues, or deploying desktop\u002Fmobile apps. Triggers on Tauri, src-tauri, invoke, emit, capabilities.json.","RyouMon\u002FMyReader","RyouMon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42371625?v=4","01M6KEHCYA36Q3V1K41QV59W7E","Wen","01M6KEHD28Z5MHEK56872145JX",2,"MIT","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"tauri v2","tauri","patterns","when","invoke","implementing","conf","apps","app","triggers","platform","commands","rust","mobile","up","command","src","issues","permissions","capabilities","app development","development","2026-11-10 10:12:30",false,"---\nname: ai-ml\ndescription: \"AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.\"\ncategory: workflow-bundle\nrisk: safe\nsource: personal\ndate_added: \"2026-02-27\"\n---\n\n# AI\u002FML Workflow Bundle\n\n## Overview\n\nComprehensive AI\u002FML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building LLM-powered applications\n- Implementing RAG (Retrieval-Augmented Generation)\n- Creating AI agents\n- Developing ML pipelines\n- Adding AI features to applications\n- Setting up AI observability\n\n## Workflow Phases\n\n### Phase 1: AI Application Design\n\n#### Skills to Invoke\n- `ai-product` - AI product development\n- `ai-engineer` - AI engineering\n- `ai-agents-architect` - Agent architecture\n- `llm-app-patterns` - LLM patterns\n\n#### Actions\n1. Define AI use cases\n2. Choose appropriate models\n3. Design system architecture\n4. Plan data flows\n5. Define success metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to design AI-powered features\n```\n\n```\nUse @ai-agents-architect to design multi-agent system\n```\n\n### Phase 2: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - AI assistant development\n- `llm-application-dev-langchain-agent` - LangChain agents\n- `llm-application-dev-prompt-optimize` - Prompt engineering\n- `gemini-api-dev` - Gemini API\n\n#### Actions\n1. Select LLM provider\n2. Set up API access\n3. Implement prompt templates\n4. Configure model parameters\n5. Add streaming support\n6. Implement error handling\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to build conversational AI\n```\n\n```\nUse @llm-application-dev-langchain-agent to create LangChain agents\n```\n\n```\nUse @llm-application-dev-prompt-optimize to optimize prompts\n```\n\n### Phase 3: RAG Implementation\n\n#### Skills to Invoke\n- `rag-engineer` - RAG engineering\n- `rag-implementation` - RAG implementation\n- `embedding-strategies` - Embedding selection\n- `vector-database-engineer` - Vector databases\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Design data pipeline\n2. Choose embedding model\n3. Set up vector database\n4. Implement chunking strategy\n5. Configure retrieval\n6. Add reranking\n7. Implement caching\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to design RAG pipeline\n```\n\n```\nUse @vector-database-engineer to set up vector search\n```\n\n```\nUse @embedding-strategies to select optimal embeddings\n```\n\n### Phase 4: AI Agent Development\n\n#### Skills to Invoke\n- `autonomous-agents` - Autonomous agent patterns\n- `autonomous-agent-patterns` - Agent patterns\n- `crewai` - CrewAI framework\n- `langgraph` - LangGraph\n- `multi-agent-patterns` - Multi-agent systems\n- `computer-use-agents` - Computer use agents\n\n#### Actions\n1. Design agent architecture\n2. Define agent roles\n3. Implement tool integration\n4. Set up memory systems\n5. Configure orchestration\n6. Add human-in-the-loop\n\n#### Copy-Paste Prompts\n```\nUse @crewai to build role-based multi-agent system\n```\n\n```\nUse @langgraph to create stateful AI workflows\n```\n\n```\nUse @autonomous-agents to design autonomous agent\n```\n\n### Phase 5: ML Pipeline Development\n\n#### Skills to Invoke\n- `ml-engineer` - ML engineering\n- `mlops-engineer` - MLOps\n- `machine-learning-ops-ml-pipeline` - ML pipelines\n- `ml-pipeline-workflow` - ML workflows\n- `data-engineer` - Data engineering\n\n#### Actions\n1. Design ML pipeline\n2. Set up data processing\n3. Implement model training\n4. Configure evaluation\n5. Set up model registry\n6. Deploy models\n\n#### Copy-Paste Prompts\n```\nUse @ml-engineer to build machine learning pipeline\n```\n\n```\nUse @mlops-engineer to set up MLOps infrastructure\n```\n\n### Phase 6: AI Observability\n\n#### Skills to Invoke\n- `langfuse` - Langfuse observability\n- `manifest` - Manifest telemetry\n- `evaluation` - AI evaluation\n- `llm-evaluation` - LLM evaluation\n\n#### Actions\n1. Set up tracing\n2. Configure logging\n3. Implement evaluation\n4. Monitor performance\n5. Track costs\n6. Set up alerts\n\n#### Copy-Paste Prompts\n```\nUse @langfuse to set up LLM observability\n```\n\n```\nUse @evaluation to create evaluation framework\n```\n\n### Phase 7: AI Security\n\n#### Skills to Invoke\n- `prompt-engineering` - Prompt security\n- `security-scanning-security-sast` - Security scanning\n\n#### Actions\n1. Implement input validation\n2. Add output filtering\n3. Configure rate limiting\n4. Set up access controls\n5. Monitor for abuse\n6. Implement audit logging\n\n## AI Development Checklist\n\n### LLM Integration\n- [ ] API keys secured\n- [ ] Rate limiting configured\n- [ ] Error handling implemented\n- [ ] Streaming enabled\n- [ ] Token usage tracked\n\n### RAG System\n- [ ] Data pipeline working\n- [ ] Embeddings generated\n- [ ] Vector search optimized\n- [ ] Retrieval accuracy tested\n- [ ] Caching implemented\n\n### AI Agents\n- [ ] Agent roles defined\n- [ ] Tools integrated\n- [ ] Memory working\n- [ ] Orchestration tested\n- [ ] Error handling robust\n\n### Observability\n- [ ] Tracing enabled\n- [ ] Metrics collected\n- [ ] Evaluation running\n- [ ] Alerts configured\n- [ ] Dashboards created\n\n## Quality Gates\n\n- [ ] All AI features tested\n- [ ] Performance benchmarks met\n- [ ] Security measures in place\n- [ ] Observability configured\n- [ ] Documentation complete\n\n## Related Workflow Bundles\n\n- `development` - Application development\n- `database` - Data management\n- `cloud-devops` - Infrastructure\n- `testing-qa` - AI testing\n","# AI\u002FML Workflow Bundle\n\n## Overview\n\nComprehensive AI\u002FML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building LLM-powered applications\n- Implementing RAG (Retrieval-Augmented Generation)\n- Creating AI agents\n- Developing ML pipelines\n- Adding AI features to applications\n- Setting up AI observability\n\n## Workflow Phases\n\n### Phase 1: AI Application Design\n\n#### Skills to Invoke\n- `ai-product` - AI product development\n- `ai-engineer` - AI engineering\n- `ai-agents-architect` - Agent architecture\n- `llm-app-patterns` - LLM patterns\n\n#### Actions\n1. Define AI use cases\n2. Choose appropriate models\n3. Design system architecture\n4. Plan data flows\n5. Define success metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to design AI-powered features\n```\n\n```\nUse @ai-agents-architect to design multi-agent system\n```\n\n### Phase 2: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - AI assistant development\n- `llm-application-dev-langchain-agent` - LangChain agents\n- `llm-application-dev-prompt-optimize` - Prompt engineering\n- `gemini-api-dev` - Gemini API\n\n#### Actions\n1. Select LLM provider\n2. Set up API access\n3. Implement prompt templates\n4. Configure model parameters\n5. Add streaming support\n6. Implement error handling\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to build conversational AI\n```\n\n```\nUse @llm-application-dev-langchain-agent to create LangChain agents\n```\n\n```\nUse @llm-application-dev-prompt-optimize to optimize prompts\n```\n\n### Phase 3: RAG Implementation\n\n#### Skills to Invoke\n- `rag-engineer` - RAG engineering\n- `rag-implementation` - RAG implementation\n- `embedding-strategies` - Embedding selection\n- `vector-database-engineer` - Vector databases\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Design data pipeline\n2. Choose embedding model\n3. Set up vector database\n4. Implement chunking strategy\n5. Configure retrieval\n6. Add reranking\n7. Implement caching\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to design RAG pipeline\n```\n\n```\nUse @vector-database-engineer to set up vector search\n```\n\n```\nUse @embedding-strategies to select optimal embeddings\n```\n\n### Phase 4: AI Agent Development\n\n#### Skills to Invoke\n- `autonomous-agents` - Autonomous agent patterns\n- `autonomous-agent-patterns` - Agent patterns\n- `crewai` - CrewAI framework\n- `langgraph` - LangGraph\n- `multi-agent-patterns` - Multi-agent systems\n- `computer-use-agents` - Computer use agents\n\n#### Actions\n1. Design agent architecture\n2. Define agent roles\n3. Implement tool integration\n4. Set up memory systems\n5. Configure orchestration\n6. Add human-in-the-loop\n\n#### Copy-Paste Prompts\n```\nUse @crewai to build role-based multi-agent system\n```\n\n```\nUse @langgraph to create stateful AI workflows\n```\n\n```\nUse @autonomous-agents to design autonomous agent\n```\n\n### Phase 5: ML Pipeline Development\n\n#### Skills to Invoke\n- `ml-engineer` - ML engineering\n- `mlops-engineer` - MLOps\n- `machine-learning-ops-ml-pipeline` - ML pipelines\n- `ml-pipeline-workflow` - ML workflows\n- `data-engineer` - Data engineering\n\n#### Actions\n1. Design ML pipeline\n2. Set up data processing\n3. Implement model training\n4. Configure evaluation\n5. Set up model registry\n6. Deploy models\n\n#### Copy-Paste Prompts\n```\nUse @ml-engineer to build machine learning pipeline\n```\n\n```\nUse @mlops-engineer to set up MLOps infrastructure\n```\n\n### Phase 6: AI Observability\n\n#### Skills to Invoke\n- `langfuse` - Langfuse observability\n- `manifest` - Manifest telemetry\n- `evaluation` - AI evaluation\n- `llm-evaluation` - LLM evaluation\n\n#### Actions\n1. Set up tracing\n2. Configure logging\n3. Implement evaluation\n4. Monitor performance\n5. Track costs\n6. Set up alerts\n\n#### Copy-Paste Prompts\n```\nUse @langfuse to set up LLM observability\n```\n\n```\nUse @evaluation to create evaluation framework\n```\n\n### Phase 7: AI Security\n\n#### Skills to Invoke\n- `prompt-engineering` - Prompt security\n- `security-scanning-security-sast` - Security scanning\n\n#### Actions\n1. Implement input validation\n2. Add output filtering\n3. Configure rate limiting\n4. Set up access controls\n5. Monitor for abuse\n6. Implement audit logging\n\n## AI Development Checklist\n\n### LLM Integration\n- [ ] API keys secured\n- [ ] Rate limiting configured\n- [ ] Error handling implemented\n- [ ] Streaming enabled\n- [ ] Token usage tracked\n\n### RAG System\n- [ ] Data pipeline working\n- [ ] Embeddings generated\n- [ ] Vector search optimized\n- [ ] Retrieval accuracy tested\n- [ ] Caching implemented\n\n### AI Agents\n- [ ] Agent roles defined\n- [ ] Tools integrated\n- [ ] Memory working\n- [ ] Orchestration tested\n- [ ] Error handling robust\n\n### Observability\n- [ ] Tracing enabled\n- [ ] Metrics collected\n- [ ] Evaluation running\n- [ ] Alerts configured\n- [ ] Dashboards created\n\n## Quality Gates\n\n- [ ] All AI features tested\n- [ ] Performance benchmarks met\n- [ ] Security measures in place\n- [ ] Observability configured\n- [ ] Documentation complete\n\n## Related Workflow Bundles\n\n- `development` - Application development\n- `database` - Data management\n- `cloud-devops` - Infrastructure\n- `testing-qa` - AI testing\n",{"name":46,"risk":47,"source":48,"category":49,"date_added":50,"description":51},"ai-ml","safe","personal","workflow-bundle","2026-02-27","AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.","main","aae5fa43a57641df274d23f55d90e394333c4050",".agents\u002Fskills\u002Ftauri-v2",0,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader","https:\u002F\u002Fryoumon.github.io\u002Fprojects\u002Fmyreader\u002F","https:\u002F\u002Fgithub.com\u002FRyouMon","2026-11-10 02:18:46",{"source":8,"skillName":46,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add RyouMon\u002FMyReader --skill ai-ml",{"tool":66,"command":67},"bunx","bunx skills add RyouMon\u002FMyReader --skill ai-ml",{"tool":69,"command":70},"pnpm","pnpm dlx skills add RyouMon\u002FMyReader --skill ai-ml","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002FSKILL.md",true,[74,80,86,90,94,98],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"assets\u002FREADME.md","ASSET","资产文件",460,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002Fassets\u002FREADME.md",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"README.md","REFERENCE","参考文档",4671,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002FREADME.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fcapabilities-reference.md",6363,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002Freferences\u002Fcapabilities-reference.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fipc-patterns.md",10143,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002Freferences\u002Fipc-patterns.md",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002FREADME.md",349,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002Freferences\u002FREADME.md",{"path":99,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002FREADME.md","SCRIPT","脚本",588,"https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ftauri-v2\u002Fscripts\u002FREADME.md",[105,148,169,191,213,233],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":109,"licenseFileUrl":110,"sourceHtmlUrl":111,"categories":112,"keywords":126,"publishedAt":41},"01M6KEPBH08HMQVRGJQDG02AVP","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[113,118,121],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":119,"categoryName":120,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,22],"frontend","design","frontend design","css","web","components","react","react components","create","code","artifacts","examples","applications","landing","use","ui design","pages","aesthetics","web ui","with","generic",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":41},"01M6KES6YTRMFMAGCCMFW26AN9","cross-platform-ui-spec","Apply the shared MyReader design workflow to desktop or mobile UI changes in Cursor.","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fcross-platform-ui-spec\u002FSKILL.md",[154],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},[156,157,158,29,159,160,128,161,162,163,32,164,165,166,167,168],"spec","cross platform","ui","platform ui","cross platform ui spec","workflow","desktop","cursor","shared","mobile ui","changes","apply","myreader design",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":176,"publishedAt":41},"01M6KES2HFY9HGF5T9GDAGSFRE","zustand-state-management","Zustand v5 store patterns, TypeScript create\u003CT>()() usage, persist\u002Fdevtools middleware, hydration pitfalls, slices, and migration notes. Use when implementing or reviewing global client state with Zustand in React apps.","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fzustand-state-management\u002FSKILL.md",[175],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[177,178,179,180,181,182,183,133,184,135,185,141,21,26,186,187,188,189,190,24,22],"state management","state","zustand","management","zustand state","zustand state management","typescript","notes","devtools","store","middleware","pitfalls","usage","persist",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":41},"01M6KERW3JC43BAGMFNA1FYVQ5","vercel-react-view-transitions","Guide for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fvercel-react-view-transitions\u002FSKILL.md",[197],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[133,199,200,201,202,203,130,132,204,205,135,206,207,208,209,164,210,211,212,24,22],"view","vercel","vercel react view transitions","react view transitions","transitions","component","libraries","api","Animation","back","route","element","types","states",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":220,"publishedAt":41},"01M6KERK6M3QARTY9GGVWH49XQ","vercel-react-native-skills","React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fvercel-react-native-skills\u002FSKILL.md",[219],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[133,221,222,200,223,224,225,132,226,227,141,228,32,229,29,28,230,26,24,231,232,22],"vercel react native skills","react native","react native skills","native","performance","tasks","list","modules","expo","mobile performance","for","animations",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":237,"categories":238,"keywords":240,"publishedAt":41},"01M6KER8AQ7EJB3W68GTRZKHA3","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FRyouMon\u002FMyReader\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[239],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[133,241,242,243,200,244,225,132,134,245,226,136,246,247,248,21,249,143,28,250,22],"vercel react best practices","best practices","react best practices","writing","guidelines","Optimization","data","refactoring","next","data fetching"]