skills/azure-cloud-migrate/SKILL.md
Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.
npx skillsauth add microsoft/azure-skills azure-cloud-migrateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill handles assessment and code migration of existing cloud workloads to Azure.
mcp_azure_mcp_get_azure_bestpractices and mcp_azure_mcp_documentation MCP toolsask_user — functions global-rules | app-service global-ruleshttp://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| Source | Target | Reference | |--------|--------|-----------| | AWS Lambda | Azure Functions | lambda-to-functions.md (assessment, code-migration) | | AWS Elastic Beanstalk | Azure App Service | beanstalk-to-app-service.md | | Heroku | Azure App Service | heroku-to-app-service.md | | Google App Engine | Azure App Service | app-engine-to-app-service.md | | AWS Fargate (ECS) | Azure Container Apps | fargate-to-container-apps.md (assessment, deployment) | | Kubernetes (GKE/EKS/Self-hosted) | Azure Container Apps | k8s-to-container-apps.md | | GCP Cloud Run | Azure Container Apps | cloudrun-to-container-apps.md | | Spring Boot (Azure Spring Apps/VMs) | Azure Container Apps | spring-apps-to-aca.md |
No matching scenario? Use
mcp_azure_mcp_documentationandmcp_azure_mcp_get_azure_bestpracticestools.
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.
<workspace-root-basename>-azure/ at workspace rootTrack progress in migration-status.md — see workflow-details.md.
development
# Azure App Onboard Scaffold — IaC Generation + Self-Review Generate deployment-ready infrastructure code from an architecture plan, verify it with adversarial self-review, and bridge to validation — all without deploying. ## Quick Reference | Property | Value | |----------|-------| | Parent | [azure-app-onboard](../SKILL.md) | | Best for | Turning `prepare-plan.json` service list into Bicep templates with secure-by-default patterns | | Inputs | `prepare-plan.json` (services, naming, quotas),
development
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
devops
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
testing
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).