[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71401M2YT05GNRXCH3485KX":3,"skill-same:01M71401M2YT05GNRXCH3485KX":83},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":13,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M71401M2YT05GNRXCH3485KX","capacity",null,"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.","engvalentineamba77-hue\u002FMY-PLAN-001","engvalentineamba77-hue","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F275656375?v=4","01M70FQJTV0VTZB145JR1EFPJZ","01M70FQJVMAPKZFCJZ83T8M331",0,"MIT","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"limits","deployment","discovery","for","availability","do","TPM","direct","openai","azure portal","azure openai","across","hand","listing","analysis","search","project","model","projects","2026-11-10 10:12:30",false,"---\nname: capacity\ndescription: \"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.\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n---\n\n# Capacity Discovery\n\nFinds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.\n\n## Quick Reference\n\n| Property | Description |\n|----------|-------------|\n| **Purpose** | Find where you can deploy a model with sufficient capacity |\n| **Scope** | All regions and projects the user has access to |\n| **Output** | Ranked table of regions\u002Fprojects with available capacity |\n| **Action** | Read-only analysis — does NOT deploy. Hands off to preset or customize |\n| **Authentication** | Azure CLI (`az login`) |\n\n## When to Use This Skill\n\n- ✅ User asks \"where can I deploy gpt-4o?\"\n- ✅ User specifies a capacity target: \"find a region with 10K TPM for gpt-4o\"\n- ✅ User wants to compare availability: \"which regions have gpt-4o available?\"\n- ✅ User got a quota error and needs to find an alternative location\n- ✅ User asks \"best region and project for deploying model X\"\n\n**After discovery → hand off to [preset](..\u002Fpreset\u002FSKILL.md) or [customize](..\u002Fcustomize\u002FSKILL.md) for actual deployment.**\n\n## Scripts\n\nPre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `scripts\u002Fdiscover_and_rank.ps1` | Full discovery: capacity + projects + ranking | Primary script for capacity discovery |\n| `scripts\u002Fdiscover_and_rank.sh` | Same as above (bash) | Primary script for capacity discovery |\n| `scripts\u002Fquery_capacity.ps1` | Raw capacity query (no project matching) | Quick capacity check or version listing |\n| `scripts\u002Fquery_capacity.sh` | Same as above (bash) | Quick capacity check or version listing |\n\n## Workflow\n\n### Phase 1: Validate Prerequisites\n\n```bash\naz account show --query \"{Subscription:name, SubscriptionId:id}\" --output table\n```\n\n### Phase 2: Identify Model and Version\n\nExtract model name from user prompt. If version is unknown, query available versions:\n\n```powershell\n.\\scripts\\query_capacity.ps1 -ModelName \u003Cmodel-name>\n```\n```bash\n.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel-name>\n```\n\nThis lists available versions. Use the latest version unless user specifies otherwise.\n\n### Phase 3: Run Discovery\n\nRun the full discovery script with model name, version, and minimum capacity target:\n\n```powershell\n.\\scripts\\discover_and_rank.ps1 -ModelName \u003Cmodel-name> -ModelVersion \u003Cversion> -MinCapacity \u003Ctarget>\n```\n```bash\n.\u002Fscripts\u002Fdiscover_and_rank.sh \u003Cmodel-name> \u003Cversion> \u003Cmin-capacity>\n```\n\n> 💡 The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.\n\n### Phase 3.5: Validate Subscription Quota\n\nAfter discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.\n\n```powershell\n# For each candidate region from discovery results:\n$usageData = az cognitiveservices usage list --location \u003Cregion> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json\n\n# Check quota for each SKU the model supports\n# Quota names follow pattern: OpenAI.\u003CSKU>.\u003Cmodel-name>\n$usageEntry = $usageData | Where-Object { $_.name.value -eq \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" }\n\nif ($usageEntry) {\n  $quotaAvailable = $usageEntry.limit - $usageEntry.currentValue\n} else {\n  $quotaAvailable = 0  # No quota allocated\n}\n```\n```bash\n# For each candidate region from discovery results:\nusage_json=$(az cognitiveservices usage list --location \u003Cregion> --subscription \"$SUBSCRIPTION_ID\" -o json 2>\u002Fdev\u002Fnull)\n\n# Extract quota for specific SKU+model\nquota_available=$(echo \"$usage_json\" | jq -r --arg name \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" \\\n  '.[] | select(.name.value == $name) | .limit - .currentValue')\n```\n\n**Annotate discovery results:**\n\nAdd a \"Quota Available\" column to the ranked output from Phase 3:\n\n| Region | Available Capacity | Meets Target | Projects | Quota Available |\n|--------|-------------------|--------------|----------|-----------------|\n| eastus2 | 120K TPM | ✅ | 3 | ✅ 80K |\n| westus3 | 90K TPM | ✅ | 1 | ❌ 0 (at limit) |\n| swedencentral | 100K TPM | ✅ | 0 | ✅ 100K |\n\nRegions\u002FSKUs where `quotaAvailable = 0` should be marked with ❌ in the results. If no region has available quota, hand off to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting.\n\n### Phase 4: Present Results and Hand Off\n\nAfter the script outputs the ranked table (now annotated with quota info), present it to the user and ask:\n\n1. 🚀 **Quick deploy** to top recommendation with defaults → route to [preset](..\u002Fpreset\u002FSKILL.md)\n2. ⚙️ **Custom deploy** with version\u002FSKU\u002Fcapacity\u002FRAI selection → route to [customize](..\u002Fcustomize\u002FSKILL.md)\n3. 📊 **Check another model** or capacity target → re-run Phase 2\n4. ❌ Cancel\n\n### Phase 5: Confirm Project Before Deploying\n\nBefore handing off to preset or customize, **always confirm the target project** with the user. See the [Project Selection](..\u002FSKILL.md#project-selection-all-modes) rules in the parent router.\n\nIf the discovery table shows a sample project for the chosen region, suggest it as the default. Otherwise, query projects in that region and let the user pick.\n\n## Error Handling\n\n| Error | Cause | Resolution |\n|-------|-------|------------|\n| \"No capacity found\" | Model not available or all at quota | Hand off to [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting |\n| Script auth error | `az login` expired | Re-run `az login` |\n| Empty version list | Model not in region catalog | Try a different region: `.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel> \"\" eastus` |\n| \"No projects found\" | No AI Services resources | Guide to `project\u002Fcreate` skill or Azure Portal |\n\n## Related Skills\n\n- **[preset](..\u002Fpreset\u002FSKILL.md)** — Quick deployment after capacity discovery\n- **[customize](..\u002Fcustomize\u002FSKILL.md)** — Custom deployment after capacity discovery\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance\n","# Capacity Discovery\n\nFinds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.\n\n## Quick Reference\n\n| Property | Description |\n|----------|-------------|\n| **Purpose** | Find where you can deploy a model with sufficient capacity |\n| **Scope** | All regions and projects the user has access to |\n| **Output** | Ranked table of regions\u002Fprojects with available capacity |\n| **Action** | Read-only analysis — does NOT deploy. Hands off to preset or customize |\n| **Authentication** | Azure CLI (`az login`) |\n\n## When to Use This Skill\n\n- ✅ User asks \"where can I deploy gpt-4o?\"\n- ✅ User specifies a capacity target: \"find a region with 10K TPM for gpt-4o\"\n- ✅ User wants to compare availability: \"which regions have gpt-4o available?\"\n- ✅ User got a quota error and needs to find an alternative location\n- ✅ User asks \"best region and project for deploying model X\"\n\n**After discovery → hand off to [preset](..\u002Fpreset\u002FSKILL.md) or [customize](..\u002Fcustomize\u002FSKILL.md) for actual deployment.**\n\n## Scripts\n\nPre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `scripts\u002Fdiscover_and_rank.ps1` | Full discovery: capacity + projects + ranking | Primary script for capacity discovery |\n| `scripts\u002Fdiscover_and_rank.sh` | Same as above (bash) | Primary script for capacity discovery |\n| `scripts\u002Fquery_capacity.ps1` | Raw capacity query (no project matching) | Quick capacity check or version listing |\n| `scripts\u002Fquery_capacity.sh` | Same as above (bash) | Quick capacity check or version listing |\n\n## Workflow\n\n### Phase 1: Validate Prerequisites\n\n```bash\naz account show --query \"{Subscription:name, SubscriptionId:id}\" --output table\n```\n\n### Phase 2: Identify Model and Version\n\nExtract model name from user prompt. If version is unknown, query available versions:\n\n```powershell\n.\\scripts\\query_capacity.ps1 -ModelName \u003Cmodel-name>\n```\n```bash\n.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel-name>\n```\n\nThis lists available versions. Use the latest version unless user specifies otherwise.\n\n### Phase 3: Run Discovery\n\nRun the full discovery script with model name, version, and minimum capacity target:\n\n```powershell\n.\\scripts\\discover_and_rank.ps1 -ModelName \u003Cmodel-name> -ModelVersion \u003Cversion> -MinCapacity \u003Ctarget>\n```\n```bash\n.\u002Fscripts\u002Fdiscover_and_rank.sh \u003Cmodel-name> \u003Cversion> \u003Cmin-capacity>\n```\n\n> 💡 The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.\n\n### Phase 3.5: Validate Subscription Quota\n\nAfter discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.\n\n```powershell\n# For each candidate region from discovery results:\n$usageData = az cognitiveservices usage list --location \u003Cregion> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json\n\n# Check quota for each SKU the model supports\n# Quota names follow pattern: OpenAI.\u003CSKU>.\u003Cmodel-name>\n$usageEntry = $usageData | Where-Object { $_.name.value -eq \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" }\n\nif ($usageEntry) {\n  $quotaAvailable = $usageEntry.limit - $usageEntry.currentValue\n} else {\n  $quotaAvailable = 0  # No quota allocated\n}\n```\n```bash\n# For each candidate region from discovery results:\nusage_json=$(az cognitiveservices usage list --location \u003Cregion> --subscription \"$SUBSCRIPTION_ID\" -o json 2>\u002Fdev\u002Fnull)\n\n# Extract quota for specific SKU+model\nquota_available=$(echo \"$usage_json\" | jq -r --arg name \"OpenAI.\u003CSKU>.\u003Cmodel-name>\" \\\n  '.[] | select(.name.value == $name) | .limit - .currentValue')\n```\n\n**Annotate discovery results:**\n\nAdd a \"Quota Available\" column to the ranked output from Phase 3:\n\n| Region | Available Capacity | Meets Target | Projects | Quota Available |\n|--------|-------------------|--------------|----------|-----------------|\n| eastus2 | 120K TPM | ✅ | 3 | ✅ 80K |\n| westus3 | 90K TPM | ✅ | 1 | ❌ 0 (at limit) |\n| swedencentral | 100K TPM | ✅ | 0 | ✅ 100K |\n\nRegions\u002FSKUs where `quotaAvailable = 0` should be marked with ❌ in the results. If no region has available quota, hand off to the [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting.\n\n### Phase 4: Present Results and Hand Off\n\nAfter the script outputs the ranked table (now annotated with quota info), present it to the user and ask:\n\n1. 🚀 **Quick deploy** to top recommendation with defaults → route to [preset](..\u002Fpreset\u002FSKILL.md)\n2. ⚙️ **Custom deploy** with version\u002FSKU\u002Fcapacity\u002FRAI selection → route to [customize](..\u002Fcustomize\u002FSKILL.md)\n3. 📊 **Check another model** or capacity target → re-run Phase 2\n4. ❌ Cancel\n\n### Phase 5: Confirm Project Before Deploying\n\nBefore handing off to preset or customize, **always confirm the target project** with the user. See the [Project Selection](..\u002FSKILL.md#project-selection-all-modes) rules in the parent router.\n\nIf the discovery table shows a sample project for the chosen region, suggest it as the default. Otherwise, query projects in that region and let the user pick.\n\n## Error Handling\n\n| Error | Cause | Resolution |\n|-------|-------|------------|\n| \"No capacity found\" | Model not available or all at quota | Hand off to [quota skill](..\u002F..\u002F..\u002Fquota\u002Fquota.md) for increase requests and troubleshooting |\n| Script auth error | `az login` expired | Re-run `az login` |\n| Empty version list | Model not in region catalog | Try a different region: `.\u002Fscripts\u002Fquery_capacity.sh \u003Cmodel> \"\" eastus` |\n| \"No projects found\" | No AI Services resources | Guide to `project\u002Fcreate` skill or Azure Portal |\n\n## Related Skills\n\n- **[preset](..\u002Fpreset\u002FSKILL.md)** — Quick deployment after capacity discovery\n- **[customize](..\u002Fcustomize\u002FSKILL.md)** — Custom deployment after capacity discovery\n- **[quota](..\u002F..\u002F..\u002Fquota\u002Fquota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance\n",{"name":5,"license":14,"metadata":42,"description":7},{"author":43,"version":44},"Microsoft","1.0.0","copilot\u002Fvscode-mo1c2t3n-ofh8","d3a7686657d083408f0da2bebd93eb2397972666",".agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue","2026-04-21 00:41:26",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add engvalentineamba77-hue\u002FMY-PLAN-001 --skill capacity",{"tool":57,"command":58},"bunx","bunx skills add engvalentineamba77-hue\u002FMY-PLAN-001 --skill capacity",{"tool":60,"command":61},"pnpm","pnpm dlx skills add engvalentineamba77-hue\u002FMY-PLAN-001 --skill capacity","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002FSKILL.md",true,[65,71,75,79],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"scripts\u002Fdiscover_and_rank.ps1","SCRIPT","脚本",4670,"https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002Fscripts\u002Fdiscover_and_rank.ps1",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"scripts\u002Fdiscover_and_rank.sh",4641,"https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002Fscripts\u002Fdiscover_and_rank.sh",{"path":76,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":77,"sourceHtmlUrl":78},"scripts\u002Fquery_capacity.ps1",3030,"https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002Fscripts\u002Fquery_capacity.ps1",{"path":80,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":81,"sourceHtmlUrl":82},"scripts\u002Fquery_capacity.sh",2942,"https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcapacity\u002Fscripts\u002Fquery_capacity.sh",[84,109,126,145,172,194],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":37},"01M71407SH2N0EQCKE3084K4CN","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\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fpreset\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",[86,23,96,22,21,19,97,98,99,100,101,102,103,104,5,29,105,106,107,108],"quick","alternatives","selection","azure","custom","deployments","high availability","use","version","check","customize","multi","first",{"skillNo":110,"name":106,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":37},"01M71404RABMJRT9NESTZ1JHEQ","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\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002Fcustomize\u002FSKILL.md",[],[106,115,96,116,117,35,118,119,98,120,121,28,122,123,26,124,125,23,21,19],"SET","processing","advanced","select","choose","configure","filter","options","flow","throughput","full",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":136,"publishedAt":37},"01M713ZYMPM1E6HYZBNMKAMC6G","deploy-model","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).","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002Fmodels\u002Fdeploy-model\u002FSKILL.md",[132,133],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":134,"categoryName":135,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发",[35,137,138,115,36,96,139,34,140,141,32,31,29,142,28,143,144,26,23,22],"deploy model","deploy","creation","up","MCP","create","intent","list",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":153,"publishedAt":37},"01M713ZSZ9ANMNRAJZE95C78ZM","microsoft-foundry","Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted\u002Fprompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fmicrosoft-foundry\u002FSKILL.md",[151,152],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":134,"categoryName":135,"taxonomyType":93,"taxonomyTypeDesc":94},[154,155,156,157,158,34,159,160,161,162,163,164,165,166,167,142,168,169,170,171],"microsoft","foundry","microsoft foundry","system prompt","app","trending","eval","improve","knowledge","invoke","workflows","monitoring","batch","permissions","versioning","run","rbac","start",{"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":181,"publishedAt":37},"01M713Z4ESY93XBKA9TSGB54PJ","entra-app-registration","Guides Microsoft Entra ID app registration, OAuth 2.0 authentication, and MSAL integration. USE FOR: create app registration, register Azure AD app, configure OAuth, set up authentication, add API permissions, generate service principal, MSAL example, console app auth, Entra ID setup, Azure AD authentication. DO NOT USE FOR: Azure RBAC or role assignments (use azure-rbac), Key Vault secrets (use azure-keyvault-expiration-audit), general Azure resource security guidance.","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fentra-app-registration\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000010","后端与接口",[158,182,183,115,184,185,140,186,120,187,188,167,142,189,190,170,191,192,193,23],"registration","entra app registration","auth","authentication","entra id","api","guides","generate","audit","secrets","security","add",{"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":201,"publishedAt":37},"01M713YW9CF82WS4BDB0WAVHW2","azure-validate","Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.","https:\u002F\u002Fgithub.com\u002Fengvalentineamba77-hue\u002FMY-PLAN-001\u002Fblob\u002Fcopilot\u002Fvscode-mo1c2t3n-ofh8\u002F.agents\u002Fskills\u002Fazure-validate\u002FSKILL.md",[200],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[202,99,203,158,204,205,206,21,19,207,208,209,210,211,32,212,105,213,214,167],"azure validate","validate","deep","ready","container apps deployment","managed","errors","container","apps","when","review","functions","roles"]