/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,
$ npx -y skills add microsoft/GitHub-Copilot-for-Azure --skill customize --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/customize
Context preview
The summary Claude sees to decide when to auto-load this skill.
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,
SKILL.md
customize.SKILL.mdname: customize
description: "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)."
license: MIT
metadata:
author: Microsoft
version: "1.0.1"
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](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 | Reso
Read more
name: customize description: "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)." license: MIT metadata: author: Microsoft version: "1.0.1"
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](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 | Reso
GitHub Copilot for Azure is a set of extensions for Visual Studio, VS Code, and Claude Code designed to streamline the process of developing for Azure.
Repo: microsoft/GitHub-Copilot-for-Azure
Other skills on github-copilot-for-azure.
- /airunway-aks-setup
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to
Open skill - /appinsights-instrumentation
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation
Open skill - /azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe,
Open skill - /azure-aigateway
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI
Open skill - /azure-app-onboard-prereq
Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be
Open skill - /azure-app-onboard
End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a
Open skill

