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Provides expert guidance for Azure Data Science Virtual Machines: troubleshooting, architecture, security, IaC deployment with Bicep/ARM, Key Vault, MLflow, GPU/Jupyter issues, and migration from Ubuntu 18.04 to 20.04.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-agent-skills:azure-data-science-vmThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides expert guidance for Azure Data Science Virtual Machines. Covers troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
This skill provides expert guidance for Azure Data Science Virtual Machines. Covers troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L35-L39 | Diagnosing and resolving common Azure Data Science VM issues, including VM creation, package/environment errors, Jupyter access, GPU/driver problems, and performance or connectivity failures. |
| Decision Making | L40-L44 | Guidance for upgrading Azure Data Science VMs from Ubuntu 18.04 to 20.04, including migration steps, compatibility considerations, and preserving tools/configurations. |
| Architecture & Design Patterns | L45-L50 | Designing scalable DSVM-based analytics environments, including architecture patterns, shared VM pools, team workflows, and resource management for data science teams. |
| Security | L51-L56 | Managing identities and credentials for Azure DSVMs, including shared identity setup, managed identities, and securing secrets with Azure Key Vault. |
| Configuration | L57-L69 | Details of all preinstalled tools, frameworks, languages, and images on Azure DSVMs, including ML/deep learning, data ingestion, dev/productivity tools, and release/version info. |
| Integrations & Coding Patterns | L70-L74 | Using MLflow on Azure DSVMs to track experiments, log metrics/artifacts, and integrate runs with Azure Machine Learning for centralized experiment management |
| Deployment | L75-L79 | How to deploy Azure Data Science VMs using infrastructure-as-code, including Bicep and ARM templates, parameters, and configuration best practices. |
| Topic | URL |
|---|---|
| Troubleshoot known issues on Azure DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/reference-known-issues?view=azureml-api-2 |
| Topic | URL |
|---|---|
| Migrate DSVM from Ubuntu 18.04 to 20.04 | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/ubuntu-upgrade?view=azureml-api-2 |
| Topic | URL |
|---|---|
| Design team analytics environments with DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-enterprise-overview?view=azureml-api-2 |
| Architect shared DSVM pools for analytics teams | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-pools?view=azureml-api-2 |
| Topic | URL |
|---|---|
| Configure common identity for multiple DSVMs | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity?view=azureml-api-2 |
| Secure DSVM credentials with managed identities and Key Vault | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-secure-access-keys?view=azureml-api-2 |
| Topic | URL |
|---|---|
| Track DSVM experiments with MLflow and Azure ML | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/how-to-track-experiments?view=azureml-api-2 |
| Topic | URL |
|---|---|
| Deploy Azure DSVM using Bicep templates | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tutorial-bicep?view=azureml-api-2 |
| Deploy Azure DSVM with ARM templates | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tutorial-resource-manager?view=azureml-api-2 |
npx claudepluginhub microsoftdocs/agent-skills --plugin azure-agent-skillsGuides Azure Copilot development for troubleshooting, architecture, security, and coding patterns. Useful for VM sizing, Bicep/Terraform generation, Cosmos DB configuration, and App Service debugging.
Provides deep-dive reference for Azure ML Workspace architecture, networking, private endpoints, compute clusters/instances, endpoints, managed identities, ACR/storage integration, az ml CLI/PowerShell commands, logs, debugging, and Terraform.
Routes Azure VM/VMSS queries to recommenders for sizing, pricing, autoscale, orchestration or troubleshooters for connectivity like RDP/SSH failures, NSG blocks, black screens.