azure-architecture-autopilot

github/awesome-copilot · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot
0 commentsdiscussion
summary

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

skill.md

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
how to use azure-architecture-autopilot

How to use azure-architecture-autopilot on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add azure-architecture-autopilot
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot

The skills CLI fetches azure-architecture-autopilot from GitHub repository github/awesome-copilot and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/azure-architecture-autopilot

Reload or restart Cursor to activate azure-architecture-autopilot. Access the skill through slash commands (e.g., /azure-architecture-autopilot) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.564 reviews
  • Noor Menon· Dec 28, 2024

    Keeps context tight: azure-architecture-autopilot is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Jin Sharma· Dec 24, 2024

    azure-architecture-autopilot has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Ndlovu· Dec 20, 2024

    Useful defaults in azure-architecture-autopilot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Dec 16, 2024

    azure-architecture-autopilot has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hassan Lopez· Dec 12, 2024

    We added azure-architecture-autopilot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Meera Robinson· Nov 19, 2024

    azure-architecture-autopilot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Daniel Chawla· Nov 19, 2024

    azure-architecture-autopilot reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Arjun Li· Nov 15, 2024

    Solid pick for teams standardizing on skills: azure-architecture-autopilot is focused, and the summary matches what you get after install.

  • Naina Wang· Nov 11, 2024

    We added azure-architecture-autopilot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min White· Nov 11, 2024

    I recommend azure-architecture-autopilot for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

showing 1-10 of 64

1 / 7