About azure-vm
azure-vm provides architecture guidance for Azure provisioning and operations, while delegating execution to Microsoft's Agent Framework. It is intended for Azure planning, with guidance on service choices, costs, permissions, infrastructure as code, and multi-agent workflows.
Key features
- 01Azure service selection guidance
- 02Cost estimation and optimization
- 03Entra ID and RBAC pre-flight guidance
- 04Bicep, ARM, and Terraform AzureRM planning
- 05Multi-agent workflow design
Use cases
- 01Compare Azure service options
- 02Estimate and optimize Azure costs
- 03Check identity and permission requirements
- 04Plan Azure infrastructure as code
MCP support
The Microsoft Agent Framework used by this skill includes a native MCP client for integrating external services. Its agents can also use tools and MCP servers.
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ alphaonedev/ openclaw-graph --skill azure-vmAfter installing, type/azure-vmin Claude Code to confirm it loaded.
Details
From the project READMEazure-vm
Microsoft Azure Agent Framework Integration
This skill delegates all Azure provisioning and operations to the official Microsoft Agent Framework.
# Python
pip install semantic-kernel
# .NET / C#
dotnet add package Microsoft.SemanticKernel.Agents
Docs: https://learn.microsoft.com/en-us/agent-framework/overview/
Use the Microsoft Agent Framework for all Azure provisioning and operational actions. This skill provides architecture guidance, cost modeling, and pre-flight requirements — the framework handles execution.
Architecture Guidance
Consult this skill for:
- Azure service selection and trade-off analysis
- Cost estimation and optimization strategy
- Pre-flight Entra ID / RBAC permission requirements
- IaC approach (Bicep vs ARM vs Terraform AzureRM)
- Integration patterns with Microsoft 365 and other Azure services
- Multi-agent workflow design using Agent Framework graph-based runtime
Agent Framework Capabilities
| Capability | Description |
|---|---|
| Agents | Individual LLM agents with tool + MCP server support |
| Workflows | Graph-based multi-agent pipelines with checkpointing |
| Providers | Azure OpenAI, OpenAI, Anthropic, Ollama, and more |
| MCP | Native MCP client for external service integration |
| Human-in-the-loop | Built-in approval and intervention checkpoints |