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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
Prerequisites
  • 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
Claude Code
Cursor
Windsurf
Cline

How to use azure-architecture-autopilot

  1. 1.Describe your desired Azure architecture in natural language, or ask to analyze existing resources in a resource group
  2. 2.The skill will ask clarifying questions about services, SKUs, regions, and security requirements
  3. 3.Review the generated interactive HTML architecture diagram and request modifications if needed
  4. 4.Confirm the architecture design; the skill generates Bicep templates automatically
  5. 5.Review the generated Bicep code and approve any changes
  6. 6.The skill validates the template, runs a what-if preview, and deploys to your Azure subscription

Use cases

Good for
  • 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
Who it's for
  • 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

Do I need to install Python or Bicep separately?

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.

What if I'm not sure whether to design new or analyze existing infrastructure?

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.

Can this skill work with services beyond the optimized list (Foundry, OpenAI, AI Search)?

Yes. All Azure services are supported. The skill automatically consults Microsoft Docs to generate the same quality output for any service.

What languages does this skill support?

The skill automatically detects your language from your first message and responds in that language throughout the entire workflow.

Can I modify an architecture after deployment?

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)

FeatureTool NameNotes
Fetch URL contentweb_fetchFor MS Docs lookups, etc.
Web searchweb_searchURL discovery
Ask userask_userchoices must be a string array
Sub-agentstaskexplore/task/general-purpose
Shell command executionpowershellWindows 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:

$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 QuestionPreload Simultaneously
Project name / scan scopeReference files, MS Docs, Python path discovery, diagram module path verification
Model/SKU selectionMS Docs for next question choices
Architecture confirmationaz account show/list, az group list
Subscription selectionaz 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

CategoryHandling MethodExamples
StableReference files firstisHnsEnabled: true, PE triple set
DynamicAlways fetch MS DocsAPI version, model availability, SKU, region

Quick Reference

FileRole
references/phase0-scanner.mdExisting resource scan + relationship inference + diagram
references/phase1-advisor.mdInteractive architecture design + fact checking
references/bicep-generator.mdBicep code generation rules
references/bicep-reviewer.mdCode review checklist
references/phase4-deployer.mdvalidate → what-if → deploy
references/service-gotchas.mdRequired properties, PE mappings
references/azure-dynamic-sources.mdMS Docs URL registry
references/azure-common-patterns.mdPE/security/naming patterns
references/ai-data.mdAI/Data service guide
assets/06-architecture-diagram.pngExample generated architecture diagram
assets/07-azure-portal-resources.pngExample Azure portal resource view
assets/08-deployment-succeeded.pngExample successful deployment result