flux-health

Detect data quality issues and pipeline health across databases and warehouses.

69|8|Updated Mar 16, 2026
One-click install
npx skills add https://github.com/tonone-ai/tonone --skill flux-health
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: flux-health
Source: https://github.com/tonone-ai/tonone/tree/main/team/flux/skills/flux-health
Command: npx skills add https://github.com/tonone-ai/tonone --skill flux-health

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data quality and pipeline health checks across the data stack to catch freshness gaps, drift, and reliability issues before they impact decisions.

Core Features & Use Cases

  • Freshness checks across key tables and sources
  • Schema drift detection between code-defined schemas and actual tables
  • Null-rate and data-quality issue detection
  • Identification of orphaned records and referential integrity issues
  • Unified pipeline health reporting and status monitoring for dashboards
  • Customizable thresholds and alerting for automated remediation

Quick Start

Run flux-health to scan your data sources and generate a consolidated health report.

Frequently Asked Questions about flux-health

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

FAQPage Schema
How do I check data freshness and pipeline health across my warehouse?

Data freshness and pipeline health checks run structured scans across your databases and warehouses to detect reliability issues. This Skill scans sources like BigQuery, Redshift, and Snowflake to generate a consolidated health report.

How do I detect schema drift in my dbt project?

Detecting schema drift in dbt projects requires comparing code-defined schemas against actual table structures. This Skill implements drift detection checks to identify mismatches and schema changes before they impact pipelines.

What is the best way to monitor null rates and orphaned records in a data pipeline?

Monitoring null rates and orphaned records requires applying standardized health checks to your data stack. This Skill performs null-rate detection and referential integrity checks to identify orphaned records and data-quality issues.

Does this data quality monitoring approach work with Airflow and Dagster?

Yes, data quality monitoring works with Airflow and Dagster by applying unified pipeline status monitoring. It provides customizable thresholds and alerting for automated remediation across these orchestration tools and warehouses.

Can I customize alerting thresholds for data quality checks?

You can customize alerting thresholds for data quality checks by setting specific parameters for freshness, null rates, and pipeline status. This enables automated remediation and generates structured health-report outputs.