About azure-ai-services
azure-ai-services provides architecture guidance for Azure service selection and operations planning, while Microsoft Agent Framework handles execution. It is intended for teams planning costs, permissions, infrastructure-as-code approaches, and multi-agent workflows.
Key features
- 01Azure service selection and trade-off analysis
- 02Cost estimation and optimization strategy
- 03Entra ID and RBAC permission preflight
- 04Guidance on Bicep, ARM, and Terraform AzureRM
- 05Graph-based multi-agent workflow design
Use cases
- 01Compare Azure services and plan an architecture
- 02Estimate costs and identify optimization strategies
- 03Check Entra ID and RBAC permissions before deployment
- 04Design multi-agent pipelines with checkpoints
MCP support
The skill delegates Azure provisioning and operations to Microsoft Agent Framework. The framework includes an MCP client for external services, and its agents support tools and MCP servers.
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/azure-ai-servicesin Claude Code to confirm it loaded.
Details
From the project READMEazure-ai-services
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 |