[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PEDDJMCD9X7ZKPEZPY8ES0":3,"skill-same:01M6PEDDJMCD9X7ZKPEZPY8ES0":88},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6PEDDJMCD9X7ZKPEZPY8ES0","deploying-machine-learning-models",null,"This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like \"deploy model,\" \"productionize model,\" \"serve model,\" or \"model deployment.\"","sandraschi\u002Fdocumentation-mcp","sandraschi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F34750307?v=4","01M6C42H9WE5EC9CK3N5RGZP8S","Sandra Schipal","01M6PE1X2FZP8KR5GANPPFTHA8",2,"MIT","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"machine learning","machine","Deploying Machine Learning Models","learning","models","environment","for","requests","errors","via","productionize","use","performance","model","api","best practices","deployment","model deployment","deploy model","workflow","2026-11-10 10:12:30",false,"---\nname: deploying-machine-learning-models\ndescription: |\n  This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like \"deploy model,\" \"productionize model,\" \"serve model,\" or \"model deployment.\"\n---\n\n## Overview\n\nThis skill streamlines the process of deploying machine learning models to production, ensuring efficient and reliable model serving. It leverages automated workflows and best practices to simplify the deployment process and optimize performance.\n\n## How It Works\n\n1. **Analyze Requirements**: The skill analyzes the context and user requirements to determine the appropriate deployment strategy.\n2. **Generate Code**: It generates the necessary code for deploying the model, including API endpoints, data validation, and error handling.\n3. **Deploy Model**: The skill deploys the model to the specified production environment.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Deploy a trained machine learning model to a production environment.\n- Serve a model via an API endpoint for real-time predictions.\n- Automate the model deployment process.\n\n## Examples\n\n### Example 1: Deploying a Regression Model\n\nUser request: \"Deploy my regression model trained on the housing dataset.\"\n\nThe skill will:\n1. Analyze the model and data format.\n2. Generate code for a REST API endpoint to serve the model.\n3. Deploy the model to a cloud-based serving platform.\n\n### Example 2: Productionizing a Classification Model\n\nUser request: \"Productionize the classification model I just trained.\"\n\nThe skill will:\n1. Create a Docker container for the model.\n2. Implement data validation and error handling.\n3. Deploy the container to a Kubernetes cluster.\n\n## Best Practices\n\n- **Data Validation**: Implement thorough data validation to ensure the model receives correct inputs.\n- **Error Handling**: Include robust error handling to gracefully manage unexpected issues.\n- **Performance Monitoring**: Set up performance monitoring to track model latency and throughput.\n\n## Integration\n\nThis skill can be integrated with other tools for model training, data preprocessing, and monitoring.\n","## Overview\n\nThis skill streamlines the process of deploying machine learning models to production, ensuring efficient and reliable model serving. It leverages automated workflows and best practices to simplify the deployment process and optimize performance.\n\n## How It Works\n\n1. **Analyze Requirements**: The skill analyzes the context and user requirements to determine the appropriate deployment strategy.\n2. **Generate Code**: It generates the necessary code for deploying the model, including API endpoints, data validation, and error handling.\n3. **Deploy Model**: The skill deploys the model to the specified production environment.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Deploy a trained machine learning model to a production environment.\n- Serve a model via an API endpoint for real-time predictions.\n- Automate the model deployment process.\n\n## Examples\n\n### Example 1: Deploying a Regression Model\n\nUser request: \"Deploy my regression model trained on the housing dataset.\"\n\nThe skill will:\n1. Analyze the model and data format.\n2. Generate code for a REST API endpoint to serve the model.\n3. Deploy the model to a cloud-based serving platform.\n\n### Example 2: Productionizing a Classification Model\n\nUser request: \"Productionize the classification model I just trained.\"\n\nThe skill will:\n1. Create a Docker container for the model.\n2. Implement data validation and error handling.\n3. Deploy the container to a Kubernetes cluster.\n\n## Best Practices\n\n- **Data Validation**: Implement thorough data validation to ensure the model receives correct inputs.\n- **Error Handling**: Include robust error handling to gracefully manage unexpected issues.\n- **Performance Monitoring**: Set up performance monitoring to track model latency and throughput.\n\n## Integration\n\nThis skill can be integrated with other tools for model training, data preprocessing, and monitoring.\n",{"name":5,"description":7},"master","be156301b65e301887f327d08190ed4f17ba08d2","docs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper",0,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp","https:\u002F\u002Fgithub.com\u002Fsandraschi","2026-11-11 11:02:01",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add sandraschi\u002Fdocumentation-mcp --skill deploying-machine-learning-models",{"tool":62,"command":63},"bunx","bunx skills add sandraschi\u002Fdocumentation-mcp --skill deploying-machine-learning-models",{"tool":65,"command":66},"pnpm","pnpm dlx skills add sandraschi\u002Fdocumentation-mcp --skill deploying-machine-learning-models","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper\u002FSKILL.md",true,[70,76,82],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"assets\u002FREADME.md","ASSET","资产文件",441,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper\u002Fassets\u002FREADME.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002FREADME.md","REFERENCE","参考文档",883,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper\u002Freferences\u002FREADME.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002FREADME.md","SCRIPT","脚本",658,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fmodel-deployment-helper\u002Fscripts\u002FREADME.md",[89,111,137,147,172,198],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":99,"publishedAt":44},"01M6PFAWAHEJJCKKVNXEWNK87W","Docs Research","Use MCP Central Docs server to search, ask, and retrieve documentation (FastMCP, standards, packaging).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fsrc\u002Fdocs_mcp\u002Fskills\u002Fdocs-research\u002FSKILL.md",[95,98],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[100,101,102,103,35,104,105,106,107,108,109,110],"docs research","docs","research","standards","packaging","ask","server","search","fastmcp","documentation","MCP",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":44},"01M6PFAN4DAENMY574Q7Q3CB3H","wrappee-drift","Read-only, single-repo audit of a repo's tool coverage against the software it wraps (its current real API\u002Fdocs, not training-data memory) and against comparable\u002Fcompeting wrappers. Trigger when the user says \"wrappee drift\" followed by a repo name, \"capability audit\" followed by a repo name, or asks whether a repo is missing features the wrapped software or a competitor now has. Always scoped to one named repo per invocation -- never trigger this fleet-wide or unprompted; that is explicitly against the SOP. Do NOT trigger for internal fleet-standard compliance (that is drift-scan) or strategic worth\u002Farchive decisions (that is quality-check) -- this is specifically about the gap between the repo's tool list and the wrapped software's\u002Fcompetitors' actual current capabilities.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwrappee-drift\u002FSKILL.md",[],[118,119,38,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"drift","wrappee drift","data","audit","capabilities","training","memory","features","repo audit","competitors","fleet","now","software","tool list","only","never","coverage","list","read",{"skillNo":138,"name":139,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":146,"publishedAt":44},"01M6PFADA3X031SK3W3T6X0N6H","modules","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002Fmodules\u002FSKILL.md",[142,145],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[139],{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":157,"publishedAt":44},"01M6PFA8YA029N68TW0X31FB1W","mcp-standards","Use when developing MCP servers to access comprehensive standards for scaffolding, packaging, sampling, documentation, testing, error handling, monitoring, and CI\u002FCD workflows.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002FSKILL.md",[153,156],{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[158,110,103,35,159,160,161,162,163,164,30,165,166,167,168,169,109,170,171,104],"mcp standards","monitoring","sampling","scaffolding","servers","cd","error","CI\u002FCD","access","ci","mcp servers","error handling","workflows","testing",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":44},"01M6PFA1XX344WX57B0DJZY94Q","webapp-cua-test","Run the pre-Tauri CUA click-through test (real browser, dev stack, OCR \"Connected\" wait, sidebar nav walk) for a fleet MCP server repo under D:\\Dev\\repos. Trigger when the user says \"cua webapp test\" or \"webapp clickthrough\" followed by a repo name, asks for an e2e click-through of a webapp, or asks whether a webapp boots and connects in a real browser. Not for the installed NSIS app (that is the nsis-build skill \u002F cua-nsis-test) and not for DOM assertions (Playwright), and not for hunting fake data or dead buttons (fakefind).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwebapp-cua-test\u002FSKILL.md",[178,179],{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[181,182,183,184,185,110,186,187,188,30,189,190,191,192,193,194,195,196,106,197],"webapp","test","webapp cua test","playwright","mcp server","dev","dom","assertions","trigger","click","app","tauri","repo","stack","e2e","ocr","browser",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":205,"publishedAt":44},"01M6PF9WYF6NWJR7WVN38TT0WH","ship","Final release gate for a fleet MCP server repo under D:\\Dev\\repos -- run all quality gates, build the release artifact, verify it, commit, push, and cut a GitHub release. Trigger this whenever the user says \"ship it\", \"ship\" followed by a repo name, \"release\" followed by a repo name, or asks to finalize and publish work that's already been done in a repo. This is the terminal step after assfix\u002Fnsis-build, not a substitute for them -- if the repo hasn't been assessed recently, say so before shipping rather than shipping blind.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fship\u002FSKILL.md",[204],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[200,193,30,189,206,207,208,209,210,211,212,106,213,186,214,215,216,185,217,110],"gates","ship it","artifact","done","say","work","publish","push","shipping","quality gates","commit","github"]