azure-kusto

Write and optimize KQL queries for Azure Monitor and Data Explorer.

16|1|Updated Apr 20, 2026

Search Tags:#azure #diagnostics #telemetry #query-optimization #kql #log-analytics #data-explorer

Ready to run

npx skills add https://github.com/manu14357/skills --skill azure-kusto-manu14357

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: azure-kusto
Source: https://github.com/manu14357/skills/tree/main/skills/azure-kusto
Command: npx skills add https://github.com/manu14357/skills --skill azure-kusto-manu14357

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Writes and optimizes Kusto Query Language (KQL) queries for Azure Monitor and Data Explorer to derive actionable insights.

Core Features & Use Cases

High-quality KQL query construction for diagnostic workflows across Azure Monitor, Log Analytics, and Application Insights.
Query optimization and performance tuning for large telemetry datasets.
Example scenarios include incident investigations, trend analysis, and alert generation.

Quick Start

Run a starter KQL query for the last 24 hours across relevant tables.

Frequently Asked Questions about azure-kusto

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I write KQL queries for Azure Monitor diagnostics?▼

To write KQL queries for Azure Monitor diagnostics, construct statements using early filtration, time-binning, and aggregation to analyze telemetry across Log Analytics and Application Insights efficiently.

What's the best way to optimize KQL queries for large telemetry datasets?▼

The best way to optimize KQL queries for large telemetry datasets is by applying early filtration and aggregation to reduce data scanned, ensuring efficient performance tuning across Azure Data Explorer and Log Analytics.

Can I use this for incident investigations across Application Insights?▼

Yes, you can use KQL queries for incident investigations across Application Insights by building diagnostic workflows that filter and aggregate telemetry data to identify root causes and performance bottlenecks.

Do I need to know KQL operators and table schemas to query Azure Data Explorer?▼

Yes, querying Azure Data Explorer requires knowledge of KQL operators and table schemas to build accurate queries, apply proper time-binning, and document query elements for diagnostic and telemetry analysis.

How do I generate alerts and analyze trends using Log Analytics queries?▼

You generate alerts and analyze trends using Log Analytics queries by writing KQL statements that aggregate telemetry data over specific time intervals, enabling performance tuning and incident detection across Azure Monitor.