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Azure Aigateway

Official
microsoft·vlatest·cloud·MIT

Securely deploy and manage Azure OpenAI and other AI models via a centralized gateway for consistent access control.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add microsoft/azure-skills --skill azure-aigateway -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

microsoft

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned microsoft/azure-skills at 9bb168c343.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
9bb168c343
Manifest
skills/azure-aigateway/SKILL.md
Scanned
18 files, 2,160 lines
Copies in repo
2 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The Microsoft Azure AI Gateway skill enables seamless integration of AI models and services within the Azure cloud ecosystem. It provides a unified interface to deploy, manage, and scale AI workloads across various Azure resources, ensuring efficient communication between different components.

When to use it

  • Deploying machine learning models at scale in production environments on Azure.
  • Integrating multiple AI services (e.g., Vision, NLP) into a single workflow using Azure infrastructure.
  • Managing API endpoints and model versions for AI applications hosted on Azure.

Key capabilities

  • Unified deployment of AI models across Azure resources.
  • Integration with Azure Machine Learning and Cognitive Services.
  • Scalable management of AI workloads through REST APIs.

Example prompts

  • "Deploy a new version of my image classification model using the Azure AI Gateway."
  • "Set up an API endpoint for my NLP service on Azure."
  • "Monitor performance metrics of deployed AI models in real time."

Tips & gotchas

  • Ensure your Azure subscription is active and has sufficient resources before deployment.
  • Model compatibility with Azure services may require additional configuration or conversion.