[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M699PYFQRSH6RY3DPSHTREF2":3,"skill-same:01M699PYFQRSH6RY3DPSHTREF2":95},{"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":26,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M699PYFQRSH6RY3DPSHTREF2","deploy-model",null,"Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent\u002Fcreate), project creation (use project\u002Fcreate).","microsoft\u002Fazure-skills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M699HXPKQFT1BH5KD1PG0CB4",1636,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,9,48],"deploy","deploy model","model","projects","listing","quick","availability","creation","project","across","do","intent","up","list","MCP","analysis","SET","openai","create","azure openai","foundry","policy","2026-11-10 10:12:30",false,"---\nname: deploy-model\ndescription: \"Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version\u002FSKU\u002Fcapacity\u002FRAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent\u002Fcreate), project creation (use project\u002Fcreate).\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n---\n\n# Deploy Model\n\n> **Scope — read this first.** This skill creates model deployments **out-of-band** via Azure CLI \u002F MCP \u002F portal. For azd-managed Foundry projects (those scaffolded from `azd ai agent init`), declare deployments in `azure.yaml services.ai-project.deployments[]` instead — `azd ai agent init` writes the entry from the sample manifest and `azd provision` creates the deployment through Bicep. See [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](..\u002F..\u002Ffoundry-agent\u002Fcreate\u002Fcreate-hosted.md) for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.\n\nUnified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.\n\n## Quick Reference\n\n| Mode | When to Use | Sub-Skill |\n|------|-------------|-----------|\n| **Preset** | Quick deployment, no customization needed | [preset\u002FSKILL.md](preset\u002FSKILL.md) |\n| **Customize** | Full control: version, SKU, capacity, RAI policy | [customize\u002FSKILL.md](customize\u002FSKILL.md) |\n| **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity\u002FSKILL.md](capacity\u002FSKILL.md) |\n\n## Intent Detection\n\nAnalyze the user's prompt and route to the correct mode:\n\n```\nUser Prompt\n    │\n    ├─ Simple deployment (no modifiers)\n    │  \"deploy gpt-4o\", \"set up a model\"\n    │  └─> PRESET mode\n    │\n    ├─ Customization keywords present\n    │  \"custom settings\", \"choose version\", \"select SKU\",\n    │  \"set capacity to X\", \"configure content filter\",\n    │  \"PTU deployment\", \"with specific quota\"\n    │  └─> CUSTOMIZE mode\n    │\n    ├─ Capacity\u002Favailability query\n    │  \"find where I can deploy\", \"check capacity\",\n    │  \"which region has X capacity\", \"best region for 10K TPM\",\n    │  \"where is this model available\"\n    │  └─> CAPACITY DISCOVERY mode\n    │\n    └─ Ambiguous (has capacity target + deploy intent)\n       \"deploy gpt-4o with 10K capacity to best region\"\n       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE\n```\n\n### Routing Rules\n\n| Signal in Prompt | Route To | Reason |\n|------------------|----------|--------|\n| Just model name, no options | **Preset** | User wants quick deployment |\n| \"custom\", \"configure\", \"choose\", \"select\" | **Customize** | User wants control |\n| \"find\", \"check\", \"where\", \"which region\", \"available\" | **Capacity** | User wants discovery |\n| Specific capacity number + \"best region\" | **Capacity → Preset** | Discover then deploy quickly |\n| Specific capacity number + \"custom\" keywords | **Capacity → Customize** | Discover then deploy with options |\n| \"PTU\", \"provisioned throughput\" | **Customize** | PTU requires SKU selection |\n| \"optimal region\", \"best region\" (no capacity target) | **Preset** | Region optimization is preset's specialty |\n\n### Multi-Mode Chaining\n\nSome prompts require two modes in sequence:\n\n**Pattern: Capacity → Deploy**\nWhen a user specifies a capacity requirement AND wants deployment:\n1. Run **Capacity Discovery** to find regions\u002Fprojects with sufficient quota\n2. Present findings to user\n3. Ask: \"Would you like to deploy with **quick defaults** or **customize settings**?\"\n4. Route to **Preset** or **Customize** based on answer\n\n> ? **Tip:** If unsure which mode the user wants, default to **Preset** (quick deployment). Users who want customization will typically use explicit keywords like \"custom\", \"configure\", or \"with specific settings\".\n\n## Project Selection (All Modes)\n\nBefore any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).\n\n### Resolution Order\n\n1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default\n2. **Check user prompt** — if user named a specific project or region, use that\n3. **If neither** — query the user's projects and suggest the current one\n\n### Confirmation Step (Required)\n\n**Always confirm the target before deploying.** Show the user what will be used and give them a chance to change it:\n\n```\nDeploying to:\n  Project:  \u003Cproject-name>\n  Region:   \u003Cregion>\n  Resource: \u003Cresource-group>\n\nIs this correct? Or choose a different project:\n  1. ✅ Yes, deploy here (default)\n  2. ? Show me other projects in this region\n  3. ? Choose a different region\n```\n\nIf user picks option 2, show top 5 projects in that region:\n\n```\nProjects in \u003Cregion>:\n  1. project-alpha (rg-alpha)\n  2. project-beta (rg-beta)\n  3. project-gamma (rg-gamma)\n  ...\n```\n\n> ⚠️ **Never deploy without showing the user which project will be used.** This prevents accidental deployments to the wrong resource.\n\n## Pre-Deployment Validation (All Modes)\n\nBefore presenting any deployment options (SKU, capacity), always validate both of these:\n\n1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:\n   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Filter for the model, extract `.model.skus[].name` to get supported SKUs.\n\n2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:\n   ```bash\n   az cognitiveservices usage list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Match by usage name pattern `OpenAI.\u003CSKU>.\u003Cmodel-name>` (e.g., `OpenAI.GlobalStandard.gpt-4o`). Compute `available = limit - currentValue`.\n\n> ⚠️ **Warning:** Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.\n\n> ? **Quota management:** For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the [quota skill](..\u002F..\u002Fquota\u002Fquota.md) instead of duplicating that guidance inline.\n\n## Prerequisites\n\nAll deployment modes require:\n- Azure CLI installed and authenticated (`az login`)\n- Active Azure subscription with deployment permissions\n- Microsoft Foundry project resource ID (or agent will help discover it via `PROJECT_RESOURCE_ID` env var)\n\n## Sub-Skills\n\n- **[preset\u002FSKILL.md](preset\u002FSKILL.md)** — Quick deployment to optimal region with sensible defaults\n- **[customize\u002FSKILL.md](customize\u002FSKILL.md)** — Interactive guided flow with full configuration control\n- **[capacity\u002FSKILL.md](capacity\u002FSKILL.md)** — Discover available capacity across regions and projects\n","# Deploy Model\n\n> **Scope — read this first.** This skill creates model deployments **out-of-band** via Azure CLI \u002F MCP \u002F portal. For azd-managed Foundry projects (those scaffolded from `azd ai agent init`), declare deployments in `azure.yaml services.ai-project.deployments[]` instead — `azd ai agent init` writes the entry from the sample manifest and `azd provision` creates the deployment through Bicep. See [foundry-agent\u002Fcreate\u002Fcreate-hosted.md](..\u002F..\u002Ffoundry-agent\u002Fcreate\u002Fcreate-hosted.md) for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.\n\nUnified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.\n\n## Quick Reference\n\n| Mode | When to Use | Sub-Skill |\n|------|-------------|-----------|\n| **Preset** | Quick deployment, no customization needed | [preset\u002FSKILL.md](preset\u002FSKILL.md) |\n| **Customize** | Full control: version, SKU, capacity, RAI policy | [customize\u002FSKILL.md](customize\u002FSKILL.md) |\n| **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity\u002FSKILL.md](capacity\u002FSKILL.md) |\n\n## Intent Detection\n\nAnalyze the user's prompt and route to the correct mode:\n\n```\nUser Prompt\n    │\n    ├─ Simple deployment (no modifiers)\n    │  \"deploy gpt-4o\", \"set up a model\"\n    │  └─> PRESET mode\n    │\n    ├─ Customization keywords present\n    │  \"custom settings\", \"choose version\", \"select SKU\",\n    │  \"set capacity to X\", \"configure content filter\",\n    │  \"PTU deployment\", \"with specific quota\"\n    │  └─> CUSTOMIZE mode\n    │\n    ├─ Capacity\u002Favailability query\n    │  \"find where I can deploy\", \"check capacity\",\n    │  \"which region has X capacity\", \"best region for 10K TPM\",\n    │  \"where is this model available\"\n    │  └─> CAPACITY DISCOVERY mode\n    │\n    └─ Ambiguous (has capacity target + deploy intent)\n       \"deploy gpt-4o with 10K capacity to best region\"\n       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE\n```\n\n### Routing Rules\n\n| Signal in Prompt | Route To | Reason |\n|------------------|----------|--------|\n| Just model name, no options | **Preset** | User wants quick deployment |\n| \"custom\", \"configure\", \"choose\", \"select\" | **Customize** | User wants control |\n| \"find\", \"check\", \"where\", \"which region\", \"available\" | **Capacity** | User wants discovery |\n| Specific capacity number + \"best region\" | **Capacity → Preset** | Discover then deploy quickly |\n| Specific capacity number + \"custom\" keywords | **Capacity → Customize** | Discover then deploy with options |\n| \"PTU\", \"provisioned throughput\" | **Customize** | PTU requires SKU selection |\n| \"optimal region\", \"best region\" (no capacity target) | **Preset** | Region optimization is preset's specialty |\n\n### Multi-Mode Chaining\n\nSome prompts require two modes in sequence:\n\n**Pattern: Capacity → Deploy**\nWhen a user specifies a capacity requirement AND wants deployment:\n1. Run **Capacity Discovery** to find regions\u002Fprojects with sufficient quota\n2. Present findings to user\n3. Ask: \"Would you like to deploy with **quick defaults** or **customize settings**?\"\n4. Route to **Preset** or **Customize** based on answer\n\n> ? **Tip:** If unsure which mode the user wants, default to **Preset** (quick deployment). Users who want customization will typically use explicit keywords like \"custom\", \"configure\", or \"with specific settings\".\n\n## Project Selection (All Modes)\n\nBefore any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).\n\n### Resolution Order\n\n1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default\n2. **Check user prompt** — if user named a specific project or region, use that\n3. **If neither** — query the user's projects and suggest the current one\n\n### Confirmation Step (Required)\n\n**Always confirm the target before deploying.** Show the user what will be used and give them a chance to change it:\n\n```\nDeploying to:\n  Project:  \u003Cproject-name>\n  Region:   \u003Cregion>\n  Resource: \u003Cresource-group>\n\nIs this correct? Or choose a different project:\n  1. ✅ Yes, deploy here (default)\n  2. ? Show me other projects in this region\n  3. ? Choose a different region\n```\n\nIf user picks option 2, show top 5 projects in that region:\n\n```\nProjects in \u003Cregion>:\n  1. project-alpha (rg-alpha)\n  2. project-beta (rg-beta)\n  3. project-gamma (rg-gamma)\n  ...\n```\n\n> ⚠️ **Never deploy without showing the user which project will be used.** This prevents accidental deployments to the wrong resource.\n\n## Pre-Deployment Validation (All Modes)\n\nBefore presenting any deployment options (SKU, capacity), always validate both of these:\n\n1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:\n   ```bash\n   az cognitiveservices model list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Filter for the model, extract `.model.skus[].name` to get supported SKUs.\n\n2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:\n   ```bash\n   az cognitiveservices usage list --location \u003Cregion> --subscription \u003Csub-id> -o json\n   ```\n   Match by usage name pattern `OpenAI.\u003CSKU>.\u003Cmodel-name>` (e.g., `OpenAI.GlobalStandard.gpt-4o`). Compute `available = limit - currentValue`.\n\n> ⚠️ **Warning:** Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.\n\n> ? **Quota management:** For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the [quota skill](..\u002F..\u002Fquota\u002Fquota.md) instead of duplicating that guidance inline.\n\n## Prerequisites\n\nAll deployment modes require:\n- Azure CLI installed and authenticated (`az login`)\n- Active Azure subscription with deployment permissions\n- Microsoft Foundry project resource ID (or agent will help discover it via `PROJECT_RESOURCE_ID` env var)\n\n## Sub-Skills\n\n- **[preset\u002FSKILL.md](preset\u002FSKILL.md)** — Quick deployment to optimal region with sensible defaults\n- **[customize\u002FSKILL.md](customize\u002FSKILL.md)** — Interactive guided flow with full configuration control\n- **[capacity\u002FSKILL.md](capacity\u002FSKILL.md)** — Discover available capacity across regions and projects\n",{"name":5,"license":15,"metadata":54,"description":7},{"author":12,"version":55},"1.0.0","main","fbda857c484a85c7ce644f56809b7f204471fb69",".github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model",266,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-11 02:03:16",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add microsoft\u002Fazure-skills --skill deploy-model",{"tool":71,"command":72},"bunx","bunx skills add microsoft\u002Fazure-skills --skill deploy-model",{"tool":74,"command":75},"pnpm","pnpm dlx skills add microsoft\u002Fazure-skills --skill deploy-model","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",true,[79,85,89],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"scripts\u002Fgenerate_deployment_url.ps1","SCRIPT","脚本",2371,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.ps1",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Fgenerate_deployment_url.sh",2687,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fscripts\u002Fgenerate_deployment_url.sh",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"TEST_PROMPTS.md","REFERENCE","参考文档",3367,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FTEST_PROMPTS.md",[96,122,144,161,174,181],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":103,"publishedAt":49},"01M699VFT1T15GFDGC4K4ZHY4N","python-appservice-deploy","Deploy Python (Flask\u002FDjango\u002FFastAPI) code to Azure App Service Linux. WHEN: \"Flask App Service\", \"Django App Service\", \"FastAPI App Service\", \"deploy Python to App Service\". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform\u002FBicep\u002FIaC, full infra — use azure-prepare.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fpython-appservice-deploy\u002FSKILL.md",[102],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[104,27,105,106,107,108,37,109,110,111,112,113,114,115,116,117,118,119,120,121],"python appservice deploy","python","linux","infra","app","django","for","azure app service","azure","container","iac","fastapi","use","apps","when","code","functions","fastapi app",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":130,"publishedAt":49},"01M699VDV3PT5P0YDXRT4GM2TJ","preset","Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",[128,129],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[124,27,131,9,132,133,37,32,33,110,134,135,136,112,137,138,139,116,140,36,141,142,143],"setup","capacity","customize","deployment","alternatives","selection","custom","deployments","high availability","version","check","multi","first",{"skillNo":145,"name":133,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":49},"01M699VBT6YFAKBWCK890XDB6N","Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard\u002FStandard\u002FProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcustomize\u002FSKILL.md",[149],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[133,124,9,132,48,43,32,151,152,29,153,154,136,155,156,46,157,158,44,159,160,37,110,134],"processing","advanced","select","choose","configure","filter","options","flow","throughput","full",{"skillNo":162,"name":132,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":49},"01M699V9FXHBSWG3Y48057VAYZ","Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002FSKILL.md",[],[132,124,27,9,133,30,29,35,167,42,31,168,36,46,169,170,44,171,172,37,33,110,173,134],"search","hand","limits","azure portal","direct","TPM","discovery",{"skillNo":175,"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":176,"categories":177,"keywords":180,"publishedAt":49},"01M699V7GW2RF4AB3RE3Z8Y048","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",[178,179],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[27,28,29,124,41,47,9,132,48,43,30,32,34,35,39,42,31,36,45,46,38,40,44,37,33],{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":188,"publishedAt":49},"01M699V3NA4YQKCHJTVWHBQH10","finetuning","Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fazure-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft-foundry\u002Ffinetuning\u002FSKILL.md",[187],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[183,189,27,47,190,9,191,34,29,192,193,194,195,196,197,198,199,200,37,110,201,202,134,203],"placeholder","microsoft foundry","Prompt Optimization","training","dataset","optimizer","Evaluation","tuning","grader","data","supervised","distillation","Optimization","calibrate","submission"]