Agent skill · data analytics · aiskillstore
azure-kusto
Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.
Why this skill is useful
Provides specific KQL query patterns and techniques for analyzing log and telemetry data in Azure Data Explorer that the AI wouldn't reliably generate on its own.
What it needs
Requires azure account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Azure Data Explorer (Kusto) Query & Analytics Execute KQL queries and manage Azure Data Explorer resources for fast, scalable big data analytics on log, telemetry, and time series data. Skill Activation Triggers Use this skill immediately when the user asks to: "Query my Kusto database for [data pattern]" "Show me events in the last hour from Azure Data Explorer" "Analyze logs in my ADX cluster" "Run a KQL query on [database]" "What tables are in my Kusto database?" "Show me the schema for [table]" "List my Azure Data Explorer clusters" "Aggregate telemetry data by [dimension]" "Create a time series chart from my logs" Key Indicators: Mentions "Kusto", "Azure Data Explorer", "ADX", or "KQL" Log analytics or telemetry analysis requests Time series data exploration IoT data analysis queries SIEM or security analytics tasks Requests for data aggregation on large datasets Performance monitoring or APM queries Overview This skill enables querying and managing Azure Data Explorer (Kusto), a fast and highly scalable data exploration service optimized for log and telemetry data. Azure Data Explorer provides sub-second query performance on billions of records using the Kusto Query Language (KQL). Key capabilities: Query Execution: Run KQL queries against massive datasets Schema Exploration: Discover tables, columns, and data types Resource Management: List clusters and databases Analytics: Aggregations, time series, anomaly detection, machine learning Core Workflow 1. Discover Resources: List available clusters and databases in subscription 2. Explore Schema: Retrieve table structures to understand data model 3. Query Data: Execute KQL queries for analysis, filtering, aggregation 4. Analyze Results: Process query output for insights and reporting Query Patterns Pattern 1: Basic Data Retrieval Fetch recent records from a table with simple filtering. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/azure-kusto