Customize

MicrosoftOfficialCreated by Microsoft

Skill
Workflow
Cloud and DevOps
MIT
No issues found

From the author

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).

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, step-by-step process for deploying Azure OpenAI models. It enables users to specify exact model versions, select from various SKUs, set capacity, configure content filtering through RAI policies, and manage advanced deployment options like dynamic quota and spillover. The workflow includes automated checks for authentication, project verification, and capacity availability, with built-in cross-region fallback logic if capacity is unavailable in the primary region.

What it does

Guides users through a 14-phase deployment process for Azure OpenAI models.
Allows selection of specific model versions and deployment SKUs.
Configures capacity, RAI policies, and advanced features like priority processing.
Performs automated validation of project resources and subscription quotas.
Supports cross-region fallback if capacity is not available in the target region.
Generates and validates deployment names for consistency.

When to use it

When you require precise control over your Azure OpenAI model deployment configuration.
When you need to select specific model versions rather than the latest default.
When configuring advanced deployment features like PTU, spillover, or dynamic quota.
When you need to manually select or verify available SKUs and capacity.

Advantages

Provides granular control over every aspect of the deployment process.
Reduces configuration errors through automated validation and live data queries.
Simplifies complex tasks like cross-region capacity searching.
Ensures deployment parameters meet specific project requirements.

Usage examples

“Deploy gpt-4o with ProvisionedManaged SKU and 100 PTU capacity.”: The agent initiates the guided flow, prompts for the project resource ID, validates the SKU and capacity requirements, and executes the deployment.
“I need to customize my model deployment with specific RAI policies.”: The agent guides you through the RAI policy selection phase, presenting available content filter options for your chosen model.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/microsoft/customize/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip customize.zip -d .agents/skills
```

Documentation by Microsoft

Customize Model Deployment

Interactive guided workflow for deploying Azure OpenAI models with full customization control over version, SKU, capacity, content filtering, and advanced options.

Quick Reference

Property	Description
Flow	Interactive step-by-step guided deployment
Customization	Version, SKU, Capacity, RAI Policy, Advanced Options
SKU Support	GlobalStandard, Standard, ProvisionedManaged, DataZoneStandard
Best For	Precise control over deployment configuration
Authentication	Azure CLI (`az login`)
Tools	Azure CLI, MCP tools (optional)

When to Use This Skill

Use this skill when you need precise control over deployment configuration:

✅ Choose specific model version (not just latest)
✅ Select deployment SKU (GlobalStandard vs Standard vs PTU)
✅ Set exact capacity within available range
✅ Configure content filtering (RAI policy selection)
✅ Enable advanced features (dynamic quota, priority processing, spillover)
✅ PTU deployments (Provisioned Throughput Units)

Alternative: Use preset for quick deployment to the best available region with automatic configuration.

Comparison: customize vs preset

Feature	customize	preset
Focus	Full customization control	Optimal region selection
Version Selection	User chooses from available	Uses latest automatically
SKU Selection	User chooses (GlobalStandard/Standard/PTU)	GlobalStandard only
Capacity	User specifies exact value	Auto-calculated (50% of available)
RAI Policy	User selects from options	Default policy only
Region	Current region first, falls back to all regions if no capacity	Checks capacity across all regions upfront
Use Case	Precise deployment requirements	Quick deployment to best region

Prerequisites

Azure subscription with Cognitive Services Contributor or Owner role
Microsoft Foundry project resource ID (format: /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project})
Azure CLI installed and authenticated (az login)
Optional: Set PROJECT_RESOURCE_ID environment variable

Workflow Overview

Complete Flow (14 Phases)

1. Verify Authentication
2. Get Project Resource ID
3. Verify Project Exists
4. Get Model Name (if not provided)
5. List Model Versions → User Selects
6. List SKUs for Version → User Selects
7. Get Capacity Range → User Configures
   7b. If no capacity: Cross-Region Fallback → Query all regions → User selects region/project
8. List RAI Policies → User Selects
9. Configure Advanced Options (if applicable)
10. Configure Version Upgrade Policy
11. Generate Deployment Name
12. Review Configuration
13. Execute Deployment & Monitor

Fast Path (Defaults)

If user accepts all defaults (latest version, GlobalStandard SKU, recommended capacity, default RAI policy, standard upgrade policy), deployment completes in ~5 interactions.

Phase Summaries

⚠️ MUST READ: Before executing any phase, load references/customize-workflow.md for the full scripts and implementation details. The summaries below describe what each phase does — the reference file contains the how (CLI commands, quota patterns, capacity formulas, cross-region fallback logic).

Phase	Action	Key Details
1. Verify Auth	Check `az account show`; prompt `az login` if needed	Verify correct subscription is active
2. Get Project ID	Read `PROJECT_RESOURCE_ID` env var or prompt user	ARM resource ID format required
3. Verify Project	Parse resource ID, call `az cognitiveservices account show`	Extracts subscription, RG, account, project, region
4. Get Model	List models via `az cognitiveservices account list-models`	User selects from available or enters custom name
5. Select Version	Query versions for chosen model	Recommend latest; user picks from list
6. Select SKU	Query model catalog + subscription quota, show only deployable SKUs	⚠️ Never hardcode SKU lists — always query live data
7. Configure Capacity	Query capacity API, validate min/max/step, user enters value	Cross-region fallback if no capacity in current region
8. Select RAI Policy	Present content filter options	Default: `Microsoft.DefaultV2`
9. Advanced Options	Dynamic quota (GlobalStandard), priority processing (PTU), spillover	SKU-dependent availability
10. Upgrade Policy	Choose: OnceNewDefaultVersionAvailable / OnceCurrentVersionExpired / NoAutoUpgrade	Default: auto-upgrade on new default
11. Deployment Name	Auto-generate unique name, allow custom override	Validates format: `^[\w.-]{2,64}$`
12. Review	Display full config summary, confirm before proceeding	User approves or cancels
13. Deploy & Monitor	`az cognitiveservices account deployment create`, poll status	Timeout after 5 min; show endpoint + portal link

Error Handling

Common Issues and Resolutions

Error	Cause	Resolution
Model not found	Invalid model name	List available models with `az cognitiveservices account list-models`
Version not available	Version not supported for SKU	Select different version or SKU
Insufficient quota	Capacity > available quota	Skill auto-searches all regions; fails only if no region has quota
SKU not supported	SKU not available in region	Cross-region fallback searches other regions automatically
Capacity out of range	Invalid capacity value	PREVENTED: Skill validates min/max/step at input (Phase 7)
Deployment name exists	Name conflict	Auto-incremented name generation
Authentication failed	Not logged in	Run `az login`
Permission denied	Insufficient permissions	Assign Cognitive Services Contributor role
Capacity query fails	API/permissions/network error	DEPLOYMENT BLOCKED: Will not proceed without valid quota data

Troubleshooting Commands

# Check deployment status
az cognitiveservices account deployment show --name <account> --resource-group <rg> --deployment-name <name>

# List all deployments
az cognitiveservices account deployment list --name <account> --resource-group <rg> -o table

# Check quota usage
az cognitiveservices usage list --name <account> --resource-group <rg>

# Delete failed deployment
az cognitiveservices account deployment delete --name <account> --resource-group <rg> --deployment-name <name>

Selection Guides & Advanced Topics

For SKU comparison tables, PTU sizing formulas, and advanced option details, load references/customize-guides.md.

SKU selection: GlobalStandard (production/HA) → Standard (dev/test) → ProvisionedManaged (high-volume/guaranteed throughput) → DataZoneStandard (data residency).

Capacity: TPM-based SKUs range from 1K (dev) to 100K+ (large production). PTU-based use formula: (Input TPM × 0.001) + (Output TPM × 0.002) + (Requests/min × 0.1).

Advanced options: Dynamic quota (GlobalStandard only), priority processing (PTU only, extra cost), spillover (overflow to backup deployment).

preset - Quick deployment to best region with automatic configuration
microsoft-foundry - Parent skill for all Microsoft Foundry operations
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance
rbac - Manage permissions and access control

Notes

Set PROJECT_RESOURCE_ID environment variable to skip prompt
Not all SKUs available in all regions; capacity varies by subscription/region/model
Custom RAI policies can be configured in Azure Portal
Automatic version upgrades occur during maintenance windows
Use Azure Monitor and Application Insights for production deployments

Files

ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
EXAMPLES.md4.3 KiB
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md8.8 KiB
references/customize-guides.md3.4 KiB
references/customize-workflow.md13.1 KiB

EXAMPLES.md

customize Examples

Example 1: Basic Deployment with Defaults

Scenario: Deploy gpt-4o accepting all defaults for quick setup. Config: gpt-4o / GlobalStandard / 10K TPM / Dynamic Quota enabled Result: Deployment gpt-4o created in ~2-3 min with auto-upgrade enabled.

Example 2: Production Deployment with Custom Capacity

Scenario: Deploy gpt-4o for production with high throughput. Config: gpt-4o / GlobalStandard / 50K TPM / Dynamic Quota / Name: gpt-4o-production Result: 50K TPM (500 req/10s). Suitable for moderate-to-high traffic production apps.

Example 3: PTU Deployment for High-Volume Workload

Scenario: Deploy gpt-4o with reserved capacity (PTU) for predictable workload. Config: gpt-4o / ProvisionedManaged / 200 PTU (min 50, max 1000) / Priority Processing enabled PTU sizing: 40K input + 20K output tokens/min → ~100 PTU estimated → 200 PTU recommended (2x headroom) Result: Guaranteed throughput, fixed monthly cost. Use case: customer service bots, document pipelines.

Example 4: Development Deployment with Standard SKU

Scenario: Deploy gpt-4o-mini for dev/testing with minimal cost. Config: gpt-4o-mini / Standard / 1K TPM / Name: gpt-4o-mini-dev Result: 1K TPM, 10 req/10s. Minimal pay-per-use cost for development and prototyping.

Example 5: Spillover Configuration

Scenario: Deploy gpt-4o with spillover to handle peak load overflow. Config: gpt-4o / GlobalStandard / 20K TPM / Dynamic Quota / Spillover → gpt-4o-backup Result: Primary handles up to 20K TPM; overflow auto-redirects to backup deployment.

Example 6: Anthropic Model Deployment (claude-sonnet-4-6)

Scenario: Deploy claude-sonnet-4-6 with customized settings. Config: claude-sonnet-4-6 / GlobalStandard / capacity 1 (MaaS) / Industry: Healthcare / No RAI policy (Anthropic manages content filtering) Result: User selected "Healthcare" as industry → tenant country code (US) and org name fetched automatically → deployed via ARM REST API with modelProviderData in ~2 min.

Comparison Matrix

Scenario	Model	SKU	Capacity	Dynamic Quota	Priority	Spillover	Use Case
Ex 1	gpt-4o	GlobalStandard	10K TPM	✓	-	-	Quick setup
Ex 2	gpt-4o	GlobalStandard	50K TPM	✓	-	-	Production
Ex 3	gpt-4o	ProvisionedManaged	200 PTU	-	✓	-	Predictable workload
Ex 4	gpt-4o-mini	Standard	1K TPM	-	-	-	Dev/testing
Ex 5	gpt-4o	GlobalStandard	20K TPM	✓	-	✓	Peak load
Ex 6	claude-sonnet-4-6	GlobalStandard	1 (MaaS)	-	-	-	Anthropic model

Common Patterns

Dev → Staging → Production

Stage	Model	SKU	Capacity	Extras
Dev	gpt-4o-mini	Standard	1K TPM	—
Staging	gpt-4o	GlobalStandard	10K TPM	—
Production	gpt-4o	GlobalStandard	50K TPM	Dynamic Quota + Spillover

Cost Optimization

High priority: gpt-4o, ProvisionedManaged, 100 PTU, Priority Processing
Low priority: gpt-4o-mini, Standard, 5K TPM

Tips and Best Practices

Capacity: Start conservative → monitor with Azure Monitor → scale gradually → use spillover for peaks.

SKU Selection: Standard for dev → GlobalStandard + dynamic quota for variable production → ProvisionedManaged (PTU) for predictable load.

Cost: Right-size capacity; use gpt-4o-mini where possible (80-90% accuracy at lower cost); enable dynamic quota; consider PTU for consistent high-volume.

Versions: Auto-upgrade recommended; test new versions in staging first; pin only if compatibility requires it.

Content Filtering: Start with DefaultV2; use custom policies only for specific needs; monitor filtered requests.

Troubleshooting

Problem	Solution
`QuotaExceeded`	Check usage with `az cognitiveservices usage list`, reduce capacity, try different SKU, check other regions, or use the quota skill to request an increase
Version not available for SKU	Check `az cognitiveservices account list-models --query "[?name=='gpt-4o'].version"`, use latest
Deployment name exists	Skill auto-generates unique name (e.g., `gpt-4o-2`), or specify custom name

Requirements 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/customize · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

Also distributed in

microsoft/azure-skills

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download customize.zip