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azure-architecture-autopilot

Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep.

Install / Use

npx skills add github/awesome-copilot --skill azure-architecture-autopilot

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

91/100

Category

Security

Supported Platforms

Universal

Our assessment of azure-architecture-autopilot

azure-architecture-autopilot scores 91/100 on our quality scale, 298th of 748 Security skills we index (top 40%).

Its SKILL.md is 7.4 KB long, well organised into 16 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.

With 39,348 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 3 days ago, so azure-architecture-autopilot is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

azure-architecture-autopilot compared with similar skills

All 4 of these similar skills score higher than azure-architecture-autopilot; compare them before choosing.

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Frequently asked questions

How do I install azure-architecture-autopilot?
Run npx skills add github/awesome-copilot --skill azure-architecture-autopilot. The install tabs above show the steps for each supported agent.
Which AI agents does azure-architecture-autopilot work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is azure-architecture-autopilot safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is azure-architecture-autopilot still maintained?
The repository was last updated 3 days ago, so azure-architecture-autopilot is actively maintained.

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_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 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 | | 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 |

Related Skills

View on GitHub
GitHub Stars39.3k
CategorySecurity
Updated3d ago
Forks5.0k

Languages

JavaScript

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions