kql

Write precise KQL queries for Azure Data Explorer and related services.

Updated Aug 23, 2026

Search Tags:#time-series #error-handling #query-patterns #kql #log-analytics #azure-data-explorer

Ready to run

npx skills add https://github.com/rrbanda/skills --skill kql-rrbanda

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kql
Source: https://github.com/rrbanda/skills/tree/main/skills/devops/kql
Command: npx skills add https://github.com/rrbanda/skills --skill kql-rrbanda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Writing correct and efficient KQL queries for Azure Data Explorer and related services can be error-prone and slow. This skill guides users through syntax, patterns, and best practices to accelerate data analysis and reduce mistakes.

Core Features & Use Cases

Mastery of KQL syntax for querying ADX, Log Analytics, and Fabric Data Explorer.
Pattern-focused guidance on filtering, joins, windowing, time series, and graph-style queries.
Real-world use cases such as troubleshooting logs, building dashboards, and time-series investigations.

Quick Start

Generate a ready-to-run KQL query that filters StormEvents by StartTime and counts events by EventType.

Frequently Asked Questions about kql

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

FAQPage Schema

How do I write efficient KQL queries for Azure Data Explorer?▼

To write efficient KQL queries for Azure Data Explorer, apply pattern-focused guidance on filtering, joins, and windowing. Using precise syntax and performance considerations reduces errors and accelerates data analysis.

What is the best way to troubleshoot logs using KQL in Log Analytics?▼

The best way to troubleshoot logs using KQL in Log Analytics is to use specific query patterns for filtering and aggregating data. This approach quickly isolates errors and identifies root causes within your telemetry data.

Can I perform time-series analysis and graph-style queries with KQL?▼

Yes, you can perform time-series analysis and graph-style queries with KQL. The query language supports windowing functions and graph-like patterns, enabling complex temporal investigations and relationship mapping across your data.

Does this KQL guidance apply to Microsoft Fabric Data Explorer?▼

Yes, this KQL guidance applies to Microsoft Fabric Data Explorer. The syntax mastery and query patterns cover Azure Data Explorer, Log Analytics, and Fabric Data Explorer, ensuring broad compatibility across related services.

Why does my KQL query fail when joining multiple tables in Azure Data Explorer?▼

KQL queries often fail when joining multiple tables in Azure Data Explorer due to syntax errors or mismatched join keys. Applying robust error handling and correct pattern-focused guidance resolves these structural issues.

How do I generate a ready-to-run KQL query to count events by type?▼

To generate a ready-to-run KQL query to count events by type, filter your dataset by a specific column like StartTime, then use the summarize operator to aggregate counts grouped by EventType.