.github/plugins/azure-skills/skills/azure-messaging/SKILL.md
Troubleshoot and resolve issues with Azure Messaging SDKs for Event Hubs and Service Bus. Covers connection failures, authentication errors, message processing issues, and SDK configuration problems. WHEN: event hub SDK error, service bus SDK issue, messaging connection failure, AMQP error, event processor host issue, message lock lost, message lock expired, lock renewal, lock renewal batch, send timeout, receiver disconnected, SDK troubleshooting, azure messaging SDK, event hub consumer, service bus queue issue, topic subscription error, enable logging event hub, service bus logging, eventhub python, servicebus java, eventhub javascript, servicebus dotnet, event hub checkpoint, event hub not receiving messages, service bus dead letter, batch processing lock, session lock expired, idle timeout, connection inactive, link detach, slow reconnect, session error, duplicate events, offset reset, receive batch.
npx skillsauth add microsoft/azure-skills azure-messagingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Property | Value |
|----------|-------|
| Services | Azure Event Hubs, Azure Service Bus |
| MCP Tools | mcp_azure_mcp_eventhubs, mcp_azure_mcp_servicebus |
| Best For | Diagnosing SDK connection, auth, and message processing issues |
| Tool | Command | Use |
|------|---------|-----|
| mcp_azure_mcp_eventhubs | Namespace/hub ops | List namespaces, hubs, consumer groups |
| mcp_azure_mcp_servicebus | Queue/topic ops | List namespaces, queues, topics, subscriptions |
| mcp_azure_mcp_monitor | logs_query | Query diagnostic logs with KQL |
| mcp_azure_mcp_resourcehealth | get | Check service health status |
| mcp_azure_mcp_documentation | Doc search | Search Microsoft Learn for troubleshooting docs |
mcp_azure_mcp_resourcehealth to verify the namespace is healthymcp_azure_mcp_documentation to search Microsoft Learn for the error or topicSee Service Troubleshooting Guide for ports, WebSocket fallback, IP firewall, private endpoints, and service tags.
Use mcp_azure_mcp_documentation to search Microsoft Learn for latest guidance. See Service Troubleshooting Guide for network and service-level docs.
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
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.
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).