skills/azure-data-science-vm/SKILL.md
Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when managing DSVM images/tools, IaC deployment (Bicep/ARM), Key Vault secrets, MLflow, or GPU/Jupyter issues, and other Azure Data Science Virtual Machines related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).
npx skillsauth add microsoftdocs/agent-skills azure-data-science-vmInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 | |-------|-----| | Use preinstalled ML tools on Azure DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-data-science?view=azureml-api-2 | | Check deep learning frameworks on Azure DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks?view=azureml-api-2 | | Identify development tools available on DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-development?view=azureml-api-2 | | Use data ingestion tools on Azure DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-ingestion?view=azureml-api-2 | | Review programming languages preinstalled on DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-languages?view=azureml-api-2 | | Leverage productivity tools on Azure DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-productivity?view=azureml-api-2 | | Reference tools installed on Ubuntu DSVM | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/reference-ubuntu-vm?view=azureml-api-2 | | Review Azure DSVM release changes and versions | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/release-notes?view=azureml-api-2 | | Review preinstalled tools on Azure DSVM images | https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/tools-included?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 |
tools
Expert knowledge for Microsoft Foundry (aka Azure AI Foundry) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Foundry agents with IQ retrieval, Entra RBAC, Azure OpenAI, M365/Teams publishing, or MCP tools, and other Microsoft Foundry related development tasks. Not for Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools).
tools
Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including best practices, configuration, and integrations & coding patterns. Use when compiling HF models with Olive, managing local models via CLI, or building chat, embeddings, or transcription apps, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Tools (use microsoft-foundry-tools), Azure Local (use azure-local).
tools
Expert knowledge for Microsoft Foundry Classic (aka Azure AI Foundry classic) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Foundry agents, routing Azure OpenAI models, integrating tools/RAG, securing endpoints, or deploying hubs, and other Microsoft Foundry Classic related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools).
development
Expert knowledge for Azure Web PubSub development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using WebSockets/MQTT, Socket.IO, Functions bindings, geo-replication, or Premium autoscale in Web PubSub, and other Azure Web PubSub related development tasks. Not for Azure SignalR Service (use azure-signalr-service), Azure Event Hubs (use azure-event-hubs), Azure Service Bus (use azure-service-bus), Azure Relay (use azure-relay).