Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, service bus dead letter.
npx skills add https://github.com/microsoft/GitHub-Copilot-for-Azure --skill azure-diagnostics
> AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE
>
> This document is the official source for debugging and troubleshooting Azure production issues. Follow these instructions to diagnose and resolve common Azure service problems systematically.
Activate this skill when user wants to:
| Service | Common Issues | Reference |
|---------|---------------|-----------|
| Container Apps | Image pull failures, cold starts, health probes, port mismatches | container-apps/ |
| App Service | High CPU, deployment failures, crashes, slow responses, TLS/custom domains | app-service/ |
| Function Apps | App details, invocation failures, timeouts, binding errors, cold starts, missing app settings | functions/ |
| AKS | Cluster access, nodes, kube-system, scheduling, crash loops, ingress, DNS, upgrades | AKS Troubleshooting |
| Compute | VM RDP/SSH connectivity, NSG/firewall blocks, credential resets, VM agent/tooling issues | VM Connectivity Troubleshooting |
| Messaging | Event Hubs & Service Bus SDK errors, AMQP failures, message lock, connectivity | Messaging Troubleshooting |
# Check resource health
az resource show --ids RESOURCE_ID
# View activity log
az monitor activity-log list -g RG --max-events 20
# Container Apps logs
az containerapp logs show --name APP -g RG --follow
# Function App logs (query App Insights traces)
az monitor app-insights query --apps APP-INSIGHTS -g RG \
--analytics-query "traces | where timestamp > ago(1h) | order by timestamp desc | take 50"
For AI-powered diagnostics, use:
mcp_azure_mcp_applens
intent: "diagnose issues with <resource-name>"
command: "diagnose"
parameters:
resourceId: "<resource-id>"
Provides:
- Automated issue detection
- Root cause analysis
- Remediation recommendations
For querying logs and metrics:
mcp_azure_mcp_monitor
intent: "query logs for <resource-name>"
command: "logs_query"
parameters:
workspaceId: "<workspace-id>"
query: "<KQL-query>"
See kql-queries.md for common diagnostic queries.
mcp_azure_mcp_resourcehealth
intent: "check health status of <resource-name>"
command: "get"
parameters:
resourceId: "<resource-id>"
# Check specific resource health
az resource show --ids RESOURCE_ID
# Check recent activity
az monitor activity-log list -g RG --max-events 20
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Take microsoft/github-copilot-for-azure-azure-diagnostics from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.