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AbsolutelySkilled
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data-quality

Enforces data quality with validation, lineage tracking, and contracts.

207|31|Updated Mar 14, 2026
Search Tags:#monitoring#validation#contracts#data-pipelines#data-quality#lineage#great-expectations
One-click install
npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill data-quality-absolutelyskilled
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality
Source: https://github.com/AbsolutelySkilled/AbsolutelySkilled/tree/main/skills/data-quality
Command: npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill data-quality-absolutelyskilled

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Data quality is essential to ensure data is accurate, complete, consistent, timely, and trustworthy as it flows through pipelines and systems. Without explicit quality gates, bad data propagates silently - corrupting dashboards, training flawed models, and breaking downstream consumers.

Core Features & Use Cases

  • Schema validation at ingestion to prevent bad data from entering the warehouse.
  • Data contracts and lineage tracking to document ownership and dependencies.
  • Great Expectations-based testing and continuous monitoring to detect anomalies in production.

Quick Start

Ask me to set up data quality checks and lineage for your dataset and I will generate GX expectations and contract guidance.

Frequently Asked Questions about data-quality

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

FAQPage Schema
How do I set up data quality checks and lineage tracking for my pipelines?▼

Data quality checks are set up by generating Great Expectations tests, defining data contracts, and emitting lineage across ingestion, processing, and serving layers to prevent bad data from propagating downstream.

What is a data contract and when do I need it for schema validation?▼

A data contract documents ownership and dependencies to enforce schema validation at ingestion. You need it to prevent corrupt or incomplete data from entering your warehouse and breaking downstream consumers.

Does this data validation approach work with Great Expectations and OpenLineage?▼

Yes, this approach supports Great Expectations for generating schema tests and OpenLineage for tracking lineage emission, ensuring continuous monitoring and production-grade data quality across your systems.

What's the best way to monitor data quality across ingestion, processing, and serving layers?▼

The best way to monitor data quality across pipeline layers is by implementing GX expectations, enforcing data contracts, and tracking lineage to detect anomalies in production and ensure data remains trustworthy.

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