[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71B7HFTR8TJ2CVCA3SDFMDM":3,"skill-same:01M71B7HFTR8TJ2CVCA3SDFMDM":83},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M71B7HFTR8TJ2CVCA3SDFMDM","azure-ai-projects-ts",null,"Build AI applications using Azure AI Projects SDK for JavaScript (@azure\u002Fai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.","strmt7\u002Fai_skills_collection_benchmarked","strmt7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F28989754?v=4","01M71929P914SWKQ7QJV24NBBY","Stratos","01M71929Q9VGKQ7PWYR95VSQQQ",1,"MIT","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fcloud-azure-microsoft-sdks\u002Fofficial-vendor-reference\u002Fazure-ai-projects-ts\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"azure ai","azure","azure ai projects ts","azure ai projects","projects","deployments","when","for","with","clients","project","connections","build","indexes","datasets","sdk","use","applications","microsoft","javascript","2026-11-10 10:12:30",false,"---\nname: azure-ai-projects-ts\ndescription: Build AI applications using Azure AI Projects SDK for JavaScript (@azure\u002Fai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: '@azure\u002Fai-projects'\n---\n\n# Azure AI Projects SDK for TypeScript\n\nHigh-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.\n\n## Installation\n\n```bash\nnpm install @azure\u002Fai-projects @azure\u002Fidentity\n```\n\nFor tracing:\n```bash\nnpm install @azure\u002Fmonitor-opentelemetry @opentelemetry\u002Fapi\n```\n\n## Environment Variables\n\n```bash\nAZURE_AI_PROJECT_ENDPOINT=https:\u002F\u002F\u003Cresource>.services.ai.azure.com\u002Fapi\u002Fprojects\u002F\u003Cproject>\nMODEL_DEPLOYMENT_NAME=gpt-4o\n```\n\n## Authentication\n\n```typescript\nimport { AIProjectClient } from \"@azure\u002Fai-projects\";\nimport { DefaultAzureCredential } from \"@azure\u002Fidentity\";\n\nconst client = new AIProjectClient(\n  process.env.AZURE_AI_PROJECT_ENDPOINT!,\n  new DefaultAzureCredential()\n);\n```\n\n## Operation Groups\n\n| Group | Purpose |\n|-------|---------|\n| `client.agents` | Create and manage AI agents |\n| `client.connections` | List connected Azure resources |\n| `client.deployments` | List model deployments |\n| `client.datasets` | Upload and manage datasets |\n| `client.indexes` | Create and manage search indexes |\n| `client.evaluators` | Manage evaluation metrics |\n| `client.memoryStores` | Manage agent memory |\n\n## Getting OpenAI Client\n\n```typescript\nconst openAIClient = await client.getOpenAIClient();\n\n\u002F\u002F Use for responses\nconst response = await openAIClient.responses.create({\n  model: \"gpt-4o\",\n  input: \"What is the capital of France?\"\n});\n\n\u002F\u002F Use for conversations\nconst conversation = await openAIClient.conversations.create({\n  items: [{ type: \"message\", role: \"user\", content: \"Hello!\" }]\n});\n```\n\n## Agents\n\n### Create Agent\n\n```typescript\nconst agent = await client.agents.createVersion(\"my-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  instructions: \"You are a helpful assistant.\"\n});\n```\n\n### Agent with Tools\n\n```typescript\n\u002F\u002F Code Interpreter\nconst agent = await client.agents.createVersion(\"code-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  instructions: \"You can execute code.\",\n  tools: [{ type: \"code_interpreter\", container: { type: \"auto\" } }]\n});\n\n\u002F\u002F File Search\nconst agent = await client.agents.createVersion(\"search-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{ type: \"file_search\", vector_store_ids: [vectorStoreId] }]\n});\n\n\u002F\u002F Web Search\nconst agent = await client.agents.createVersion(\"web-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"web_search_preview\",\n    user_location: { type: \"approximate\", country: \"US\", city: \"Seattle\" }\n  }]\n});\n\n\u002F\u002F Azure AI Search\nconst agent = await client.agents.createVersion(\"aisearch-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"azure_ai_search\",\n    azure_ai_search: {\n      indexes: [{\n        project_connection_id: connectionId,\n        index_name: \"my-index\",\n        query_type: \"simple\"\n      }]\n    }\n  }]\n});\n\n\u002F\u002F Function Tool\nconst agent = await client.agents.createVersion(\"func-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"function\",\n    function: {\n      name: \"get_weather\",\n      description: \"Get weather for a location\",\n      strict: true,\n      parameters: {\n        type: \"object\",\n        properties: { location: { type: \"string\" } },\n        required: [\"location\"]\n      }\n    }\n  }]\n});\n\n\u002F\u002F MCP Tool\nconst agent = await client.agents.createVersion(\"mcp-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"mcp\",\n    server_label: \"my-mcp\",\n    server_url: \"https:\u002F\u002Fmcp-server.example.com\",\n    require_approval: \"always\"\n  }]\n});\n```\n\n### Run Agent\n\n```typescript\nconst openAIClient = await client.getOpenAIClient();\n\n\u002F\u002F Create conversation\nconst conversation = await openAIClient.conversations.create({\n  items: [{ type: \"message\", role: \"user\", content: \"Hello!\" }]\n});\n\n\u002F\u002F Generate response using agent\nconst response = await openAIClient.responses.create(\n  { conversation: conversation.id },\n  { body: { agent: { name: agent.name, type: \"agent_reference\" } } }\n);\n\n\u002F\u002F Cleanup\nawait openAIClient.conversations.delete(conversation.id);\nawait client.agents.deleteVersion(agent.name, agent.version);\n```\n\n## Connections\n\n```typescript\n\u002F\u002F List all connections\nfor await (const conn of client.connections.list()) {\n  console.log(conn.name, conn.type);\n}\n\n\u002F\u002F Get connection by name\nconst conn = await client.connections.get(\"my-connection\");\n\n\u002F\u002F Get connection with credentials\nconst connWithCreds = await client.connections.getWithCredentials(\"my-connection\");\n\n\u002F\u002F Get default connection by type\nconst defaultAzureOpenAI = await client.connections.getDefault(\"AzureOpenAI\", true);\n```\n\n## Deployments\n\n```typescript\n\u002F\u002F List all deployments\nfor await (const deployment of client.deployments.list()) {\n  if (deployment.type === \"ModelDeployment\") {\n    console.log(deployment.name, deployment.modelName);\n  }\n}\n\n\u002F\u002F Filter by publisher\nfor await (const d of client.deployments.list({ modelPublisher: \"OpenAI\" })) {\n  console.log(d.name);\n}\n\n\u002F\u002F Get specific deployment\nconst deployment = await client.deployments.get(\"gpt-4o\");\n```\n\n## Datasets\n\n```typescript\n\u002F\u002F Upload single file\nconst dataset = await client.datasets.uploadFile(\n  \"my-dataset\",\n  \"1.0\",\n  \".\u002Fdata\u002Ftraining.jsonl\"\n);\n\n\u002F\u002F Upload folder\nconst dataset = await client.datasets.uploadFolder(\n  \"my-dataset\",\n  \"2.0\",\n  \".\u002Fdata\u002Fdocuments\u002F\"\n);\n\n\u002F\u002F Get dataset\nconst ds = await client.datasets.get(\"my-dataset\", \"1.0\");\n\n\u002F\u002F List versions\nfor await (const version of client.datasets.listVersions(\"my-dataset\")) {\n  console.log(version);\n}\n\n\u002F\u002F Delete\nawait client.datasets.delete(\"my-dataset\", \"1.0\");\n```\n\n## Indexes\n\n```typescript\nimport { AzureAISearchIndex } from \"@azure\u002Fai-projects\";\n\nconst indexConfig: AzureAISearchIndex = {\n  name: \"my-index\",\n  type: \"AzureSearch\",\n  version: \"1\",\n  indexName: \"my-index\",\n  connectionName: \"search-connection\"\n};\n\n\u002F\u002F Create index\nconst index = await client.indexes.createOrUpdate(\"my-index\", \"1\", indexConfig);\n\n\u002F\u002F List indexes\nfor await (const idx of client.indexes.list()) {\n  console.log(idx.name);\n}\n\n\u002F\u002F Delete\nawait client.indexes.delete(\"my-index\", \"1\");\n```\n\n## Key Types\n\n```typescript\nimport {\n  AIProjectClient,\n  AIProjectClientOptionalParams,\n  Connection,\n  ModelDeployment,\n  DatasetVersionUnion,\n  AzureAISearchIndex\n} from \"@azure\u002Fai-projects\";\n```\n\n## Best Practices\n\n1. **Use getOpenAIClient()** - For responses, conversations, files, and vector stores\n2. **Version your agents** - Use `createVersion` for reproducible agent definitions\n3. **Clean up resources** - Delete agents, conversations when done\n4. **Use connections** - Get credentials from project connections, don't hardcode\n5. **Filter deployments** - Use `modelPublisher` filter to find specific models\n","# Azure AI Projects SDK for TypeScript\n\nHigh-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.\n\n## Installation\n\n```bash\nnpm install @azure\u002Fai-projects @azure\u002Fidentity\n```\n\nFor tracing:\n```bash\nnpm install @azure\u002Fmonitor-opentelemetry @opentelemetry\u002Fapi\n```\n\n## Environment Variables\n\n```bash\nAZURE_AI_PROJECT_ENDPOINT=https:\u002F\u002F\u003Cresource>.services.ai.azure.com\u002Fapi\u002Fprojects\u002F\u003Cproject>\nMODEL_DEPLOYMENT_NAME=gpt-4o\n```\n\n## Authentication\n\n```typescript\nimport { AIProjectClient } from \"@azure\u002Fai-projects\";\nimport { DefaultAzureCredential } from \"@azure\u002Fidentity\";\n\nconst client = new AIProjectClient(\n  process.env.AZURE_AI_PROJECT_ENDPOINT!,\n  new DefaultAzureCredential()\n);\n```\n\n## Operation Groups\n\n| Group | Purpose |\n|-------|---------|\n| `client.agents` | Create and manage AI agents |\n| `client.connections` | List connected Azure resources |\n| `client.deployments` | List model deployments |\n| `client.datasets` | Upload and manage datasets |\n| `client.indexes` | Create and manage search indexes |\n| `client.evaluators` | Manage evaluation metrics |\n| `client.memoryStores` | Manage agent memory |\n\n## Getting OpenAI Client\n\n```typescript\nconst openAIClient = await client.getOpenAIClient();\n\n\u002F\u002F Use for responses\nconst response = await openAIClient.responses.create({\n  model: \"gpt-4o\",\n  input: \"What is the capital of France?\"\n});\n\n\u002F\u002F Use for conversations\nconst conversation = await openAIClient.conversations.create({\n  items: [{ type: \"message\", role: \"user\", content: \"Hello!\" }]\n});\n```\n\n## Agents\n\n### Create Agent\n\n```typescript\nconst agent = await client.agents.createVersion(\"my-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  instructions: \"You are a helpful assistant.\"\n});\n```\n\n### Agent with Tools\n\n```typescript\n\u002F\u002F Code Interpreter\nconst agent = await client.agents.createVersion(\"code-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  instructions: \"You can execute code.\",\n  tools: [{ type: \"code_interpreter\", container: { type: \"auto\" } }]\n});\n\n\u002F\u002F File Search\nconst agent = await client.agents.createVersion(\"search-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{ type: \"file_search\", vector_store_ids: [vectorStoreId] }]\n});\n\n\u002F\u002F Web Search\nconst agent = await client.agents.createVersion(\"web-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"web_search_preview\",\n    user_location: { type: \"approximate\", country: \"US\", city: \"Seattle\" }\n  }]\n});\n\n\u002F\u002F Azure AI Search\nconst agent = await client.agents.createVersion(\"aisearch-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"azure_ai_search\",\n    azure_ai_search: {\n      indexes: [{\n        project_connection_id: connectionId,\n        index_name: \"my-index\",\n        query_type: \"simple\"\n      }]\n    }\n  }]\n});\n\n\u002F\u002F Function Tool\nconst agent = await client.agents.createVersion(\"func-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"function\",\n    function: {\n      name: \"get_weather\",\n      description: \"Get weather for a location\",\n      strict: true,\n      parameters: {\n        type: \"object\",\n        properties: { location: { type: \"string\" } },\n        required: [\"location\"]\n      }\n    }\n  }]\n});\n\n\u002F\u002F MCP Tool\nconst agent = await client.agents.createVersion(\"mcp-agent\", {\n  kind: \"prompt\",\n  model: \"gpt-4o\",\n  tools: [{\n    type: \"mcp\",\n    server_label: \"my-mcp\",\n    server_url: \"https:\u002F\u002Fmcp-server.example.com\",\n    require_approval: \"always\"\n  }]\n});\n```\n\n### Run Agent\n\n```typescript\nconst openAIClient = await client.getOpenAIClient();\n\n\u002F\u002F Create conversation\nconst conversation = await openAIClient.conversations.create({\n  items: [{ type: \"message\", role: \"user\", content: \"Hello!\" }]\n});\n\n\u002F\u002F Generate response using agent\nconst response = await openAIClient.responses.create(\n  { conversation: conversation.id },\n  { body: { agent: { name: agent.name, type: \"agent_reference\" } } }\n);\n\n\u002F\u002F Cleanup\nawait openAIClient.conversations.delete(conversation.id);\nawait client.agents.deleteVersion(agent.name, agent.version);\n```\n\n## Connections\n\n```typescript\n\u002F\u002F List all connections\nfor await (const conn of client.connections.list()) {\n  console.log(conn.name, conn.type);\n}\n\n\u002F\u002F Get connection by name\nconst conn = await client.connections.get(\"my-connection\");\n\n\u002F\u002F Get connection with credentials\nconst connWithCreds = await client.connections.getWithCredentials(\"my-connection\");\n\n\u002F\u002F Get default connection by type\nconst defaultAzureOpenAI = await client.connections.getDefault(\"AzureOpenAI\", true);\n```\n\n## Deployments\n\n```typescript\n\u002F\u002F List all deployments\nfor await (const deployment of client.deployments.list()) {\n  if (deployment.type === \"ModelDeployment\") {\n    console.log(deployment.name, deployment.modelName);\n  }\n}\n\n\u002F\u002F Filter by publisher\nfor await (const d of client.deployments.list({ modelPublisher: \"OpenAI\" })) {\n  console.log(d.name);\n}\n\n\u002F\u002F Get specific deployment\nconst deployment = await client.deployments.get(\"gpt-4o\");\n```\n\n## Datasets\n\n```typescript\n\u002F\u002F Upload single file\nconst dataset = await client.datasets.uploadFile(\n  \"my-dataset\",\n  \"1.0\",\n  \".\u002Fdata\u002Ftraining.jsonl\"\n);\n\n\u002F\u002F Upload folder\nconst dataset = await client.datasets.uploadFolder(\n  \"my-dataset\",\n  \"2.0\",\n  \".\u002Fdata\u002Fdocuments\u002F\"\n);\n\n\u002F\u002F Get dataset\nconst ds = await client.datasets.get(\"my-dataset\", \"1.0\");\n\n\u002F\u002F List versions\nfor await (const version of client.datasets.listVersions(\"my-dataset\")) {\n  console.log(version);\n}\n\n\u002F\u002F Delete\nawait client.datasets.delete(\"my-dataset\", \"1.0\");\n```\n\n## Indexes\n\n```typescript\nimport { AzureAISearchIndex } from \"@azure\u002Fai-projects\";\n\nconst indexConfig: AzureAISearchIndex = {\n  name: \"my-index\",\n  type: \"AzureSearch\",\n  version: \"1\",\n  indexName: \"my-index\",\n  connectionName: \"search-connection\"\n};\n\n\u002F\u002F Create index\nconst index = await client.indexes.createOrUpdate(\"my-index\", \"1\", indexConfig);\n\n\u002F\u002F List indexes\nfor await (const idx of client.indexes.list()) {\n  console.log(idx.name);\n}\n\n\u002F\u002F Delete\nawait client.indexes.delete(\"my-index\", \"1\");\n```\n\n## Key Types\n\n```typescript\nimport {\n  AIProjectClient,\n  AIProjectClientOptionalParams,\n  Connection,\n  ModelDeployment,\n  DatasetVersionUnion,\n  AzureAISearchIndex\n} from \"@azure\u002Fai-projects\";\n```\n\n## Best Practices\n\n1. **Use getOpenAIClient()** - For responses, conversations, files, and vector stores\n2. **Version your agents** - Use `createVersion` for reproducible agent definitions\n3. **Clean up resources** - Delete agents, conversations when done\n4. **Use connections** - Get credentials from project connections, don't hardcode\n5. **Filter deployments** - Use `modelPublisher` filter to find specific models\n",{"name":5,"license":15,"metadata":44,"description":7},{"author":45,"package":46,"version":47},"Microsoft","@azure\u002Fai-projects","1.0.0","main","5c95bcac4646c5aed06bd9aed5235cfc7985aafc","included\u002Fskills\u002Fby-category\u002Fcloud-azure-microsoft-sdks\u002Fofficial-vendor-reference\u002Fazure-ai-projects-ts",0,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked","https:\u002F\u002Fgithub.com\u002Fstrmt7","2026-11-11 04:18:12",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add strmt7\u002Fai_skills_collection_benchmarked --skill azure-ai-projects-ts",{"tool":61,"command":62},"bunx","bunx skills add 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Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000023","Skill与智能体开发",[126,127,128,129,130,131,25,132,133,134,135,136,137,138,139,140,141,142,26,143],"skill creator","creator","performance","improve","run evals","new","analysis","create","optimize","variance analysis","run","edit","benchmark","a skill","existing skill","evals","modify","accuracy",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":152,"publishedAt":39},"01M71D488XHEZVXXSXFYP8ENMH","playwright-skill","Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Flatest-release-browser-automation\u002Fplaywright-skill\u002FSKILL.md",[150,151],{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},{"categoryNo":123,"categoryName":124,"taxonomyType":95,"taxonomyTypeDesc":96},[110,153,154,155,102,156,157,25,158,109,159,160,161,162,163,164,165,166,167,168],"playwright skill","browser automation","flows","auto","access","Bash","website","ux","login","scripts","read","write","node","install","network","responsive",{"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":88,"licenseFileUrl":173,"sourceHtmlUrl":174,"categories":175,"keywords":177,"publishedAt":39},"01M71CY25GM939M6RES91QSG3T","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FSKILL.md",[176],{"categoryNo":98,"categoryName":99,"taxonomyType":95,"taxonomyTypeDesc":96},[178,179,180,111,181,182,183,26,184,185,105,186,35,187,188,189,190,191,192,193],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","single","file","components","react","multi","state","routing","state management",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":88,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":210,"publishedAt":39},"01M71CXZB60QJJ1H4S3NN94WMZ","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.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FSKILL.md",[201,202,205],{"categoryNo":98,"categoryName":99,"taxonomyType":95,"taxonomyTypeDesc":96},{"categoryNo":203,"categoryName":204,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":206,"categoryName":207,"taxonomyType":208,"taxonomyTypeDesc":209},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[105,211,212,183,213,214,215,36,216,217,35,25,180,218,188,219,189,220,221,27],"design","frontend design","ui design","generic","aesthetics","landing","pages","code","web ui","react components","examples",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":228,"publishedAt":39},"01M71CVZ54WFBTVA7R16PFY1ZE","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fzarr-python\u002FSKILL.md",[],[229,230,231,232,233,234,235,236,237,26,238,239,240,241,242,243,244,245],"zarr python","python","zarr","arrays","Cloud Storage","scientific","storage","parallel","dask","numpy","gcs","s3","integration","scientific computing","cloud","scale","pipelines"]