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Deploys Azure OpenAI models via preset quick setups, full customizations (version/SKU/capacity/RAI policy), and capacity discovery across regions/projects. Uses intent-based routing for appropriate mode.
npx claudepluginhub joshuarweaver/cascade-code-devops-misc-1 --plugin microsoft-github-copilot-for-azureHow this skill is triggered — by the user, by Claude, or both
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Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.
TEST_PROMPTS.mdcapacity/scripts/discover_and_rank.ps1capacity/scripts/discover_and_rank.shcapacity/scripts/query_capacity.ps1capacity/scripts/query_capacity.shcustomize/EXAMPLES.mdcustomize/references/customize-guides.mdcustomize/references/customize-workflow.mdpreset/EXAMPLES.mdpreset/references/preset-workflow.mdpreset/references/workflow.mdscripts/generate_deployment_url.ps1scripts/generate_deployment_url.shGuides interactive step-by-step deployment of Azure OpenAI models with customization of version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy, and advanced options like dynamic quota. Use for precise control over configurations.
Provides guidance on Azure OpenAI Service 2025 models like GPT-5 series, GPT-4.1, o3/o4-mini reasoning, Sora video generation, image/audio models, and Azure CLI deployment.
Routes Azure tasks to the right specialist agent from a catalog. Classifies tasks into domains (architecture, containers, database, etc.) and dispatches single agents or parallel teams. Does not answer Azure questions itself.
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Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.
| Mode | When to Use | Sub-Skill |
|---|---|---|
| Preset | Quick deployment, no customization needed | preset/SKILL.md |
| Customize | Full control: version, SKU, capacity, RAI policy | customize/SKILL.md |
| Capacity Discovery | Find where you can deploy with specific capacity | capacity/SKILL.md |
Analyze the user's prompt and route to the correct mode:
User Prompt
│
├─ Simple deployment (no modifiers)
│ "deploy gpt-4o", "set up a model"
│ └─> PRESET mode
│
├─ Customization keywords present
│ "custom settings", "choose version", "select SKU",
│ "set capacity to X", "configure content filter",
│ "PTU deployment", "with specific quota"
│ └─> CUSTOMIZE mode
│
├─ Capacity/availability query
│ "find where I can deploy", "check capacity",
│ "which region has X capacity", "best region for 10K TPM",
│ "where is this model available"
│ └─> CAPACITY DISCOVERY mode
│
└─ Ambiguous (has capacity target + deploy intent)
"deploy gpt-4o with 10K capacity to best region"
└─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE
| Signal in Prompt | Route To | Reason |
|---|---|---|
| Just model name, no options | Preset | User wants quick deployment |
| "custom", "configure", "choose", "select" | Customize | User wants control |
| "find", "check", "where", "which region", "available" | Capacity | User wants discovery |
| Specific capacity number + "best region" | Capacity → Preset | Discover then deploy quickly |
| Specific capacity number + "custom" keywords | Capacity → Customize | Discover then deploy with options |
| "PTU", "provisioned throughput" | Customize | PTU requires SKU selection |
| "optimal region", "best region" (no capacity target) | Preset | Region optimization is preset's specialty |
Some prompts require two modes in sequence:
Pattern: Capacity → Deploy When a user specifies a capacity requirement AND wants deployment:
💡 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".
Before any deployment, resolve which project to deploy to. This applies to all modes (preset, customize, and after capacity discovery).
PROJECT_RESOURCE_ID env var — if set, use it as the defaultAlways confirm the target before deploying. Show the user what will be used and give them a chance to change it:
Deploying to:
Project: <project-name>
Region: <region>
Resource: <resource-group>
Is this correct? Or choose a different project:
1. ✅ Yes, deploy here (default)
2. 📋 Show me other projects in this region
3. 🌍 Choose a different region
If user picks option 2, show top 5 projects in that region:
Projects in <region>:
1. project-alpha (rg-alpha)
2. project-beta (rg-beta)
3. project-gamma (rg-gamma)
...
⚠️ Never deploy without showing the user which project will be used. This prevents accidental deployments to the wrong resource.
Before presenting any deployment options (SKU, capacity), always validate both of these:
Model supports the SKU — query the model catalog to confirm the selected model+version supports the target SKU:
az cognitiveservices model list --location <region> --subscription <sub-id> -o json
Filter for the model, extract .model.skus[].name to get supported SKUs.
Subscription has available quota — check that the user's subscription has unallocated quota for the SKU+model combination:
az cognitiveservices usage list --location <region> --subscription <sub-id> -o json
Match by usage name pattern OpenAI.<SKU>.<model-name> (e.g., OpenAI.GlobalStandard.gpt-4o). Compute available = limit - currentValue.
⚠️ 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.
💡 Quota management: For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the quota skill instead of duplicating that guidance inline.
All deployment modes require:
az login)PROJECT_RESOURCE_ID env var)