AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The preset skill streamlines the deployment of Azure OpenAI models by programmatically checking for capacity across regions. It first evaluates the user's current project region and, if capacity is unavailable, queries all regions to identify viable alternatives. The skill handles the end-to-end process, including authentication verification, project identification, model selection, and deployment execution using the GlobalStandard SKU.
What it does
- Verifies Azure CLI authentication and subscription context.
- Checks capacity for specific models in the current project region.
- Scans all available regions for capacity if the primary region is exhausted.
- Facilitates the selection of an alternative region and project.
- Automates the deployment of models with the GlobalStandard SKU.
- Monitors and reports on the status of the deployment process.
When to use it
- When you need to deploy an Azure OpenAI model quickly without manual capacity checks.
- When your primary region lacks sufficient capacity for a new deployment.
- When you want to ensure high availability by selecting the best available region.
- When setting up new AI projects within the Microsoft Foundry environment.
Advantages
- Reduces manual effort by automating multi-region capacity lookups.
- Prevents deployment failures caused by regional capacity constraints.
- Ensures consistent deployment configurations using GlobalStandard SKU.
- Provides clear paths for error handling and quota management.
Usage examples
- “Deploy my GPT-4o model to the best available region.”
- The agent checks the current project region, finds it full, scans other regions, presents the best options, and deploys the model to the selected location.
- “I need to set up a new model deployment but I am not sure which region has space.”
- The agent queries all regions for the specified model's capacity and guides you through selecting a region that can accommodate the deployment.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/preset/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip preset.zip -d .agents/skills
Documentation by Microsoft
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
Verifies Azure authentication and project scope
Checks capacity in current project's region
If no capacity: analyzes all regions and shows available alternatives
Filters projects by selected region
Supports creating new projects if needed
Deploys model with GlobalStandard SKU
Monitors deployment progress
Prerequisites
Azure CLI installed and configured
Active Azure subscription with Cognitive Services read/create permissions
Microsoft Foundry project resource ID (
PROJECT_RESOURCE_IDenv var or provided interactively)Format:
/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}Found in: Microsoft 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 immediatelyAlternative 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. DeployDeployment 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.
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 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
# 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 Microsoft Foundry operations
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
azure-quick-review - Review Azure resources for compliance
cost-estimation - Estimate costs through the separately installed
azure-costpluginazure-validate - Validate Azure infrastructure before deployment
Files
- ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
- EXAMPLES.md2.9 KiB
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md4.8 KiB
- references/preset-workflow.md22.0 KiB
- references/workflow.md5.6 KiB
EXAMPLES.md
Examples: preset
Example 1: Fast Path — Current Region Has Capacity
Scenario: Deploy gpt-4o to project in East US, which has capacity. Result: Deployed in ~45s. No region selection needed. 100K TPM default, GlobalStandard SKU.
Example 2: Alternative Region — No Capacity in Current Region
Scenario: Deploy gpt-4-turbo to dev project in West US 2 (no capacity). Result: Queried all regions → user selected East US 2 (120K available) → deployed in ~2 min.
Example 3: Create New Project in Optimal Region
Scenario: Deploy gpt-4o-mini in Europe for data residency; no existing European project. Result: Created AI Services hub + project in Sweden Central → deployed in ~4 min with 150K TPM.
Example 4: Insufficient Quota Everywhere
Scenario: Deploy gpt-4 but all regions have exhausted quota. Result: Graceful failure with actionable guidance:
Request quota increase via the quota skill
List existing deployments consuming quota
Suggest alternative models (gpt-4o, gpt-4o-mini)
Example 5: First-Time User — No Project
Scenario: Deploy gpt-4o with no existing Microsoft Foundry project. Result: Full onboarding in ~5 min — created resource group, AI Services hub, project, then deployed.
Example 6: Deployment Name Conflict
Scenario: Auto-generated deployment name already exists.
Result: Appended random hex suffix (e.g., -7b9e) and retried automatically.
Example 7: Multi-Version Model Selection
Scenario: Deploy "latest gpt-4o" when multiple versions exist. Result: Latest stable version auto-selected. Capacity aggregated across versions.
Example 8: Anthropic Model (claude-sonnet-4-6)
Scenario: Deploy claude-sonnet-4-6 (Anthropic model requiring modelProviderData).
Result: User prompted for industry selection → tenant country code and org name fetched automatically → deployed via ARM REST API with modelProviderData payload in ~2 min. Capacity set to 1 (MaaS billing).
Summary of Scenarios
| Scenario | Duration | Key Features |
|---|---|---|
| 1: Fast Path | ~45s | Current region has capacity, direct deploy |
| 2: Alt Region | ~2m | Region selection, project switch |
| 3: New Project | ~4m | Project creation in optimal region |
| 4: No Quota | N/A | Graceful failure, actionable guidance |
| 5: First-Time | ~5m | Complete onboarding |
| 6: Name Conflict | ~1m | Auto-retry with suffix |
| 7: Multi-Version | ~1m | Latest version auto-selected |
| 8: Anthropic | ~2m | Industry prompt, tenant info, REST API deploy |
Common Patterns
A: Quick Deploy Auth → Get Project → Check Region (✓) → Deploy
B: Region Select Auth → Get Project → Region (✗) → Query All → Select → Deploy
C: Full Onboarding Auth → No Projects → Create Project → Deploy
D: Error Recovery Deploy (✗) → Analyze → Fix → RetryRequirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/skills · .github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/preset · commit 0696600
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- microsoft/azure-skills
AutoGineer is not affiliated with or endorsed by Microsoft.
Related skills
Modal
K-Dense· Community
A serverless cloud platform for deploying and scaling Python applications, AI models, and GPU-accelerated workloads.
Cloud and DevOpsClaude CodeClaude appsOpenAI Codex+14 moreApache-2.0Low riskGitHub stars of the source repository56.4k
Cloud Infrastructure Network Engineer
Seth Hobson· Community
An expert network engineer persona specializing in cloud architecture, security, and performance optimization.
Cloud and DevOpsClaude CodeMITGitHub stars of the source repository42.3k
Deployment Validation Cloud Architect
Seth Hobson· Community
Expert cloud architect for multi-cloud infrastructure, cost optimization, and resilient system design.
Cloud and DevOpsClaude CodeMITGitHub stars of the source repository42.3k
Airflow Dag Patterns
Seth Hobson· Community
Best practices and design patterns for building and maintaining production-ready Apache Airflow DAGs.
Cloud and DevOpsClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
Cost Optimization
Seth Hobson· Community
A comprehensive framework for reducing cloud infrastructure costs across AWS, Azure, GCP, and OCI.
Cloud and DevOpsClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
Hybrid Cloud Networking
Seth Hobson· Community
A guide to configuring secure, high-performance network connectivity between on-premises infrastructure and major cloud providers.
Cloud and DevOpsClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download preset.zip