Skills
Skill 45 of 200
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with…
3 minutes · 630 words · 16 sections
Install
npx skills add github/awesome-copilot --skill azure-architecture-autopilotnpx skills add github/awesome-copilot/plugin marketplace add github/awesome-copilotThe first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.
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 → deployTrigger: “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 aboveAsk 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 |
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 |
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 verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.
main, last pushed 24 September 2026.SKILL.md, not by matching a directory convention. 2 distinct layouts observed: .github/skills/*/SKILL.md, skills/*/SKILL.md.h1 and no skipped levels:.github/plugin/marketplace.json by GitHub, declaring 162 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree./github/awesome-copilot.md, and each skill at its own .md URL.17 files · 2.8 MB
Everything this skill ships beside its prose. 13 of them are set here as subchapters of skill 45; the other 4 are described rather than reproduced.
Documentation the agent loads on demand, rather than up front.
Executable code the skill can run.
Templates, schemas and fixtures the skill draws on.
Everything else published alongside the skill.