[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DBK9Z9EGDB6EXDVFDSTPVY":3,"skill-same:01M6DBK9Z9EGDB6EXDVFDSTPVY":85},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6DBK9Z9EGDB6EXDVFDSTPVY","preset",null,"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).","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"check","high availability","across","location","do","selection","models","deployments","version","use","microsoft","first","analyzing","deployment","azure openai","azure","capacity check","customize","capacity","2026-11-10 10:12:30",false,"---\nname: preset\ndescription: \"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).\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.1\"\n---\n\n# Deploy Model to Optimal Region\n\nAutomates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.\n\n## What This Skill Does\n\n1. Verifies Azure authentication and project scope\n2. Checks capacity in current project's region\n3. If no capacity: analyzes all regions and shows available alternatives\n4. Filters projects by selected region\n5. Supports creating new projects if needed\n6. Deploys model with GlobalStandard SKU\n7. Monitors deployment progress\n\n## Prerequisites\n\n- Azure CLI installed and configured\n- Active Azure subscription with Cognitive Services read\u002Fcreate permissions\n- Azure AI Foundry project resource ID (`PROJECT_RESOURCE_ID` env var or provided interactively)\n  - Format: `\u002Fsubscriptions\u002F{sub-id}\u002FresourceGroups\u002F{rg}\u002Fproviders\u002FMicrosoft.CognitiveServices\u002Faccounts\u002F{account}\u002Fprojects\u002F{project}`\n  - Found in: Azure AI Foundry portal → Project → Overview → Resource ID\n\n## Quick Workflow\n\n### Fast Path (Current Region Has Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region capacity\n→ 4. Deploy immediately\n```\n\n### Alternative Region Path (No Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region (no capacity)\n→ 4. Query all regions → 5. Show alternatives → 6. Select region + project\n→ 7. Deploy\n```\n\n---\n\n## Deployment Phases\n\n| Phase | Action | Key Commands |\n|-------|--------|-------------|\n| 1. Verify Auth | Check Azure CLI login and subscription | `az account show`, `az login` |\n| 2. Get Project | Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists | `az cognitiveservices account show` |\n| 3. Get Model | List available models, user selects model + version | `az cognitiveservices account list-models` |\n| 4. Check Current Region | Query capacity using GlobalStandard SKU | `az rest --method GET ...\u002FmodelCapacities` |\n| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |\n| 6. Select Region + Project | User picks region; find or create project | `az cognitiveservices account list`, `az cognitiveservices account create` |\n| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | `az cognitiveservices account deployment create` |\n\nFor detailed step-by-step instructions, see [workflow reference](references\u002Fworkflow.md).\n\n---\n\n## Error Handling\n\n| Error | Symptom | Resolution |\n|-------|---------|------------|\n| Auth failure | `az account show` returns error | Run `az login` then `az account set --subscription \u003Cid>` |\n| No quota | All regions show 0 capacity | Defer to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting; check existing deployments; try alternative models |\n| Model not found | Empty capacity list | Verify model name with `az cognitiveservices account list-models`; check case sensitivity |\n| Name conflict | \"deployment already exists\" | Append suffix to deployment name (handled automatically by `generate_deployment_name` script) |\n| Region unavailable | Region doesn't support model | Select a different region from the available list |\n| Permission denied | \"Forbidden\" or \"Unauthorized\" | Verify Cognitive Services Contributor role: `az role assignment list --assignee \u003Cuser>` |\n\n---\n\n## Advanced Usage\n\n```bash\n# Custom capacity\naz cognitiveservices account deployment create ... --sku-capacity \u003Cvalue>\n\n# Check deployment status\naz cognitiveservices account deployment show --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname> --query \"{Status:properties.provisioningState}\"\n\n# Delete deployment\naz cognitiveservices account deployment delete --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname>\n```\n\n## Notes\n\n- **SKU:** GlobalStandard only — **API Version:** 2024-10-01 (GA stable)\n\n---\n\n## Related Skills\n\n- **microsoft-foundry** - Parent skill for Azure AI Foundry operations\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill\n- **azure-quick-review** - Review Azure resources for compliance\n- **azure-cost-estimation** - Estimate costs for Azure deployments\n- **azure-validate** - Validate Azure infrastructure before deployment\n","# Deploy Model to Optimal Region\n\nAutomates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.\n\n## What This Skill Does\n\n1. Verifies Azure authentication and project scope\n2. Checks capacity in current project's region\n3. If no capacity: analyzes all regions and shows available alternatives\n4. Filters projects by selected region\n5. Supports creating new projects if needed\n6. Deploys model with GlobalStandard SKU\n7. Monitors deployment progress\n\n## Prerequisites\n\n- Azure CLI installed and configured\n- Active Azure subscription with Cognitive Services read\u002Fcreate permissions\n- Azure AI Foundry project resource ID (`PROJECT_RESOURCE_ID` env var or provided interactively)\n  - Format: `\u002Fsubscriptions\u002F{sub-id}\u002FresourceGroups\u002F{rg}\u002Fproviders\u002FMicrosoft.CognitiveServices\u002Faccounts\u002F{account}\u002Fprojects\u002F{project}`\n  - Found in: Azure AI Foundry portal → Project → Overview → Resource ID\n\n## Quick Workflow\n\n### Fast Path (Current Region Has Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region capacity\n→ 4. Deploy immediately\n```\n\n### Alternative Region Path (No Capacity)\n```\n1. Check authentication → 2. Get project → 3. Check current region (no capacity)\n→ 4. Query all regions → 5. Show alternatives → 6. Select region + project\n→ 7. Deploy\n```\n\n---\n\n## Deployment Phases\n\n| Phase | Action | Key Commands |\n|-------|--------|-------------|\n| 1. Verify Auth | Check Azure CLI login and subscription | `az account show`, `az login` |\n| 2. Get Project | Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists | `az cognitiveservices account show` |\n| 3. Get Model | List available models, user selects model + version | `az cognitiveservices account list-models` |\n| 4. Check Current Region | Query capacity using GlobalStandard SKU | `az rest --method GET ...\u002FmodelCapacities` |\n| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |\n| 6. Select Region + Project | User picks region; find or create project | `az cognitiveservices account list`, `az cognitiveservices account create` |\n| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | `az cognitiveservices account deployment create` |\n\nFor detailed step-by-step instructions, see [workflow reference](references\u002Fworkflow.md).\n\n---\n\n## Error Handling\n\n| Error | Symptom | Resolution |\n|-------|---------|------------|\n| Auth failure | `az account show` returns error | Run `az login` then `az account set --subscription \u003Cid>` |\n| No quota | All regions show 0 capacity | Defer to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting; check existing deployments; try alternative models |\n| Model not found | Empty capacity list | Verify model name with `az cognitiveservices account list-models`; check case sensitivity |\n| Name conflict | \"deployment already exists\" | Append suffix to deployment name (handled automatically by `generate_deployment_name` script) |\n| Region unavailable | Region doesn't support model | Select a different region from the available list |\n| Permission denied | \"Forbidden\" or \"Unauthorized\" | Verify Cognitive Services Contributor role: `az role assignment list --assignee \u003Cuser>` |\n\n---\n\n## Advanced Usage\n\n```bash\n# Custom capacity\naz cognitiveservices account deployment create ... --sku-capacity \u003Cvalue>\n\n# Check deployment status\naz cognitiveservices account deployment show --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname> --query \"{Status:properties.provisioningState}\"\n\n# Delete deployment\naz cognitiveservices account deployment delete --name \u003Cacct> --resource-group \u003Crg> --deployment-name \u003Cname>\n```\n\n## Notes\n\n- **SKU:** GlobalStandard only — **API Version:** 2024-10-01 (GA stable)\n\n---\n\n## Related Skills\n\n- **microsoft-foundry** - Parent skill for Azure AI Foundry operations\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill\n- **azure-quick-review** - Review Azure resources for compliance\n- **azure-cost-estimation** - Estimate costs for Azure deployments\n- **azure-validate** - Validate Azure infrastructure before deployment\n",{"name":5,"license":15,"metadata":48,"description":7},{"author":49,"version":50},"Microsoft","1.0.1","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill preset",{"tool":63,"command":64},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill preset",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill preset","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",true,[71,77,81],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"EXAMPLES.md","REFERENCE","参考文档",2982,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FEXAMPLES.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fpreset-workflow.md",22472,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002Freferences\u002Fpreset-workflow.md",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fworkflow.md",5763,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002Freferences\u002Fworkflow.md",[86,107,135,161,175,191],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":43},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. 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