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βš™ Needs: an Azure subscription (billed β€” compute, job runs, a…

Azure AI/ML for Python β€” manage Azure ML workspaces, jobs, models, datasets, compute, pipelines

πŸ’³ Paid API required β€” Azure subscription billed. Work the Azure Machine Learning SDK v2 in Python: MLClient auth, workspaces, data assets, model registry, compute clusters, command jobs, pipelines, environments, datastores

At a glance
What
πŸ’³ Paid API required β€” Azure subscription billed. Work the Azure Machine Learning SDK v2 in Python: MLClient auth, workspaces, data assets, model registry, compute clusters, command jobs, pipelines, environments, datastores
Cost
Free
Needs
an Azure subscription (billed β€” compute, job runs, and storage cost real money), Python, and your Azure credentials (never hardcode them)
Install
Copy the installer prompt below into your Muse β€” your agent does the rest.

Version:

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Created by: @sickn33
⌁

Install

Prerequisites: an Azure subscription (billed β€” compute, job runs, and storage cost real money), Python, and your Azure credentials (never hardcode them) Install "Azure AI/ML for Python β€” manage Azure ML workspaces, jobs, models, datasets, compute, pipelines" for me. It gives my agent @sickn33's Azure ML SDK v2 reference: install azure-ai-ml, authenticate MLClient via DefaultAzureCredential (subscription ID, resource group, workspace name from environment variables or a config file), create and list workspaces, register data assets and models, create and scale compute clusters with idle scale-down, submit and stream command jobs, build @dsl.pipeline multi-step workflows, create custom environments, and manage datastores β€” following the cost-conscious best practices (versioning, idle scale-down, tagging). MIT licensed. Repository: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/azure-ai-ml-py/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via environment variables, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "azure-ai-ml-py". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. set my Azure env vars in the vault, create the workspace, configure idle scale-down before submitting jobs). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.

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Questions

How do I install a build?

Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you β€” no manual configuration.

Where does my money go?

Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.

What does the βœ“ next to a creator’s name mean?

It means we confirmed the identity of the person behind the listing. It says nothing about the code itself β€” always check a build before installing it.