> azure-observability
Azure Observability Services including Azure Monitor, Application Insights, Log Analytics, Alerts, and Workbooks. Provides metrics, APM, distributed tracing, KQL queries, and interactive reports.
curl "https://skillshub.wtf/microsoft/skills/azure-observability?format=md"Azure Observability Services
Services
| Service | Use When | MCP Tools | CLI |
|---|---|---|---|
| Azure Monitor | Metrics, alerts, dashboards | azure__monitor | az monitor |
| Application Insights | APM, distributed tracing | azure__applicationinsights | az monitor app-insights |
| Log Analytics | Log queries, KQL | azure__kusto | az monitor log-analytics |
| Alerts | Notifications, actions | - | az monitor alert |
| Workbooks | Interactive reports | azure__workbooks | - |
MCP Server (Preferred)
When Azure MCP is enabled:
Monitor
azure__monitorwith commandmonitor_metrics_query- Query metricsazure__monitorwith commandmonitor_logs_query- Query logs with KQL
Application Insights
azure__applicationinsightswith commandapplicationinsights_component_list- List App Insights resources
Log Analytics
azure__kustowith commandkusto_cluster_list- List clustersazure__kustowith commandkusto_query- Execute KQL queries
If Azure MCP is not enabled: Run /azure:setup or enable via /mcp.
CLI Reference
# List Log Analytics workspaces
az monitor log-analytics workspace list --output table
# Query logs with KQL
az monitor log-analytics query \
--workspace WORKSPACE_ID \
--analytics-query "AzureActivity | take 10"
# List Application Insights
az monitor app-insights component list --output table
# List alerts
az monitor alert list --output table
# Query metrics
az monitor metrics list \
--resource RESOURCE_ID \
--metric "Percentage CPU"
Common KQL Queries
// Recent errors
AppExceptions
| where TimeGenerated > ago(1h)
| project TimeGenerated, Message, StackTrace
| order by TimeGenerated desc
// Request performance
AppRequests
| where TimeGenerated > ago(1h)
| summarize avg(DurationMs), count() by Name
| order by avg_DurationMs desc
// Resource usage
AzureMetrics
| where TimeGenerated > ago(1h)
| where MetricName == "Percentage CPU"
| summarize avg(Average) by Resource
Monitoring Strategy
| What to Monitor | Service | Metric/Log |
|---|---|---|
| Application errors | App Insights | Exceptions, failed requests |
| Performance | App Insights | Response time, dependencies |
| Infrastructure | Azure Monitor | CPU, memory, disk |
| Security | Log Analytics | Sign-ins, audit logs |
| Costs | Cost Management | Budget alerts |
SDK Quick References
For programmatic access to monitoring services, see the condensed SDK guides:
- OpenTelemetry: Python | TypeScript | Python Exporter
- Monitor Query: Python | Java
- Log Ingestion: Python | Java
- App Insights Mgmt: .NET
Service Details
For deep documentation on specific services:
- Application Insights setup ->
appinsights-instrumentationskill - KQL query patterns -> Log Analytics KQL documentation
- Alert configuration -> Azure Monitor alerts documentation
> related_skills --same-repo
> skill-creator
Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.
> podcast-generation
Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
> mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).
> github-issue-creator
Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.