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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).

231 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/preset/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/microsoft/preset/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/preset/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How preset Compares

Feature / AgentpresetStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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).

Which AI agents support this skill?

This skill is compatible with multi.

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 deployment