A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionazure-architecture-autopilotExecute the skills CLI command in your project's root directory to begin installation:
Fetches azure-architecture-autopilot from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate azure-architecture-autopilot. Access via /azure-architecture-autopilot in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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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.
🚨 Detect the language of the user's first message and provide all subsequent responses in that language. This is the highest-priority principle.
⚠️ 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.
| 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.
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.
Use blockquote + emoji + bold format:
> **⏳ [Action]** — [Reason]
> **✅ [Complete]** — [Result]
> **⚠️ [Warning]** — [Details]
> **❌ [Failed]** — [Cause]
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 |
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
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
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"
]
})
references/*.md file01_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.Microsoft Foundry, Azure OpenAI, AI Search, ADLS Gen2, Key Vault, Microsoft Fabric, Azure Data Factory, VNet/Private Endpoint, AML/AI Hub
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".
| Category | Handling Method | Examples |
|---|---|---|
| Stable | Reference files first | isHnsEnabled: true, PE triple set |
| Dynamic | Always fetch MS Docs | API version, model availability, SKU, region |
| 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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
Keeps context tight: azure-architecture-autopilot is the kind of skill you can hand to a new teammate without a long onboarding doc.
azure-architecture-autopilot has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in azure-architecture-autopilot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
azure-architecture-autopilot has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added azure-architecture-autopilot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
azure-architecture-autopilot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
azure-architecture-autopilot reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: azure-architecture-autopilot is focused, and the summary matches what you get after install.
We added azure-architecture-autopilot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend azure-architecture-autopilot for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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