azure-kusto

Query and analyze Azure Data Explorer data using KQL.

Updated Apr 20, 2026
One-click install
npx skills add https://github.com/qprjack86/apexbase --skill azure-kusto-qprjack86
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: azure-kusto
Source: https://github.com/qprjack86/apexbase/tree/main/.github/skills/azure-kusto
Command: npx skills add https://github.com/qprjack86/apexbase --skill azure-kusto-qprjack86

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you quickly explore, query, and analyze data stored in Azure Data Explorer without needing to handcraft every KQL pattern from scratch.

Core Features & Use Cases

  • KQL Querying: Run searches, filters, aggregations, joins, and time-series analysis against Kusto tables.
  • Schema Discovery: Inspect cluster, database, and table structure before writing queries.
  • Operational Fallbacks: Use Azure CLI and REST-based alternatives when MCP tools are unavailable or timing out.
  • Use Case: Analyze recent logs, investigate correlated errors, build time-based telemetry charts, or identify top events by category across large datasets.

Quick Start

Ask the assistant to query your Azure Data Explorer database for the last hour of error events and summarize the results by event type.

Frequently Asked Questions about azure-kusto

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

FAQPage Schema
How do I query Azure Data Explorer using KQL without writing complex patterns from scratch?

You can query Azure Data Explorer by asking the assistant to run KQL searches, filters, aggregations, and joins. This Skill handles KQL execution for log analytics and telemetry exploration without requiring you to handcraft query patterns.

What is the best way to discover table schemas in an Azure Data Explorer database?

Schema discovery in Azure Data Explorer is handled by inspecting cluster, database, and table structures before writing queries. This allows you to retrieve table schemas directly to understand your data layout prior to KQL execution.

How do I analyze time-series telemetry data in Azure Data Explorer?

Time-series monitoring in Azure Data Explorer is performed by running KQL aggregations against telemetry tables. You can build time-based telemetry charts and identify top events by category across large datasets stored in ADX clusters.

Can I use Azure CLI to query Kusto when MCP tools are unavailable or timing out?

Yes, you can use Azure CLI and REST-based alternatives as operational fallbacks when direct MCP tools fail. This Skill supports CLI fallback to maintain Kusto query execution and schema retrieval during tool timeouts.

Does Azure Data Explorer support cross-table correlation for investigating log errors?

Cross-table correlation in Azure Data Explorer allows you to investigate correlated errors and analyze recent logs. You can execute KQL joins across multiple tables within ADX databases to identify correlated operational events.

What are the limitations of using KQL for Azure Data Explorer log analytics?

KQL log analytics requires support for cluster listing, database listing, and table schema retrieval to function properly. Direct KQL execution may require CLI fallback when MCP tools become unavailable or experience timeouts.