.github/plugins/azure-skills/skills/azure-diagnostics/SKILL.md
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, 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.
npx skillsauth add microsoft/skills azure-diagnosticsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
4 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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/ |
| 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 |
# 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
tools
KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.
development
Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
testing
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
testing
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".