azure-architecture-autopilot
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
Best use case
azure-architecture-autopilot is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
Teams using azure-architecture-autopilot 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-architecture-autopilot/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-architecture-autopilot Compares
| Feature / Agent | azure-architecture-autopilot | 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?
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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.
Related Guides
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
SKILL.md Source
# Azure Architecture Builder
A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.
The diagram engine is **embedded within the skill** (`scripts/` folder).
No `pip install` needed — it directly uses the bundled Python scripts
to generate interactive HTML diagrams with 605+ official Azure icons.
Ready to use immediately without network access or package installation.
## Automatic User Language Detection
**🚨 Detect the language of the user's first message and provide all subsequent responses in that language. This is the highest-priority principle.**
- If the user writes in Korean → respond in Korean
- If the user writes in English → **respond in English** (ask_user, progress updates, reports, Bicep comments — all in English)
- The instructions and examples in this document are written in English, and **all user-facing output must match the user's language**
**⚠️ Do not copy examples from this document verbatim to the user.**
Use only the structure as reference, and adapt text to the user's language.
## Tool Usage Guide (GHCP Environment)
| Feature | Tool Name | Notes |
|---------|-----------|-------|
| Fetch URL content | `web_fetch` | For MS Docs lookups, etc. |
| Web search | `web_search` | URL discovery |
| Ask user | `ask_user` | `choices` must be a string array |
| Sub-agents | `task` | explore/task/general-purpose |
| Shell command execution | `powershell` | Windows PowerShell |
> All sub-agents (explore/task/general-purpose) cannot use `web_fetch` or `web_search`.
> Fact-checking that requires MS Docs lookups must be performed **directly by the main agent**.
## External Tool Path Discovery
`az`, `python`, `bicep`, etc. are often not on PATH.
**Discover once before starting a Phase and cache the result. Do not re-discover every time.**
> **⚠️ Do not use `Get-Command python`** — risk of Windows Store alias.
> Direct filesystem discovery (`$env:LOCALAPPDATA\Programs\Python`) takes priority.
az CLI path:
```powershell
$azCmd = $null
if (Get-Command az -ErrorAction SilentlyContinue) { $azCmd = 'az' }
if (-not $azCmd) {
$azExe = Get-ChildItem -Path "$env:ProgramFiles\Microsoft SDKs\Azure\CLI2\wbin", "$env:LOCALAPPDATA\Programs\Azure CLI\wbin" -Filter "az.cmd" -ErrorAction SilentlyContinue | Select-Object -First 1 -ExpandProperty FullName
if ($azExe) { $azCmd = $azExe }
}
```
Python path + embedded diagram engine: refer to the diagram generation section in `references/phase1-advisor.md`.
## Progress Updates Required
Use blockquote + emoji + bold format:
```markdown
> **⏳ [Action]** — [Reason]
> **✅ [Complete]** — [Result]
> **⚠️ [Warning]** — [Details]
> **❌ [Failed]** — [Cause]
```
## Parallel Preload Principle
While waiting for user input via `ask_user`, preload information needed for the next step in parallel.
| ask_user Question | Preload Simultaneously |
|---|---|
| Project name / scan scope | Reference files, MS Docs, Python path discovery, **diagram module path verification** |
| Model/SKU selection | MS Docs for next question choices |
| Architecture confirmation | `az account show/list`, `az group list` |
| Subscription selection | `az group list` |
---
## Path Branching — Automatically Determined by User Request
### Path A: New Design (New Build)
**Trigger**: "create", "set up", "deploy", "build", etc.
```
Phase 1 (references/phase1-advisor.md) — Interactive architecture design + diagram
↓
Phase 2 (references/bicep-generator.md) — Bicep code generation
↓
Phase 3 (references/bicep-reviewer.md) — Code review + compilation verification
↓
Phase 4 (references/phase4-deployer.md) — validate → what-if → deploy
```
### Path B: Existing Analysis + Modification (Analyze & Modify)
**Trigger**: "analyze", "current resources", "scan", "draw a diagram", "show my infrastructure", etc.
```
Phase 0 (references/phase0-scanner.md) — Existing resource scan + diagram
↓
Modification conversation — "What would you like to change here?" (natural language modification request → follow-up questions)
↓
Phase 1 (references/phase1-advisor.md) — Confirm modifications + update diagram
↓
Phase 2~4 — Same as above
```
### When Path Determination Is Ambiguous
Ask the user directly:
```
ask_user({
question: "What would you like to do?",
choices: [
"Design a new Azure architecture (Recommended)",
"Analyze + modify existing Azure resources"
]
})
```
---
## Phase Transition Rules
- Each Phase reads and follows the instructions in its corresponding `references/*.md` file
- When transitioning between Phases, always inform the user about the next step
- Do not skip Phases (especially the what-if between Phase 3 → Phase 4)
- **🚨 Required condition for Phase 1 → Phase 2 transition**: `01_arch_diagram_draft.html` must have been generated using the embedded diagram engine and shown to the user. **Do not proceed to Bicep generation without a diagram.** Completing spec collection alone does not mean Phase 1 is done — Phase 1 includes diagram generation + user confirmation.
- Modification request after deployment → return to Phase 1, not Phase 0 (Delta Confirmation Rule)
## Service Coverage & Fallback
### Optimized Services
Microsoft Foundry, Azure OpenAI, AI Search, ADLS Gen2, Key Vault, Microsoft Fabric, Azure Data Factory, VNet/Private Endpoint, AML/AI Hub
### Other Azure Services
All supported — MS Docs are automatically consulted to generate at the same quality standard.
**Do not send messages that cause user anxiety such as "out of scope" or "best-effort".**
### Stable vs Dynamic Information Handling
| Category | Handling Method | Examples |
|----------|----------------|---------|
| **Stable** | Reference files first | `isHnsEnabled: true`, PE triple set |
| **Dynamic** | **Always fetch MS Docs** | API version, model availability, SKU, region |
## Quick Reference
| File | Role |
|------|------|
| `references/phase0-scanner.md` | Existing resource scan + relationship inference + diagram |
| `references/phase1-advisor.md` | Interactive architecture design + fact checking |
| `references/bicep-generator.md` | Bicep code generation rules |
| `references/bicep-reviewer.md` | Code review checklist |
| `references/phase4-deployer.md` | validate → what-if → deploy |
| `references/service-gotchas.md` | Required properties, PE mappings |
| `references/azure-dynamic-sources.md` | MS Docs URL registry |
| `references/azure-common-patterns.md` | PE/security/naming patterns |
| `references/ai-data.md` | AI/Data service guide |Related Skills
terraform-azurerm-set-diff-analyzer
Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.
gtm-partnership-architecture
Build and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
azure-static-web-apps
Helps create, configure, and deploy Azure Static Web Apps using the SWA CLI. Use when deploying static sites to Azure, setting up SWA local development, configuring staticwebapp.config.json, adding Azure Functions APIs to SWA, or setting up GitHub Actions CI/CD for Static Web Apps.
azure-role-selector
When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role.
azure-resource-visualizer
Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. Use this skill when the user asks for a diagram of their Azure resources or help in understanding how the resources relate to each other.
azure-devops-cli
Manage Azure DevOps resources via CLI including projects, repos, pipelines, builds, pull requests, work items, artifacts, and service endpoints. Use when working with Azure DevOps, az commands, devops automation, CI/CD, or when user mentions Azure DevOps CLI.
azure-deployment-preflight
Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
architecture-blueprint-generator
Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.
azure-resource-health-diagnose
Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems.
azure-pricing
Fetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com) and estimates Copilot Studio agent credit consumption. Use when the user asks about the cost of any Azure service, wants to compare SKU prices, needs pricing data for a cost estimate, mentions Azure pricing, Azure costs, Azure billing, or asks about Copilot Studio pricing, Copilot Credits, or agent usage estimation. Covers compute, storage, networking, databases, AI, Copilot Studio, and all other Azure service families.
write-coding-standards-from-file
Write a coding standards document for a project using the coding styles from the file(s) and/or folder(s) passed as arguments in the prompt.
workiq-copilot
Guides the Copilot CLI on how to use the WorkIQ CLI/MCP server to query Microsoft 365 Copilot data (emails, meetings, docs, Teams, people) for live context, summaries, and recommendations.