azure-architecture-autopilot

por github

Um pipeline que projeta infraestrutura Azure usando linguagem natural, ou analisa recursos existentes para visualizar arquitetura e prosseguir com modificação e implantação.

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

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

Mais skills de github

console-rendering
github
Instruções para usar o sistema de renderização de console baseado em tags de struct em Go
official
acquire-codebase-knowledge
github
Use esta habilidade quando o usuário solicitar explicitamente mapear, documentar ou integrar-se a uma base de código existente. Ative para comandos como "mapeie esta base de código", "documente…
official
acreadiness-assess
github
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc…
official
acreadiness-generate-instructions
github
Gera arquivos de instrução de agente de IA personalizados através do comando de instruções do AgentRC. Produz .github/copilot-instructions.md (padrão, recomendado para o Copilot no VS…
official
acreadiness-policy
github
Ajude o usuário a escolher, escrever ou aplicar uma política AgentRC. Políticas personalizam a pontuação de prontidão desabilitando verificações irrelevantes, substituindo impacto/nível, definindo…
official
add-educational-comments
github
Adiciona comentários educacionais a arquivos de código para transformá-los em recursos de aprendizado eficazes. Adapta a profundidade e o tom das explicações para três níveis de conhecimento configuráveis: iniciante, intermediário e avançado. Solicita automaticamente um arquivo caso nenhum seja fornecido, com correspondência de lista numerada para seleção rápida. Expande arquivos em até 125% usando apenas comentários educacionais (limite máximo: 400 novas linhas; 300 para arquivos com mais de 1.000 linhas). Preserva a codificação do arquivo, o estilo de indentação, a correção sintática e...
official
adobe-illustrator-scripting
github
Escreva, depure e otimize scripts de automação do Adobe Illustrator usando ExtendScript (JavaScript/JSX). Use ao criar ou modificar scripts que manipulam…
official
agent-governance
github
Políticas declarativas, classificação de intenção e trilhas de auditoria para controlar o acesso e comportamento de ferramentas de agentes de IA. Políticas de governança componíveis definem ferramentas permitidas/bloqueadas, filtros de conteúdo, limites de taxa e requisitos de aprovação — armazenados como configuração, não código. A classificação semântica de intenção detecta prompts perigosos (exfiltração de dados, escalada de privilégio, injeção de prompt) antes da execução da ferramenta usando sinais baseados em padrões. O decorador de governança em nível de ferramenta aplica políticas em funções...
official