azure-architecture-autopilot

Original：🇺🇸 English

Translated

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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

3installs

Sourcegithub/awesome-copilot

Added on2026-03-25

NPX Install

npx skill4agent add github/awesome-copilot azure-architecture-autopilot

SKILL.md Content

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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