Azure Skills Plugin is an agent plugin that packages Azure-specific guidance with MCP server configurations for carrying out Azure work. Coding agents use it to plan, deploy, troubleshoot, monitor, govern, and optimize Azure applications and services. The catalogue entries are the plugin's skills, instructions, and execution integrations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add microsoft/azure-skills --skill presetgit clone --depth 1 https://github.com/microsoft/azure-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/azure-skills/preset)<a href="https://agentmods.dev/skills/microsoft/azure-skills/preset"><img src="https://agentmods.dev/badge/skills/microsoft/azure-skills/preset.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.01067 |
| Opus 5 | $0.00049 | $0.00534 |
| Sonnet 5 | $0.00020 | $0.00213 |
| Haiku 4.5 | $0.00010 | $0.00107 |
Grade A, and why
preset scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to preset — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Format:
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 |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 104 lines · 99 tokens per session scan A f4e44bf4eac0
preset is a skill published in the GitHub repository microsoft/azure-skills (1,459 stars, last pushed yesterday), licensed MIT. It adds 99 tokens to every session and 1,067 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to preset, differing in 0 lines, and is treated as a copy.
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