What metrics can I track with the AnomalyArmor Data Profiling skill?

You can track a wide range of metrics including row counts, data freshness, null rates, distinct counts (cardinality), and min/max value ranges for numeric or date columns.

How do I view historical data trends?

The skill provides a snapshots feature that allows you to retrieve and visualize historical values for any metric over a specific timeframe, such as row count growth over the last 30 days.

Does this skill require an external API key?

Yes, it requires an AnomalyArmor API key configured in your environment or config file, as well as the anomalyarmor Python SDK.

Can I create new profiling metrics using this skill?

Yes, you can programmatically create new metrics for specific tables and columns, defining the metric type and capture interval directly through the skill interface.

AnomalyArmor Data Profiling

Name: AnomalyArmor Data Profiling
Author: anomalyarmor

byanomalyarmor

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Analíticas y Monitorización

Profiles database tables and columns to analyze statistics, data distributions, and cardinality for enhanced data observability.

The AnomalyArmor Data Profiling skill empowers Claude to perform deep analysis of data structures by calculating essential metrics such as row counts, null rates, and distinct value distributions. It enables developers and data engineers to monitor data freshness, identify cardinality patterns, and track historical trends through snapshots. By providing a clear picture of data characteristics, this skill helps ensure high data quality and reliability across data warehouse environments, making it indispensable for debugging data pipelines and maintaining observability.

Características Principales

011 GitHub stars

02Detailed column-level analysis for null rates, cardinality, and value ranges

03Automated creation of new profiling metrics for specific database assets

04Historical trend tracking with metric snapshots to visualize data growth

05Immediate metric capture triggers for real-time data status updates

06Comprehensive table-level metrics including row counts and data freshness

Casos de Uso

01Identifying columns with high null rates or unexpected distribution patterns

02Monitoring data drift and row count anomalies in production warehouses

03Performing initial data exploration and quality assessment on new datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anomalyarmor/agents profile

For use in Claude.ai and ChatGPT

Download Skill