azure-networking
Configure Azure VNet, NSG, Load Balancer, and network topology.
Best use case
azure-networking is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Configure Azure VNet, NSG, Load Balancer, and network topology.
Teams using azure-networking should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-networking/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-networking Compares
| Feature / Agent | azure-networking | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Configure Azure VNet, NSG, Load Balancer, and network topology.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# azure-networking ## Microsoft Azure Agent Framework Integration This skill delegates all Azure provisioning and operations to the official Microsoft Agent Framework. ```bash # 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 | ## Reference - [Microsoft Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/) - [Agents](https://learn.microsoft.com/en-us/agent-framework/agents/) - [Workflows](https://learn.microsoft.com/en-us/agent-framework/workflows/) - [MCP Tools](https://learn.microsoft.com/en-us/agent-framework/agents/tools/hosted-mcp-tools) - [Azure Pricing Calculator](https://azure.microsoft.com/en-us/pricing/calculator/) - [Microsoft Entra ID](https://learn.microsoft.com/en-us/entra/identity/)
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