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Azure Ai Ml Py

Listed
sickn33·vlatest·cloud·MIT

Helps with Azure, AI, machine learning as part of deploying and managing cloud infrastructure workflows.

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 sickn33/agentic-awesome-skills --skill azure-ai-ml-py -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

sickn33

Taken from the source listing. We haven't verified who controls this account.

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/azure-ai-ml-py/SKILL.md
Scanned
0 files
Copies in repo
3 — 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

This skill allows AI agents to interact with Azure Machine Learning using Python. It enables tasks like submitting training jobs, deploying models as endpoints, and managing datasets within an Azure ML workspace. The agent can leverage existing Python scripts or generate new ones for these operations, streamlining machine learning workflows.

When to use it

  • Automated Model Training: Schedule and execute model training runs in Azure Machine Learning based on data updates or performance metrics.
  • Model Deployment & Management: Deploy trained models as real-time endpoints within Azure ML and manage their versions.
  • Data Exploration & Preparation: Create, register, and query datasets stored in an Azure ML workspace for machine learning projects.
  • Workflow Orchestration: Automate complex machine learning pipelines involving data preprocessing, training, evaluation, and deployment steps.

Key capabilities

  • Submit training jobs using Python scripts.
  • Deploy models as endpoints.
  • Manage datasets within an Azure Machine Learning workspace.
  • Interact with existing Python code for ML tasks.

Example prompts

  • "Run the train_model.py script in my Azure ML workspace."
  • "Deploy the model my_best_model as a real-time endpoint."
  • "Register this CSV file as a dataset named 'customer_data' in my Azure ML workspace."

Tips & gotchas

  • Requires an active Azure subscription and access to an Azure Machine Learning workspace.
  • The agent needs appropriate permissions within the Azure ML workspace to perform actions like submitting jobs or deploying models.