此登錄庫釘選版本所收錄的最新上游作者日期。

azure-architecture-autopilot

已同步 v1.1.0 授權: MIT 來源: github/awesome-copilot/skills/azure-architecture-autopilot 提交: 4742f26 收錄的最新變更:

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

摘要

用途
根據自然語言需求設計 Azure 基礎架構,或分析現有資源,並支援架構調整與以 Bicep 部署。
使用時機
當您要規劃新的 Azure 環境、了解或修改現有環境,或建立並部署其基礎架構即程式碼時,請使用此工具。
輸出結果
此工具可產生互動式架構圖與 Bicep 程式碼,並在後續流程階段進行審查、驗證與部署。
上游變更(2 筆,最新收錄 2026-08-07)
4214189 fix: repair broken skill references, fence nesting, and invisible unicode (#2568) 此技能的快速參考表新增了三個範例圖片資產:架構圖、Azure 入口網站資源檢視畫面,以及成功部署結果。
3e01810 Add azure-architecture-autopilot skill 🤖🤖🤖 (#1158) 此修補新增了該技能的 SKILL.md,定義設計或分析 Azure 基礎架構、產生圖表與 Bicep、審查程式碼及部署的工作流程。
npx skills add "lettucebo/Skills#v2.2.0@azure-architecture-autopilot" --full-depth

⚠ v2.2.0 發布後即可使用

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
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

歷程

v1.0.0 bootstrap
v1.1.0 baseline-verified 4742f26
v1.1.0 license-refresh 4742f26