Preset

OrganizationPopular

microsoft

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).

Overview

Publisher	microsoft
Repository	`azure-skills`
Skill name	`preset`
Stars	1.5K
Forks	246
Bundled files	3
License	MIT
Links	Skill source Repository

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/microsoft/azure-skills/tree/main/skills/microsoft-foundry/models/deploy-model/preset

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/preset .claude/skills/preset

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Preset 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 Preset 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 Preset 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.

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_ID env 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 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.

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

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 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
azure-cost-estimation - Estimate costs for Azure deployments
azure-validate - Validate Azure infrastructure before deployment

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 Preset AI skill do?

Why use Preset on TypingMind?

Because you install it once and use it with any model. Preset 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 Preset 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/preset. 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 Preset?

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 Preset?

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

Is the Preset 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.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deploy Model to Optimal Region

What This Skill Does

Prerequisites

Quick Workflow

Fast Path (Current Region Has Capacity)

Alternative Region Path (No Capacity)

Deployment Phases

Error Handling

Advanced Usage

Notes

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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