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 agentmods add skills/microsoft/azure-skills/customizenpx skills add microsoft/azure-skills --skill customizegit 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/customize)<a href="https://agentmods.dev/skills/microsoft/azure-skills/customize"><img src="https://agentmods.dev/badge/skills/microsoft/azure-skills/customize.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 | $0.00116 | $0.01947 |
| Opus 5 | $0.00058 | $0.00974 |
| Sonnet 5 | $0.00023 | $0.00389 |
| Haiku 4.5 | $0.00012 | $0.00195 |
Grade A, and why
customize 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- customize — 95% identical, 94 lines differ
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_IDenvironment variable
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.
- 4d ago First seen · 168 lines · 116 tokens per session scan A d0a25090ffc3
customize is a skill published in the GitHub repository microsoft/azure-skills (1,448 stars, last pushed today), licensed MIT. It adds 116 tokens to every session and 1,947 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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