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).
Best use case
preset is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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).
Teams using preset should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/preset/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How preset Compares
| Feature / Agent | preset | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Deploy Model to Optimal Region
Automates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.
## What This Skill Does
1. Verifies Azure authentication and project scope
2. Checks capacity in current project's region
3. If no capacity: analyzes all regions and shows available alternatives
4. Filters projects by selected region
5. Supports creating new projects if needed
6. Deploys model with GlobalStandard SKU
7. Monitors deployment progress
## Prerequisites
- Azure CLI installed and configured
- Active Azure subscription with Cognitive Services read/create permissions
- Azure AI Foundry project resource ID (`PROJECT_RESOURCE_ID` env var or provided interactively)
- Format: `/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}`
- Found in: Azure AI Foundry portal → Project → Overview → Resource ID
## Quick Workflow
### Fast Path (Current Region Has Capacity)
```
1. Check authentication → 2. Get project → 3. Check current region capacity
→ 4. Deploy immediately
```
### Alternative Region Path (No Capacity)
```
1. Check authentication → 2. Get project → 3. Check current region (no capacity)
→ 4. Query all regions → 5. Show alternatives → 6. Select region + project
→ 7. Deploy
```
---
## Deployment Phases
| Phase | Action | Key Commands |
|-------|--------|-------------|
| 1. Verify Auth | Check Azure CLI login and subscription | `az account show`, `az login` |
| 2. Get Project | Parse `PROJECT_RESOURCE_ID` ARM ID, verify exists | `az cognitiveservices account show` |
| 3. Get Model | List available models, user selects model + version | `az cognitiveservices account list-models` |
| 4. Check Current Region | Query capacity using GlobalStandard SKU | `az rest --method GET .../modelCapacities` |
| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |
| 6. Select Region + Project | User picks region; find or create project | `az cognitiveservices account list`, `az cognitiveservices account create` |
| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | `az cognitiveservices account deployment create` |
For detailed step-by-step instructions, see [workflow reference](references/workflow.md).
---
## Error Handling
| Error | Symptom | Resolution |
|-------|---------|------------|
| Auth failure | `az account show` returns error | Run `az login` then `az account set --subscription <id>` |
| No quota | All regions show 0 capacity | Defer to the [quota skill](../../../quota/quota.md) for increase requests and troubleshooting; check existing deployments; try alternative models |
| Model not found | Empty capacity list | Verify model name with `az cognitiveservices account list-models`; check case sensitivity |
| Name conflict | "deployment already exists" | Append suffix to deployment name (handled automatically by `generate_deployment_name` script) |
| Region unavailable | Region doesn't support model | Select a different region from the available list |
| Permission denied | "Forbidden" or "Unauthorized" | Verify Cognitive Services Contributor role: `az role assignment list --assignee <user>` |
---
## Advanced Usage
```bash
# Custom capacity
az cognitiveservices account deployment create ... --sku-capacity <value>
# Check deployment status
az cognitiveservices account deployment show --name <acct> --resource-group <rg> --deployment-name <name> --query "{Status:properties.provisioningState}"
# Delete deployment
az cognitiveservices account deployment delete --name <acct> --resource-group <rg> --deployment-name <name>
```
## Notes
- **SKU:** GlobalStandard only — **API Version:** 2024-10-01 (GA stable)
---
## Related Skills
- **microsoft-foundry** - Parent skill for Azure AI Foundry operations
- **[quota](../../../quota/quota.md)** — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
- **azure-quick-review** - Review Azure resources for compliance
- **azure-cost-estimation** - Estimate costs for Azure deployments
- **azure-validate** - Validate Azure infrastructure before deploymentRelated Skills
customize
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), 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).
capacity
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.
deploy-model
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI 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/create), project creation (use project/create).
microsoft-foundry
Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt 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).
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), Azure resource security (use azure-security).
azure-validate
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), 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.
azure-upgrade
Assess and upgrade Azure workloads between plans, tiers, or SKUs within Azure. Generates assessment reports and automates upgrade steps. WHEN: upgrade Consumption to Flex Consumption, upgrade Azure Functions plan, migrate hosting plan, upgrade Functions SKU, move to Flex Consumption, upgrade Azure service tier, change hosting plan, upgrade function app plan, migrate App Service to Container Apps.
azure-storage
Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake. Provides object storage, SMB file shares, async messaging, NoSQL key-value, and big data analytics capabilities. Includes access tiers (hot, cool, archive) and lifecycle management. USE FOR: blob storage, file shares, queue storage, table storage, data lake, upload files, download blobs, storage accounts, access tiers, lifecycle management. DO NOT USE FOR: SQL databases, Cosmos DB (use azure-prepare), messaging with Event Hubs or Service Bus (use azure-messaging).
azure-resource-visualizer
Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. WHEN: create architecture diagram, visualize Azure resources, show resource relationships, generate Mermaid diagram, analyze resource group, diagram my resources, architecture visualization, resource topology, map Azure infrastructure.
azure-resource-lookup
List, find, and show Azure resources across subscriptions or resource groups. Handles prompts like "list websites", "list virtual machines", "list my VMs", "show storage accounts", "find container apps", and "what resources do I have". USE FOR: resource inventory, find resources by tag, tag analysis, orphaned resource discovery (not for cost analysis), unattached disks, count resources by type, cross-subscription lookup, and Azure Resource Graph queries. DO NOT USE FOR: deploying/changing resources, cost optimization, or non-Azure clouds.
azure-rbac
Helps users find the right Azure RBAC role for an identity with least privilege access, then generate CLI commands and Bicep code to assign it. Also provides guidance on permissions required to grant roles. WHEN: bicep for role assignment, what role should I assign, least privilege role, RBAC role for, role to read blobs, role for managed identity, custom role definition, assign role to identity, what role do I need to grant access, permissions to assign roles.
azure-quotas
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".