azure-architecture-autopilot
github/awesome-copilot
Design and deploy Azure infrastructure from natural language, or analyze existing resources and auto-generate architecture diagrams.
What is azure-architecture-autopilot?
This skill enables you to describe Azure infrastructure in plain language and receive interactive architecture diagrams, Bicep templates, and deployment automation. Use it to design new Azure systems, analyze and visualize existing infrastructure, or refine architectures through conversation.
- Generate interactive HTML architecture diagrams with 605+ official Azure icons from natural language descriptions
- Scan and visualize existing Azure resources and their relationships
- Auto-generate Bicep Infrastructure-as-Code templates from architecture specifications
- Validate, preview (what-if), and deploy Bicep templates to Azure subscriptions
- Support natural language modification requests to refine architectures iteratively
- Detect user language and respond in the same language throughout the workflow
How to install azure-architecture-autopilot
npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot- Azure CLI (az) installed and authenticated to your Azure subscription
- Python 3.7+ (for embedded diagram generation engine)
- PowerShell (for tool discovery and command execution)
- Access to at least one Azure subscription and resource group
How to use azure-architecture-autopilot
- 1.Describe your desired Azure architecture in natural language, or ask to analyze existing resources in a resource group
- 2.The skill will ask clarifying questions about services, SKUs, regions, and security requirements
- 3.Review the generated interactive HTML architecture diagram and request modifications if needed
- 4.Confirm the architecture design; the skill generates Bicep templates automatically
- 5.Review the generated Bicep code and approve any changes
- 6.The skill validates the template, runs a what-if preview, and deploys to your Azure subscription
Use cases
- Design a new RAG (Retrieval-Augmented Generation) architecture on Azure with AI Search and OpenAI
- Analyze your current Azure resource group and generate a visual diagram of the infrastructure
- Modify an existing architecture by describing changes in natural language (e.g., 'add a private endpoint for security')
- Generate and review Bicep code for infrastructure deployment without manual template writing
- Reduce Azure costs or strengthen security by requesting architecture recommendations
- Azure architects and infrastructure engineers
- DevOps and platform engineers managing Infrastructure-as-Code
- Data engineers designing data pipelines with Foundry, ADLS Gen2, or Databricks
- AI/ML engineers setting up Azure OpenAI and AI Search infrastructure
- Teams adopting Bicep and IaC practices
azure-architecture-autopilot FAQ
No. The diagram engine is embedded in the skill's scripts folder and works immediately without pip install. Bicep and az CLI discovery is automatic.
The skill will ask you directly. Choose 'Design a new Azure architecture' for new builds, or 'Analyze + modify existing Azure resources' to visualize and refine current infrastructure.
Yes. All Azure services are supported. The skill automatically consults Microsoft Docs to generate the same quality output for any service.
The skill automatically detects your language from your first message and responds in that language throughout the entire workflow.
Yes. After deployment, you can request changes in natural language. The skill will return to the architecture design phase to confirm modifications before regenerating Bicep and redeploying.
Full instructions (SKILL.md)
Source of truth, from github/awesome-copilot.
name: azure-architecture-autopilot description: > 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
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_fetchorweb_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:
$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:
> **⏳ [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/*.mdfile - 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.htmlmust 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 |
assets/06-architecture-diagram.png | Example generated architecture diagram |
assets/07-azure-portal-resources.png | Example Azure portal resource view |
assets/08-deployment-succeeded.png | Example successful deployment result |
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