github/awesome-copilot

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. 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, Bice

87CollectingNetwork access
See how to use itView GitHub source
npx skills add https://github.com/github/awesome-copilot --skill "skills/azure-architecture-autopilot"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn azure-architecture-autopilot's source instructions into a guide you can follow

According to the pinned SKILL.md from github/awesome-copilot: A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.

npx skills add https://github.com/github/awesome-copilot --skill "skills/azure-architecture-autopilot"
Check the pinned source

Best fit

  • 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, Bice

Bring this context

  • A concrete task that matches the documented purpose of azure-architecture-autopilot.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • A result that follows the pinned azure-architecture-autopilot instructions.
  • A concise record of assumptions, inputs used, and unresolved questions.
  • A final check against the source workflow and relevant permission signals.

Key source sections

Read azure-architecture-autopilot through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Tool Usage Guide (GHCP Environment)

All sub-agents (explore/task/general-purpose) cannot use webfetch or websearch. Fact-checking that requires MS Docs lookups must be performed directly by the main agent.

SKILL.md · Tool Usage Guide (GHCP Environment)
All sub-agents (explore/task/general-purpose) cannot use webfetch or websearch. Fact-checking that requires MS Docs lookups must be performed directly by the main agent.
02

Phase Transition Rules

Each Phase reads and follows the instructions in its corresponding references/.md file

SKILL.md · Phase Transition Rules
Each Phase reads and follows the instructions in its corresponding references/.md fileWhen transitioning between Phases, always inform the user about the next stepDo not skip Phases (especially the what-if between Phase 3 → Phase 4)
03

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.

SKILL.md · Automatic User Language Detection
If the user writes in Korean → respond in KoreanIf the user writes in English → respond in English (askuser, 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
04

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.

SKILL.md · 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.Python path + embedded diagram engine: refer to the diagram generation section in references/phase1-advisor.md.

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use azure-architecture-autopilot to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned azure-architecture-autopilot source to [task]. Pay particular attention to these source sections: “Tool Usage Guide (GHCP Environment)”, “Phase Transition Rules”, “Automatic User Language Detection”, “External Tool Path Discovery”, “Progress Updates Required”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current azure-architecture-autopilot result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Tool Usage Guide (GHCP Environment)” has been checked.

The source section “Phase Transition Rules” has been checked.

The source section “Automatic User Language Detection” has been checked.

The source section “External Tool Path Discovery” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

FAQ

What does azure-architecture-autopilot do?

A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.

How do I start using azure-architecture-autopilot?

The catalog detected this source-specific install command: npx skills add https://github.com/github/awesome-copilot --skill "skills/azure-architecture-autopilot". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
37,126
Repository forks
4,654
Quality
87/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

87/100
Documentation28/30
Specificity21/25
Maintenance18/20
Trust signals20/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

simpy by k-dense-ai

Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.

suede-code-grader by JasonColapietro

Give a blunt A-F ship grade for a code change across correctness, security, data, UX, verification, and deploy readiness. Use for a grade, not a findings review.

brand-landingpage by wshobson

Brand-first landing page designer — runs a brand-identity interview (colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML. Use when the user asks to create, design, or build a landing page, homepage, or marketing page and has no established visual direction. Skip when they have a design mockup, need a dashboard or app UI, are working at component level, building a multi-page app, or restyling with known design tokens —

github-release by github

Guides IA through releasing a new version of a GitHub library end-to-end. Handles SemVer versioning and Keep a Changelog formatting automatically.

foundry-hosted-agent-copilotkit by github

Ongoing development guidance for agentic web apps that pair a CopilotKit frontend with Microsoft Agent Framework agents on Azure AI Foundry hosted agents over the AG-UI protocol - add and gate agent tools, wire human-in-the-loop approvals, build generative UI and shared state, debug the event stream, upgrade pre-1.0 packages safely, and deploy hosted agent updates.

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 4 min

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)

FeatureTool NameNotes
Fetch URL contentweb_fetchFor MS Docs lookups, etc.
Web searchweb_searchURL discovery
Ask userask_userchoices must be a string array
Sub-agentstaskexplore/task/general-purpose
Shell command executionpowershellWindows 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 QuestionPreload Simultaneously
Project name / scan scopeReference files, MS Docs, Python path discovery, diagram module path verification
Model/SKU selectionMS Docs for next question choices
Architecture confirmationaz account show/list, az group list
Subscription selectionaz 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

CategoryHandling MethodExamples
StableReference files firstisHnsEnabled: true, PE triple set
DynamicAlways fetch MS DocsAPI version, model availability, SKU, region

Quick Reference

FileRole
references/phase0-scanner.mdExisting resource scan + relationship inference + diagram
references/phase1-advisor.mdInteractive architecture design + fact checking
references/bicep-generator.mdBicep code generation rules
references/bicep-reviewer.mdCode review checklist
references/phase4-deployer.mdvalidate → what-if → deploy
references/service-gotchas.mdRequired properties, PE mappings
references/azure-dynamic-sources.mdMS Docs URL registry
references/azure-common-patterns.mdPE/security/naming patterns
references/ai-data.mdAI/Data service guide
Skill path
skills/azure-architecture-autopilot/SKILL.md
Commit SHA
9933dcad5be5
Repository license
MIT
Data collected