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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/architecture-guidance-sources.md

Architecture Guidance Sources (For Design Direction Decisions)

A source registry for using Azure official architecture guidance only for design direction decisions.

The URLs in this document are a list of sources for "where to look". Do not hardcode the contents of these URLs as fixed facts. Do not use for SKU, API version, region, model availability, or PE mapping decisions — those are handled exclusively via azure-dynamic-sources.md.


Purpose Separation

PurposeDocument to UseDecidable Items
Design direction decisionsThis document (architecture-guidance-sources)Architecture patterns, best practices, service combination direction, security boundary design
Deployment spec verificationazure-dynamic-sources.mdAPI version, SKU, region, model availability, PE groupId, actual property values

What must NOT be decided using this document:

  • API version

  • SKU names/pricing

  • Region availability

  • Model names/versions/deployment types

  • PE groupId / DNS Zone mapping

  • Specific values for resource properties


Primary Sources

Targeted fetch targets for design direction decisions.

IDDocumentURLPurpose
A1Azure Architecture Centerhttps://learn.microsoft.com/en-us/azure/architecture/Hub — Entry point for finding domain-specific documents
A2Well-Architected Frameworkhttps://learn.microsoft.com/en-us/azure/architecture/framework/Security/reliability/performance/cost/operations principles
A3Cloud Adoption Framework / Landing Zonehttps://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ready/landing-zone/Enterprise governance, network topology, subscription structure
A4Azure AI/ML Architecturehttps://learn.microsoft.com/en-us/azure/architecture/ai-ml/AI/ML workload reference architecture hub
A5Basic Foundry Chat Reference Architecturehttps://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/basic-azure-ai-foundry-chatBasic Foundry-based chatbot structure
A6Baseline AI Foundry Chat Reference Architecturehttps://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-openai-e2e-chatFoundry chatbot enterprise baseline (including network isolation)
A7RAG Solution Design Guidehttps://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guideRAG pattern design guide
A8Microsoft Fabric Overviewhttps://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overviewFabric platform overview and workload understanding
A9Fabric Governance / Adoptionhttps://learn.microsoft.com/en-us/power-bi/guidance/fabric-adoption-roadmap-governanceFabric governance, adoption roadmap

Secondary Sources (awareness only)

Not direct fetch targets; referenced only for change awareness.

DocumentURLNotes
Azure Updateshttps://azure.microsoft.com/en-us/updates/Service changes/new feature announcements. Not a targeted fetch target

Fetch Trigger — When to Query

Architecture guidance documents are not queried on every request. Only perform targeted fetch when the following triggers apply.

Trigger Conditions
  1. When the user's workload type is identified in Phase 1 (automatic)

    • Pre-query the relevant workload's reference architecture to adjust question depth

    • Triggers automatically even if the user doesn't mention "best practice" etc.

    • Purpose: Reflect official architecture-based design decision points in questions, beyond SKU/region spec questions

  2. When the user requests design direction justification

    • Keywords such as "best practice", "reference architecture", "recommended structure", "baseline", "well-architected", "landing zone", "enterprise pattern"

  3. When architecture boundaries for a new service combination are ambiguous

    • Inter-service relationships that cannot be determined from existing reference files/service-gotchas

  4. When enterprise-level security/governance design is needed

    • Subscription structure, network topology, landing zone patterns

When Triggers Do Not Apply
  • Simple resource creation (SKU/API version/region questions) → Use only azure-dynamic-sources.md

  • Service combinations already covered in domain-packs → Prioritize reference files

  • Bicep property value verification → service-gotchas.md or MS Docs Bicep reference


Fetch Budget

ScenarioMax Fetch Count
Default (when trigger fires)Architecture guidance documents up to 2
Additional fetch allowed whenConflicts between documents / core design uncertainty remains / user explicitly requests deeper justification
Simple deployment spec questions0 (no architecture guidance queries)

Decision Rule by Question Type

Question TypeDocuments to QueryDesign Decision Points to ExtractDocuments NOT to Query
RAG / chatbot / Foundry appA5 or A6 + A7Network isolation level, authentication method (managed identity vs key), indexing strategy (push vs pull), monitoring scopeDo not traverse entire Architecture Center
Enterprise security / governance / landing zoneA2 + A3Subscription structure, network topology (hub-spoke etc.), identity/governance model, security boundaryAI/ML domain documents not needed
Fabric data platformA8 + A9Capacity model (SKU selection criteria), governance level, data boundary (workspace separation etc.)AI-related documents not needed
Ambiguous service combination (unclear pattern)A1 (find closest domain document from hub) + that documentKey design decision points identified from the documentDo not traverse all sub-documents
Simple resource creation values (SKU/API/region)No query—All architecture guidance
General AI/ML architectureA4 (hub) + closest reference architectureCompute isolation, data boundary, model serving approachDo not crawl entirely

URL Fallback Rule

  1. Use en-us Learn URLs by default

  2. If a specific URL returns 404 / redirect / deprecated → Fall back to the parent hub page

    • Example: If A5 fails → Search for "foundry chat" keyword on A4 (AI/ML hub)

  3. If not found on the parent hub either → Search by title keyword on A1 (Architecture Center main)

  4. Do not use the contents of a URL as fixed rules just because the URL exists


Full Traversal Prohibited

  • Do not broadly traverse (crawl) Architecture Center sub-documents

  • Only targeted fetch 1–2 related documents according to the decision rule by question type

  • Even within fetched documents, only reference relevant sections; do not read the entire document

  • Unlimited fetching, recursive link following, and sub-page enumeration are prohibited

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

AutoGineer is not affiliated with or endorsed by whoniiii or GitHub.

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