[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H4DPYAMMTBV3RFXPS9M3NV":3,"skill-same:01M6H4DPYAMMTBV3RFXPS9M3NV":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6H4DPYAMMTBV3RFXPS9M3NV","deploying-machine-learning-models",null,"Deploy this skill enables AI assistant 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 th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI\u002FCD'.","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"machine learning","machine","Deploying Machine Learning Models","models","learning","read","environments","errors","CI\u002FCD","io","edit","write","assistant","Bash","ci","code","infrastructure","workflow","performance","openclaw","2026-11-10 10:12:30",false,"---\nname: deploying-machine-learning-models\ndescription: |\n  Deploy this skill enables AI assistant 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 th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI\u002FCD'.\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)\nversion: 1.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\ncompatible-with: claude-code, codex, openclaw\n---\n# Model Deployment Helper\n\nDeploy trained ML models to production environments with API endpoints, containerization, data validation, and performance monitoring.\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\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands","# Model Deployment Helper\n\nDeploy trained ML models to production environments with API endpoints, containerization, data validation, and performance monitoring.\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\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands",{"name":5,"author":51,"license":14,"version":52,"description":53,"allowed-tools":54,"compatible-with":55},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Deploy this skill enables AI assistant 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 th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI\u002FCD'.\n","Read, Write, Edit, Grep, Glob, Bash(cmd:*)","claude-code, codex, openclaw","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","claude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models",0,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills","https:\u002F\u002Fgithub.com\u002Fmicsapp","2026-03-20 11:24:18",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add micsapp\u002Fmicstec-skills --skill deploying-machine-learning-models",{"tool":69,"command":70},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill deploying-machine-learning-models",{"tool":72,"command":73},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill deploying-machine-learning-models","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models\u002FSKILL.md",true,[77,83,89],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"assets\u002FREADME.md","ASSET","资产文件",164,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models\u002Fassets\u002FREADME.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002FREADME.md","REFERENCE","参考文档",67,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models\u002Freferences\u002FREADME.md",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"scripts\u002FREADME.md","SCRIPT","脚本",658,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fmodel-deployment-helper\u002Fskills\u002Fdeploying-machine-learning-models\u002Fscripts\u002FREADME.md",[96,131,153,174,194,218],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":110,"publishedAt":46},"01M6HBTJ02FXBFG9NYEHHYAP52","azure-apim-architecture","Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fazure-apim-architecture\u002FSKILL.md",[102,105],{"categoryNo":103,"categoryName":104,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130],"architecture","azure","azure apim architecture","component","authentication","gateway","topology","Optimization","network","api","team","environment","multi-environment","workspaces","instances","selection","strategies","azure api management","mode","for",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":141,"publishedAt":46},"01M6HBT7J4H082JEEA670B3EQ9","apiops-deployment","Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep\u002FTerraform), CI\u002FCD pipelines (GitHub Actions\u002FAzure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapiops-deployment\u002FSKILL.md",[137,138,139,140],{"categoryNo":103,"categoryName":104,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},[142,143,144,145,112,42,41,146,147,40,148,149,120,121,127,150,151,128,34,152],"deployment","apiops deployment","workflows","azure devops","infrastructure as code","actions","github actions","github-actions","as","guides","implementing",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":160,"publishedAt":46},"01M6HBT2HEA23THNE9E00GYQBE","apim-policy-authoring","Creates production-ready Azure API Management policy XML for authentication (OAuth 2.0, JWT validation, subscription keys), rate limiting, CORS configuration, error handling, and API transformations. Use when implementing API security, access control, or request\u002Fresponse processing logic.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapim-policy-authoring\u002FSKILL.md",[159],{"categoryNo":103,"categoryName":104,"taxonomyType":20,"taxonomyTypeDesc":21},[161,162,163,164,112,115,120,121,165,166,167,168,169,170,171,128,172,173,152,130],"policy","authoring","apim policy authoring","security","access","use","ready","api security","Access Control","error","processing","cors configuration","production-ready",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":182,"publishedAt":46},"01M6HBSY65SB29J9WE9KYD0K6T","api-security-review","Reviews Azure API Management configurations for security vulnerabilities, OWASP API Security Top 10 compliance, VNet Internal mode validation, Private Link verification, and Azure Security Benchmark alignment. Use when performing security audits, pre-deployment validation, or compliance reviews.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapi-security-review\u002FSKILL.md",[180,181],{"categoryNo":103,"categoryName":104,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},[183,164,184,120,168,185,112,142,186,187,188,189,121,190,128,191,192,193,129,130],"review","security review","api security review","benchmark","verification","owasp","owasp-api","alignment","azure security","reviews","top",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":200,"publishedAt":46},"01M6HBSRW4PDFE6K4F8JYPZQ05","jira-cli","Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fjira-cli\u002FSKILL.md",[],[201,202,203,144,204,205,206,207,120,208,209,210,36,211,212,213,214,215,216,217],"cli","jira","jira cli","init","tasks","create","issues","common","work","command","list","view","project","project work","token","line","sprints",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":224,"publishedAt":46},"01M6HBSNS3JHMW603G47TWE31H","calculator","Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fcalculator\u002FSKILL.md",[],[220,225,226,227,228,229,230,166,231,33],"npm","js","calculate","node","math","user","compute"]