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Azure Architecture Autopilot

GitHubOfficialFirst committed by whoniiiiDistributed by GitHub

  • Skill
  • Workflow
  • Cloud and DevOps
  • MIT
  • Low risk
  • Has scripts

From the author

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
— the description by whoniiii

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Azure Architecture Autopilot is a multi-phase agentic workflow designed to assist users in creating or modifying Azure infrastructure. It guides users through an interactive design process, generates architecture diagrams using an embedded engine, produces Bicep templates, and manages the deployment lifecycle. The skill integrates with Azure services and utilizes official Microsoft documentation to ensure configurations align with current service specifications.

What it does

  • Designs new Azure infrastructure based on natural language requirements.
  • Analyzes existing Azure resources to generate visual architecture diagrams.
  • Refines infrastructure designs through interactive conversation.
  • Generates and reviews Bicep templates for infrastructure as code.
  • Executes deployment workflows including validation and what-if analysis.
  • Detects user language to provide localized support and documentation.

When to use it

  • When you need to design a new Azure environment or RAG architecture.
  • When you want to visualize your current Azure resource configuration.
  • When you need to generate or update Bicep templates for Azure deployments.
  • When you require guidance on modifying existing Azure infrastructure to meet new requirements.

Advantages

  • Automated diagram generation using an embedded engine with official Azure icons.
  • Structured multi-phase workflow ensuring design confirmation before deployment.
  • Integration with Microsoft documentation for dynamic service property lookups.
  • Supports natural language interaction for complex infrastructure tasks.
  • Includes built-in safety checks like what-if analysis prior to deployment.

Usage examples

“Create a RAG architecture on Azure.”
The agent will initiate the design phase, suggest a baseline architecture, generate a diagram, and guide you through the Bicep generation and deployment process.
“Analyze my current Azure infrastructure and draw a diagram.”
The agent will scan your existing resources, infer relationships, and produce an interactive HTML diagram of your current setup.
“I want to strengthen security for my existing Azure resources.”
The agent will analyze your current configuration and propose modifications to your infrastructure design to align with security patterns.

Install

Documentation by whoniiii

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

Files

references/phase4-deployer.md

Phase 4: Deployment Agent

This file contains detailed instructions for Phase 4. Read and follow this file when the user approves deployment after Phase 3 (code review) is complete.


🚨🚨🚨 Phase 4 Mandatory Execution Order — Never Skip Any Step 🚨🚨🚨

The following 5 steps must be executed strictly in order. No step may be omitted or skipped. Even if the user requests deployment with "deploy it", "go ahead", "do it", etc., always proceed from Step 1 in order.

Step 1: Verify prerequisites (az login, subscription, resource group)
    ↓
Step 2: What-if validation (az deployment group what-if) ← Must execute
    ↓
Step 3: Generate preview diagram (02_arch_diagram_preview.html) ← Must generate
    ↓
Step 4: Actual deployment after user final confirmation (az deployment group create)
    ↓
Step 5: Generate deployment result diagram (03_arch_diagram_result.html)

Never do the following:

  • Execute az deployment group create directly without What-if

  • Skip generating the preview diagram (02_arch_diagram_preview.html)

  • Proceed with deployment without showing What-if results to the user

  • Only provide az commands for the user to run manually


Step 1: Verify Prerequisites
# Verify az CLI installation and login
az account show 2>&1

If not logged in, ask the user to run az login. The agent must never enter or store credentials directly.

Create resource group:

az group create --name "<RG_NAME>" --location "<LOCATION>"  # Location confirmed in Phase 1

→ Proceed to next step after confirming success

Step 2: Validate → What-if Validation — 🚨 Mandatory

Do not skip this step. Always execute it no matter how urgently the user requests deployment.

Step 2-A: Run Validate First (Quick Pre-validation)

what-if can hang indefinitely without error messages when there are Azure policy violations, resource reference errors, etc. To prevent this, always run validate first. Validate returns errors quickly.

# validate — Quickly catches policy violations, schema errors, parameter issues
az deployment group validate `
  --resource-group "<RG_NAME>" `
  --parameters main.bicepparam
  • Validate succeeds → Proceed to Step 2-B (what-if)

  • Validate fails → Analyze error messages, fix Bicep, recompile, re-validate

    • Azure Policy violation (RequestDisallowedByPolicy) → Reflect policy requirements in Bicep (e.g., azureADOnlyAuthentication: true)

    • Schema error → Fix API version/properties

    • Parameter error → Fix parameter file

Step 2-B: Run What-if

Run what-if after validate passes.

Choose parameter passing method:

  • If all @secure() parameters have default values → Use .bicepparam

  • If @secure() parameters require user input → Use --template-file + JSON parameter file

# Method 1: Use .bicepparam (when all @secure() parameters have defaults)
az deployment group what-if `
  --resource-group "<RG_NAME>" `
  --parameters main.bicepparam

# Method 2: Use JSON parameter file (when @secure() parameters require user input)
az deployment group what-if `
  --resource-group "<RG_NAME>" `
  --template-file main.bicep `
  --parameters main.parameters.json `
  --parameters secureParam='value'

→ Summarize the What-if results and present them to the user.

⏱️ What-if Execution Method and Timeout Handling:

What-if performs resource validation on the Azure server side, so it may take time depending on the service/region. Always execute with initial_wait: 300 (5 minutes). If not completed within 5 minutes, it automatically times out.

# Always set initial_wait: 300 when calling the powershell tool
# mode: "sync", initial_wait: 300
az deployment group what-if `
  --resource-group "<RG_NAME>" `
  --parameters main.bicepparam

Completed within 5 minutes → Proceed normally (summarize results → preview diagram → deployment confirmation)

Not completed within 5 minutes (timeout) → Immediately stop with stop_powershell and offer choices to the user:

ask_user({
  question: "What-if validation did not complete within 5 minutes. The Azure server response is delayed. How would you like to proceed?",
  choices: [
    "Retry (Recommended)",
    "Skip What-if and deploy directly"
  ]
})

If "Retry" is selected: Re-execute the same command with initial_wait: 300. Retry up to 2 times maximum. If "Skip What-if and deploy directly" is selected:

  • Generate the preview diagram based on the Phase 1 draft

  • Inform the user of the risks:

    ⚠️ Deploying without What-if validation. Unexpected resource changes may occur. Please verify in the Azure Portal after deployment.

Never do the following:

  • Execute without setting initial_wait, causing indefinite waiting

  • Let the agent arbitrarily decide "what-if is optional" and skip it

  • Automatically switch to deployment without asking the user on timeout

  • Skip what-if for reasons like "deployment is faster"

Step 3: Preview Diagram Based on What-if Results — 🚨 Mandatory

Do not skip this step. Always generate the preview diagram when What-if succeeds.

Regenerate the diagram using the actual resources to be deployed (resource names, types, locations, counts) from the What-if results. Keep the draft from Phase 1 (01_arch_diagram_draft.html) as-is, and generate the preview as 02_arch_diagram_preview.html. The draft can be reopened at any time.

## Architecture to Be Deployed (Based on What-if)

[Interactive diagram link — 02_arch_diagram_preview.html]
(Design draft: 01_arch_diagram_draft.html)

Resources to be created (N items):
[What-if results summary table]

Deploy these resources? (Yes/No)

Proceed to Step 4 when the user confirms. Do not proceed to deployment without the preview diagram.

Step 4: Actual Deployment

Execute only when the user has reviewed the preview diagram and What-if results and approved the deployment. Use the same parameter passing method used in What-if.

$deployName = "deploy-$(Get-Date -Format 'yyyyMMdd-HHmmss')"

# Method 1: Use .bicepparam
az deployment group create `
  --resource-group "<RG_NAME>" `
  --parameters main.bicepparam `
  --name $deployName `
  2>&1 | Tee-Object -FilePath deployment.log

# Method 2: Use JSON parameter file
az deployment group create `
  --resource-group "<RG_NAME>" `
  --template-file main.bicep `
  --parameters main.parameters.json `
  --name $deployName `
  2>&1 | Tee-Object -FilePath deployment.log

Periodically monitor progress during deployment:

az deployment group show `
  --resource-group "<RG_NAME>" `
  --name "<DEPLOYMENT_NAME>" `
  --query "{status:properties.provisioningState, duration:properties.duration}" `
  -o table
Handling Deployment Failures

When deployment fails, some resources may remain in a 'Failed' state. Redeploying in this state causes errors like AccountIsNotSucceeded.

⚠️ Resource deletion is a destructive command. Always explain the situation to the user and obtain approval before executing.

[Resource name] failed during deployment.
To redeploy, the failed resources must be deleted first.

Delete and redeploy? (Yes/No)

Delete failed resources and redeploy once the user approves.

🔹 Handling Soft-deleted Resources (Prevent Redeployment Blocking):

When a resource group is deleted after a failed deployment, Cognitive Services (Foundry), Key Vault, etc. remain in a soft-delete state. Redeploying with the same name causes FlagMustBeSetForRestore, Conflict errors.

Always check before redeployment:

# Check soft-deleted Cognitive Services
az cognitiveservices account list-deleted -o table

# Check soft-deleted Key Vault
az keyvault list-deleted -o table

Resolution options (provide choices to the user):

ask_user({
  question: "Soft-deleted resources from a previous deployment were found. How would you like to handle this?",
  choices: [
    "Purge and redeploy (Recommended) - Clean delete then create new",
    "Redeploy in restore mode - Recover existing resources"
  ]
})

Caution — Key Vault with enablePurgeProtection: true:

  • Cannot be purged (must wait until retention period expires)

  • Cannot recreate with the same name

  • Solution: Change the Key Vault name and redeploy (e.g., add timestamp to uniqueString() seed)

  • Explain the situation to the user and guide them on the name change

Step 5: Deployment Complete — Generate Diagram from Actual Resources and Report

Once deployment is complete, query the actually deployed resources and generate the final architecture diagram.

Step 1: Query Deployed Resources

az resource list --resource-group "<RG_NAME>" --output json

Step 2: Generate Diagram from Actual Resources

Extract resource names, types, SKUs, and endpoints from the query results and generate the final diagram using the built-in diagram engine. Be careful with file names to avoid overwriting previous diagrams:

  • 01_arch_diagram_draft.html — Design draft (keep)

  • 02_arch_diagram_preview.html — What-if preview (keep)

  • 03_arch_diagram_result.html — Deployment result final version

Populate the diagram's services JSON with actual deployed resource information:

  • name: Actual resource name (e.g., foundry-duru57kxgqzxs)

  • sku: Actual SKU

  • details: Actual values such as endpoints, location, etc.

Step 3: Report

## Deployment Complete!

[Interactive architecture diagram — 03_arch_diagram_result.html]
(Design draft: 01_arch_diagram_draft.html | What-if preview: 02_arch_diagram_preview.html)

Created resources (N items):
[Dynamically extracted resource names, types, and endpoints from actual deployment results]

## Next Steps
1. Verify resources in Azure Portal
2. Check Private Endpoint connection status
3. Additional configuration guidance if needed

## Cleanup Command (If Needed)
az group delete --name <RG_NAME> --yes --no-wait

Handling Architecture Change Requests After Deployment

When the user requests resource additions/changes/deletions after deployment is complete, do NOT go directly to Bicep/deployment. Always return to Phase 1 and update the architecture first.

Process:

  1. Confirm user intent — Ask first whether they want to add to the existing deployed architecture:

    Would you like to add a VM to the currently deployed architecture?
    Current configuration: [Deployed services summary]
  2. Return to Phase 1 — Apply Delta Confirmation Rule

    • Use the existing deployment result (03_arch_diagram_result.html) as the current state baseline

    • Verify required fields for new services (SKU, networking, region availability, etc.)

    • Confirm undecided items via ask_user

    • Fact-check (MS Docs fetch + cross-validation)

  3. Generate Updated Architecture Diagram

    • Combine existing deployed resources + new resources into 04_arch_diagram_update_draft.html

    • Show to the user and get confirmation:

    ## Updated Architecture
    
    [Interactive diagram — 04_arch_diagram_update_draft.html]
    (Previous deployment result: 03_arch_diagram_result.html)
    
    **Changes:**
    - Added: [New services list]
    - Removed: [Removed services list] (if any)
    
    Proceed with this configuration?
  4. After confirmation, proceed through Phase 2 → 3 → 4 in order

    • Incrementally add new resource modules to existing Bicep

    • Review → What-if → Deploy (incremental deployment)

Never do the following:

  • Jump directly to Bicep generation without updating the architecture diagram when a change is requested after deployment

  • Ignore the existing deployment state and create new resources in isolation

  • Proceed without confirming with the user whether to add to the existing architecture

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Phrasing that asks for actions without confirmation
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of whoniiiiwhoniiii
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/azure-architecture-autopilot · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
whoniiii, NehaGitHubAcc, Sertxito, stbenjam

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