About azure-iam
azure-iam is a skill for planning Azure architecture and operations, including service selection, cost modeling, permissions, and infrastructure-as-code choices. The Microsoft Agent Framework handles execution and supports agent workflows and MCP connections.
Key features
- 01Azure service selection and trade-off analysis
- 02Cost estimation and optimization
- 03Entra ID and RBAC pre-flight checks
- 04Bicep, ARM, and Terraform AzureRM guidance
- 05Graph-based multi-agent workflow design
- 06Human approval checkpoints
Use cases
- 01Compare Azure service options
- 02Estimate and optimize Azure costs
- 03Plan Azure infrastructure as code
- 04Design multi-agent workflows
MCP support
The Microsoft Agent Framework provides a native MCP client for connecting to external services. Its agents can 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-iamAfter installing, type/azure-iamin Claude Code to confirm it loaded.
Details
From the project READMEazure-iam
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 |