/flux-health

FluxData & Databases

Audits data quality and pipeline health across multiple dimensions: freshness of data in each table versus expected SLA, schema drift between environments, null rates on fields that should never be null, orphaned records from missing foreign key constraints, and execution status of all scheduled pipelines.

Install

This skill

Install Flux Health

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install this skill

$ claude plugin install flux-health@tonone-ai

The agent

Install Flux

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install Flux

$ claude plugin install flux@tonone-ai

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Full installation guide

Invoke this skill

Command|$ /flux-health

When to use

When dashboards are showing unexpected numbers and you need to determine whether the data itself is the problem. When a pipeline has been running for a while and you want to verify it is producing clean data. As a regular data quality audit before a product or analytics release.

Deep dive

AI Data Quality and Pipeline Health Check

Data goes stale silently. /flux-health checks freshness, schema drift, null rates, orphaned records, and pipeline status with a prioritized fix list.

Read the article

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Designs a database schema with proper normalization, appropriate denormalization for high-frequency read paths, indexes for the expected query patterns, constraints that enforce data integrity at the database level, and migration files ready to run. Explains the reasoning behind every design decision.

When designing a new data model from scratch and w…

FluxData

/flux-migrate

Writes zero-downtime database migrations: reversible steps designed to run while the application is serving traffic, safe for concurrent reads and writes during the migration window, with an explicit rollback plan for every step. Handles column renames, backfills, and index creation on large tables safely.

Whenever making schema changes on a live productio…

FluxData

/flux-pipeline

Builds a data pipeline from source to destination: extraction from APIs, databases, or files; transformation with type coercion, deduplication, and business logic; loading into the target store; with error handling, retry logic, dead letter queues, and scheduled or event-driven execution.

When moving data between systems on a schedule. Wh…

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