elementary-data-quality

Automate anomaly detection and schema validation for Microsoft Fabric data quality monitoring.

Updated Mar 16, 2026

Search Tags:#data quality #dbt #anomaly detection #Microsoft Fabric #Elementary

Ready to run

npx skills add https://github.com/accelerate-data/vibedata-official --skill elementary-data-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: elementary-data-quality
Source: https://github.com/accelerate-data/vibedata-official/tree/main/plugins/vibedata-dbt-skills/skills/elementary-data-quality
Command: npx skills add https://github.com/accelerate-data/vibedata-official --skill elementary-data-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dbt, elementary-data, dbt-msft/tsql_utils, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of ensuring data quality and anomaly detection on Microsoft Fabric, leveraging dbt and Elementary for streamlined data observability.

Core Features & Use Cases

Data Quality Testing: Implement comprehensive anomaly detection and schema tests to monitor data integrity.
Layered Testing Strategy: Apply targeted tests across Bronze, Silver, and Gold layers to catch issues early and protect data quality.
Volume and Column Anomalies: Detect unexpected changes in data volume and column metrics, including missing data, outliers, and schema drift.
Schema Changes: Monitor schema changes to ensure consistency with business logic and downstream systems.
Use Case: For a new data pipeline on Fabric, this Skill can automatically set up and maintain a robust data quality monitoring system, alerting on issues before they impact critical business processes.

Quick Start

Install the elementary-data-quality skill using Claude Code:

/plugin install elementary-data-quality@vibedata-dbt-skills

Frequently Asked Questions about elementary-data-quality

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

FAQPage Schema

How do I automate data quality checks and anomaly detection on Microsoft Fabric?▼

Automate data quality checks on Microsoft Fabric by leveraging dbt and Elementary to enforce standards, detect schema drift, and monitor anomalies across bronze, silver, and gold data layers.

What is the best way to monitor schema changes and data volume anomalies in dbt?▼

Monitor schema changes and volume anomalies in dbt by applying targeted tests across data layers to catch missing data, outliers, and schema drift before impacting downstream systems.

Does the Elementary anomaly detection Skill require dbt-msft/tsql_utils?▼

Yes, Elementary anomaly detection on Fabric requires dbt, elementary-data, and the dbt-msft/tsql_utils package to execute schema validation and data observability workflows.

Can I apply layered data testing strategies across bronze, silver, and gold layers in Fabric?▼

Yes, you can apply a layered testing strategy across bronze, silver, and gold layers in Fabric to catch data integrity issues early and protect critical business processes.

How do I set up data observability for a new pipeline on Microsoft Fabric?▼

Set up data observability for a new Fabric pipeline by configuring schema tests and anomaly detection with dbt and Elementary to automatically alert on unexpected volume or column metric changes.

What type of column anomalies can Elementary detect on Fabric?▼

Elementary detects unexpected changes in column metrics, including missing data, outliers, and schema drift, ensuring data consistency with business logic and downstream systems.