[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H4CBC9CEQG6WGVSPFDJ192":3,"skill-same:01M6H4CBC9CEQG6WGVSPFDJ192":87},{"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":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6H4CBC9CEQG6WGVSPFDJ192","setting-up-experiment-tracking",null,"Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to \"setup experiment tracking\" or \"initialize MLflow\". Trigger with relevant phrases based on skill purpose.","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\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"Experiment Tracking","tracking","Setting Up Experiment Tracking","experiment","up","for","implement","with","relevant","machine","trigger","io","use","environment","initialize","learning","Bash","artifacts","code","machine learning","2026-11-10 10:12:30",false,"---\nname: setting-up-experiment-tracking\ndescription: |\n  Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to \"setup experiment tracking\" or \"initialize MLflow\". Trigger with relevant phrases based on skill purpose.\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# Experiment Tracking Setup\n\nConfigure ML experiment tracking with MLflow or Weights & Biases, including environment setup and code for logging parameters, metrics, and artifacts.\n\n## Overview\n\nThis skill streamlines the process of setting up experiment tracking for machine learning projects. It automates environment configuration, tool initialization, and provides code examples to get you started quickly.\n\n## How It Works\n\n1. **Analyze Context**: The skill analyzes the current project context to determine the appropriate experiment tracking tool (MLflow or W&B) based on user preference or existing project configuration.\n2. **Configure Environment**: It configures the environment by installing necessary Python packages and setting environment variables.\n3. **Initialize Tracking**: The skill initializes the chosen tracking tool, potentially starting a local MLflow server or connecting to a W&B project.\n4. **Provide Code Snippets**: It provides code snippets demonstrating how to log experiment parameters, metrics, and artifacts within your ML code.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Start tracking machine learning experiments in a new project.\n- Integrate experiment tracking into an existing ML project.\n- Quickly set up MLflow or Weights & Biases for experiment management.\n- Automate the process of logging parameters, metrics, and artifacts.\n\n## Examples\n\n### Example 1: Starting a New Project with MLflow\n\nUser request: \"track experiments using mlflow\"\n\nThe skill will:\n1. Install the `mlflow` Python package.\n2. Generate example code for logging parameters, metrics, and artifacts to an MLflow server.\n\n### Example 2: Integrating W&B into an Existing Project\n\nUser request: \"setup experiment tracking with wandb\"\n\nThe skill will:\n1. Install the `wandb` Python package.\n2. Generate example code for initializing W&B and logging experiment data.\n\n## Best Practices\n\n- **Tool Selection**: Consider the scale and complexity of your project when choosing between MLflow and W&B. MLflow is well-suited for local tracking, while W&B offers cloud-based collaboration and advanced features.\n- **Consistent Logging**: Establish a consistent logging strategy for parameters, metrics, and artifacts to ensure comparability across experiments.\n- **Artifact Management**: Utilize artifact logging to track models, datasets, and other relevant files associated with each experiment.\n\n## Integration\n\nThis skill can be used in conjunction with other skills that generate or modify machine learning code, such as skills for model training or data preprocessing. It ensures that all experiments are properly tracked and documented.\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","# Experiment Tracking Setup\n\nConfigure ML experiment tracking with MLflow or Weights & Biases, including environment setup and code for logging parameters, metrics, and artifacts.\n\n## Overview\n\nThis skill streamlines the process of setting up experiment tracking for machine learning projects. It automates environment configuration, tool initialization, and provides code examples to get you started quickly.\n\n## How It Works\n\n1. **Analyze Context**: The skill analyzes the current project context to determine the appropriate experiment tracking tool (MLflow or W&B) based on user preference or existing project configuration.\n2. **Configure Environment**: It configures the environment by installing necessary Python packages and setting environment variables.\n3. **Initialize Tracking**: The skill initializes the chosen tracking tool, potentially starting a local MLflow server or connecting to a W&B project.\n4. **Provide Code Snippets**: It provides code snippets demonstrating how to log experiment parameters, metrics, and artifacts within your ML code.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Start tracking machine learning experiments in a new project.\n- Integrate experiment tracking into an existing ML project.\n- Quickly set up MLflow or Weights & Biases for experiment management.\n- Automate the process of logging parameters, metrics, and artifacts.\n\n## Examples\n\n### Example 1: Starting a New Project with MLflow\n\nUser request: \"track experiments using mlflow\"\n\nThe skill will:\n1. Install the `mlflow` Python package.\n2. Generate example code for logging parameters, metrics, and artifacts to an MLflow server.\n\n### Example 2: Integrating W&B into an Existing Project\n\nUser request: \"setup experiment tracking with wandb\"\n\nThe skill will:\n1. Install the `wandb` Python package.\n2. Generate example code for initializing W&B and logging experiment data.\n\n## Best Practices\n\n- **Tool Selection**: Consider the scale and complexity of your project when choosing between MLflow and W&B. MLflow is well-suited for local tracking, while W&B offers cloud-based collaboration and advanced features.\n- **Consistent Logging**: Establish a consistent logging strategy for parameters, metrics, and artifacts to ensure comparability across experiments.\n- **Artifact Management**: Utilize artifact logging to track models, datasets, and other relevant files associated with each experiment.\n\n## Integration\n\nThis skill can be used in conjunction with other skills that generate or modify machine learning code, such as skills for model training or data preprocessing. It ensures that all experiments are properly tracked and documented.\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":43,"license":14,"version":44,"description":45,"allowed-tools":46,"compatible-with":47},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to \"setup experiment tracking\" or \"initialize MLflow\". Trigger with relevant phrases based on skill purpose.\n","Read, Write, Edit, Grep, Glob, Bash(cmd:*)","claude-code, codex, openclaw","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","claude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking",0,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills","https:\u002F\u002Fgithub.com\u002Fmicsapp","2026-03-20 11:24:18",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add micsapp\u002Fmicstec-skills --skill setting-up-experiment-tracking",{"tool":61,"command":62},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill setting-up-experiment-tracking",{"tool":64,"command":65},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill setting-up-experiment-tracking","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\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking\u002FSKILL.md",true,[69,75,81],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"assets\u002FREADME.md","ASSET","资产文件",65,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking\u002Fassets\u002FREADME.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002FREADME.md","REFERENCE","参考文档",69,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking\u002Freferences\u002FREADME.md",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002FREADME.md","SCRIPT","脚本",721,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fai-ml\u002Fexperiment-tracking-setup\u002Fskills\u002Fsetting-up-experiment-tracking\u002Fscripts\u002FREADME.md",[88,123,152,172,192,217],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":104,"publishedAt":38},"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",[94,99],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[105,106,107,108,109,110,111,112,113,114,115,31,116,117,118,119,120,121,122,23],"architecture","azure","azure apim architecture","component","authentication","gateway","topology","Optimization","network","api","team","multi-environment","workspaces","instances","selection","strategies","azure api management","mode",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":137,"publishedAt":38},"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",[129,130,133,136],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},{"categoryNo":131,"categoryName":132,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":134,"categoryName":135,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[138,139,140,141,106,142,36,143,144,145,146,147,114,115,120,148,149,121,150,151],"deployment","apiops deployment","workflows","azure devops","infrastructure","infrastructure as code","actions","ci","github actions","github-actions","as","guides","CI\u002FCD","implementing",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":159,"publishedAt":38},"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",[158],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[160,161,162,163,106,109,114,115,164,30,165,166,167,168,169,121,170,171,151,23],"policy","authoring","apim policy authoring","security","access","ready","api security","Access Control","error","processing","cors configuration","production-ready",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":38},"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",[178,179],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[181,163,182,114,166,183,106,138,184,185,186,187,115,188,121,189,190,191,122,23],"review","security review","api security review","benchmark","verification","owasp","owasp-api","alignment","azure security","reviews","top",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":198,"publishedAt":38},"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",[],[199,200,201,140,202,203,204,205,114,206,207,208,209,210,211,212,213,214,215,216],"cli","jira","jira cli","init","tasks","create","issues","common","work","command","edit","list","view","project","project work","token","line","sprints",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":223,"publishedAt":38},"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",[],[219,224,225,226,227,228,229,30,230,231],"npm","js","calculate","node","math","user","compute","errors"]