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-autopilotInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
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.
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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| azure-architecture-autopilot (this skill)by github | 91 | 39.3k | 3d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 5d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 5d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 6d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 6d ago | SKILL.md |
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.
Skill content
View source on GitHubname: 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_fetchorweb_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/*.mdfile - 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.htmlmust 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 |
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
