Customize logo

Customize

OrganizationPopular
microsoft
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).

Overview

Publishermicrosoft
Repositoryazure-skills
Skill namecustomize
Stars
1.5K
Forks
246
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Customize AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/azure-skills.git /tmp/azure-skills
mkdir -p .claude/skills
cp -r /tmp/azure-skills/skills/microsoft-foundry/models/deploy-model/customize .claude/skills/customize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customize in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Customize on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Customize is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

PropertyDescription
FlowInteractive step-by-step guided deployment
CustomizationVersion, SKU, Capacity, RAI Policy, Advanced Options
SKU SupportGlobalStandard, Standard, ProvisionedManaged, DataZoneStandard
Best ForPrecise control over deployment configuration
AuthenticationAzure CLI (az login)
ToolsAzure 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

Featurecustomizepreset
FocusFull customization controlOptimal region selection
Version SelectionUser chooses from availableUses latest automatically
SKU SelectionUser chooses (GlobalStandard/Standard/PTU)GlobalStandard only
CapacityUser specifies exact valueAuto-calculated (50% of available)
RAI PolicyUser selects from optionsDefault policy only
RegionCurrent region first, falls back to all regions if no capacityChecks capacity across all regions upfront
Use CasePrecise deployment requirementsQuick 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).

PhaseActionKey Details
1. Verify AuthCheck az account show; prompt az login if neededVerify correct subscription is active
2. Get Project IDRead PROJECT_RESOURCE_ID env var or prompt userARM resource ID format required
3. Verify ProjectParse resource ID, call az cognitiveservices account showExtracts subscription, RG, account, project, region
4. Get ModelList models via az cognitiveservices account list-modelsUser selects from available or enters custom name
5. Select VersionQuery versions for chosen modelRecommend latest; user picks from list
6. Select SKUQuery model catalog + subscription quota, show only deployable SKUs⚠️ Never hardcode SKU lists — always query live data
7. Configure CapacityQuery capacity API, validate min/max/step, user enters valueCross-region fallback if no capacity in current region
8. Select RAI PolicyPresent content filter optionsDefault: Microsoft.DefaultV2
9. Advanced OptionsDynamic quota (GlobalStandard), priority processing (PTU), spilloverSKU-dependent availability
10. Upgrade PolicyChoose: OnceNewDefaultVersionAvailable / OnceCurrentVersionExpired / NoAutoUpgradeDefault: auto-upgrade on new default
11. Deployment NameAuto-generate unique name, allow custom overrideValidates format: ^[\w.-]{2,64}$
12. ReviewDisplay full config summary, confirm before proceedingUser approves or cancels
13. Deploy & Monitoraz cognitiveservices account deployment create, poll statusTimeout after 5 min; show endpoint + portal link

Error Handling

Common Issues and Resolutions

ErrorCauseResolution
Model not foundInvalid model nameList available models with az cognitiveservices account list-models
Version not availableVersion not supported for SKUSelect different version or SKU
Insufficient quotaCapacity > available quotaSkill auto-searches all regions; fails only if no region has quota
SKU not supportedSKU not available in regionCross-region fallback searches other regions automatically
Capacity out of rangeInvalid capacity valuePREVENTED: Skill validates min/max/step at input (Phase 7)
Deployment name existsName conflictAuto-incremented name generation
Authentication failedNot logged inRun az login
Permission deniedInsufficient permissionsAssign Cognitive Services Contributor role
Capacity query failsAPI/permissions/network errorDEPLOYMENT BLOCKED: Will not proceed without valid quota data

Troubleshooting Commands

bash
# 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).


Related Skills

  • 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Customize AI skill do?

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).

Why use Customize on TypingMind?

Because you install it once and use it with any model. Customize is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Customize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/microsoft-foundry/models/deploy-model/customize. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Customize?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Customize?

As many as you like. As long as a model supports skills, you can use Customize with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Customize AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇