azure-cloud-migrate

Assess and migrate cloud workloads from AWS, GCP, and other providers to Azure services. Supports Lambda-to-Azure Functions migration with dedicated scenario reference and best practices Generates assessment reports mapping source services to Azure equivalents before any code conversion Converts source code to target Azure runtime models, with output isolated in a separate <source-folder>-azure/ directory Requires sequential phase execution: assessment first, then migration, with user...

npx skills add https://github.com/microsoft/azure-skills --skill azure-cloud-migrate

Azure Cloud Migrate

This skill handles assessment and code migration of existing cloud workloads to Azure.

Rules

  1. Follow phases sequentially — do not skip
  2. Generate assessment before any code migration
  3. Load the scenario reference and follow its rules
  4. Use mcp_azure_mcp_get_azure_bestpractices and mcp_azure_mcp_documentation MCP tools
  5. Use the latest supported runtime for the target service
  6. Destructive actions require ask_userfunctions global-rules | app-service global-rules
  7. Report progress to user — During long-running operations (deployments, image pushes), provide resource-level status updates so the user is never left waiting without feedback — see workflow-details.md
  8. Audit service discovery in app code — Kubernetes DNS names (e.g., http://order-service:3001) do not resolve in Container Apps. During assessment, scan source code for hardcoded hostnames/ports in HTTP clients and flag them for env-var-driven URL injection

Migration Scenarios

SourceTargetReference
AWS LambdaAzure Functionslambda-to-functions.md (assessment, code-migration)
AWS Elastic BeanstalkAzure App Servicebeanstalk-to-app-service.md
HerokuAzure App Serviceheroku-to-app-service.md
Google App EngineAzure App Serviceapp-engine-to-app-service.md
AWS Fargate (ECS)Azure Container Appsfargate-to-container-apps.md (assessment, deployment)
Kubernetes (GKE/EKS/Self-hosted)Azure Container Appsk8s-to-container-apps.md
GCP Cloud RunAzure Container Appscloudrun-to-container-apps.md
Spring Boot (Azure Spring Apps/VMs)Azure Container Appsspring-apps-to-aca.md

No matching scenario? Use mcp_azure_mcp_documentation and mcp_azure_mcp_get_azure_bestpractices tools.

Output Directory

All output goes to <workspace-root-basename>-azure/ at workspace root, where <workspace-root-basename> is the name of the top-level workspace directory itself (NOT a subdirectory within it). Never modify the source directory.

Steps

  1. Create <workspace-root-basename>-azure/ at workspace root
  2. Assess — Analyze source, map services, generate report using the scenario-specific assessment guide → functions assessment | app-service assessment
  3. Migrate — Convert code/config using the scenario-specific migration guide → functions code-migration | app-service code-migration
  4. Ask User — "Migration complete. Test locally or deploy to Azure?"
  5. Hand off to azure-prepare for infrastructure, testing, and deployment

Track progress in migration-status.md — see workflow-details.md.

Mehr Skills von microsoft

oss-growth
microsoft
OSS-Wachstums-Hacker-Persona
agent-framework-azure-ai-py
microsoft
Erstellen Sie Azure AI Foundry-Agents mit dem Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Verwenden Sie dies beim Erstellen persistenter Agents mit AzureAIAgentsProvider, bei der Nutzung gehosteter Tools (Code-Interpreter, Dateisuche, Websuche), bei der Integration von MCP-Servern, bei der Verwaltung von Konversationsthreads oder bei der Implementierung von Streaming-Antworten. Umfasst Funktionstools, strukturierte Ausgaben und Multi-Tool-Agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrumentieren Sie Browser-/Web-Apps mit dem Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Verwenden Sie es für Real User Monitoring (RUM) – Seitenaufrufe, Klicks, AJAX/Fetch-Abhängigkeiten, Ausnahmen, benutzerdefinierte Ereignisse und browser-seitige GenAI-Agent-Traces, die mit Backend-OpenTelemetry-Traces korreliert werden. Umfasst SDK-Loader-Skript und npm-Setup, Framework-Erweiterungen (React, React Native, Angular), Click Analytics, Telemetrie-Initialisierer und OTel-GenAI-Semantik-Konventionen für Agent-/Tool-/Modell-Spans, die vom Browser ausgegeben werden.
devops
azure-ai-anomalydetector-java
microsoft
Erstellen Sie Anomalieerkennungsanwendungen mit dem Azure AI Anomaly Detector SDK für Java. Verwenden Sie dies bei der Implementierung von univariater/multivariater Anomalieerkennung, Zeitreihenanalyse oder KI-gestützter Überwachung.
development
azure-ai-language-conversations-py
microsoft
Implementieren Sie Conversational Language Understanding (CLU) mit dem azure-ai-language-conversations Python SDK. Verwenden Sie dies, wenn Sie mit ConversationAnalysisClient arbeiten, um Gesprächsabsichten und Entitäten zu analysieren, NLP-Funktionen zu erstellen oder Sprachverständnis in Anwendungen zu integrieren.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 für Python. Verwenden für ML-Workspaces, Jobs, Modelle, Datensätze, Compute und Pipelines. Auslöser: „azure-ai-ml“, „MLClient“, „Workspace“, „Modell-Registry“, „Trainings-Jobs“, „Datensätze“.
development