azure-enterprise-infra-planner

Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.

npx skills add https://github.com/microsoft/skills --skill azure-enterprise-infra-planner

Azure Enterprise Infra Planner

When to Use This Skill

Activate this skill when user wants to:

  • Plan enterprise Azure infrastructure from a workload or architecture description
  • Architect a landing zone, hub-spoke network, or multi-region topology
  • Design networking infrastructure: VNets, subnets, firewalls, private endpoints, VPN gateways
  • Plan identity, RBAC, and compliance-driven infrastructure
  • Generate Bicep or Terraform for subscription-scope or multi-resource-group deployments
  • Plan disaster recovery, failover, or cross-region high-availability topologies

Quick Reference

PropertyDetails
MCP toolsinsights_get, get_azure_bestpractices_get, wellarchitectedframework_serviceguide_get, microsoft_docs_fetch, microsoft_docs_search, bicepschema_get
CLI commandsaz deployment group create, az bicep build, az resource list, terraform init, terraform plan, terraform validate, terraform apply, checkov
Output schemaschema.md
Key referencesworkflow.md, waf-checklist.md, resources/, constraints/

Workflow (Start Here)

Follow the step-by-step instructions in workflow.md to execute the 7 phases of infrastructure planning and provisioning.

Architecture

The skill runs a 7-phase, gated pipeline. Input is triaged into one of two flows:

  • Greenfield — only new requirements; run the phases straight through.
  • Referenced (brownfield) — the user supplies something that already exists (a live resource / resource group / subscription, IaC or an infra plan, or a requirements doc). The same phases run, plus referenced-workload.md: existing resources are inventoried and referenced (never recreated), the new workload is wired into them, and Phase 7 deploys additively (incremental only — never modifying or destroying the referenced resources).

Every phase advances only after its gate passes. Phase 5 requires explicit user approval; Phase 6 is a hardened, self-verifying gate — the generated IaC must be secure-by-default, pass local validation (az bicep build / terraform validate) with zero errors, pass a checkov security scan with no unresolved high/critical findings, and the skill must show the command output and emit a completion self-check before advancing; Phase 7 requires an explicit, risk-acknowledged deploy confirmation.

flowchart TD
    IN([Input]) --> TRIAGE{Existing infra<br/>referenced?}
    TRIAGE -- "No (greenfield)" --> P1
    TRIAGE -- "Yes (referenced)" --> RW[/referenced-workload.md:<br/>inventory + assign roles<br/>reference, never recreate/]
    RW --> P1

    subgraph PIPE [7-phase gated pipeline]
        direction TB
        P1[Phase 1 · Extract insights] --> P2[Phase 2 · Research best practices]
        P2 --> P3[Phase 3 · Research resources]
        P3 --> P4[Phase 4 · Generate plan]
        P4 --> P5{Phase 5 · Verify<br/>user approves?}
        P5 -- "no" --> P4
        P5 -- "approved" --> P6[Phase 6 · Generate IaC]
        P6 --> VAL{Validate<br/>az bicep build /<br/>terraform validate}
        VAL -- "errors" --> P6
        VAL -- "clean" --> P7{Phase 7 · Deploy<br/>risk-ack confirm?}
    end

    P7 -- "greenfield" --> DEP[az deployment / terraform apply]
    P7 -- "referenced" --> DEPADD[Additive deploy · incremental only<br/>what-if preview · no destroy of<br/>referenced resources]
    DEP --> OUT([Deployed])
    DEPADD --> OUT

    classDef gate fill:#fff3cd,stroke:#d39e00,color:#000;
    classDef ref fill:#e2f0d9,stroke:#548235,color:#000;
    class P5,VAL,P7,TRIAGE gate;
    class RW,DEPADD ref;

Artifacts (written under <project-root>/): .azure/insights.json (Phase 1), .azure/infrastructure-plan.json (Phase 4, status draftapproveddeployed), and infra/main.bicep + infra/modules/* or infra/main.tf + infra/modules/** (Phase 6).

MCP Tools

ToolPurpose
insights_getRetrieve insights about the user's existing Azure environment to guide planning decisions
get_azure_bestpractices_getAzure best practices for code generation, operations, and deployment
wellarchitectedframework_serviceguide_getWAF service guide for a specific Azure service
microsoft_docs_searchSearch Microsoft Learn for relevant documentation chunks
microsoft_docs_fetchFetch full content of a Microsoft Learn page by URL
bicepschema_getBicep schema definition for any Azure resource type (latest API version)

Error Handling

ErrorCauseFix
MCP tool error or not availableTool call timeout, connection error, or tool doesn't existRetry once; fall back to reference files and notify user if unresolved
Plan approval missingmeta.status is not approvedStop and prompt user for approval before IaC generation or deployment
IaC validation failureaz bicep build or terraform validate returns errorsFix the generated code and re-validate; notify user if unresolved
Pairing constraint violationIncompatible SKU or resource combinationFix in plan before proceeding to IaC generation
Infra plan or IaC files not foundFiles written to wrong location or not createdVerify files exist at <project-root>/.azure/ and <project-root>/infra/; if missing, re-create the files by following workflow.md exactly

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development