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Samuelca6399
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data-quality

Validate schemas, define data contracts, and monitor pipeline freshness.

Updated Aug 27, 2026
Search Tags:#data validation#data lineage#schema drift#data contracts#great expectations#data monitoring#data observability
One-click install
npx skills add https://github.com/Samuelca6399/AbsolutelySkilled --skill data-quality-samuelca6399
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality
Source: https://github.com/Samuelca6399/AbsolutelySkilled/tree/main/skills/data-quality
Command: npx skills add https://github.com/Samuelca6399/AbsolutelySkilled --skill data-quality-samuelca6399

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Data quality failures quietly propagate through pipelines, corrupting dashboards, training models on flawed data, and breaking downstream consumers without clear ownership or early signals.

Core Features & Use Cases

  • Validate schemas and expectations at boundaries: Add ingress/serving quality gates using schema validation and Great Expectations suites so issues like null spikes, missing columns, and constraint violations are caught early.
  • Create data contracts between producers and consumers: Define versioned YAML contracts covering schema, semantics, SLAs, and ownership to prevent breaking changes and clarify escalation paths.
  • Implement monitoring and lineage for impact analysis: Set up freshness/volume anomaly checks and track lineage (including impact of schema changes) using OpenLineage-compatible patterns.

Quick Start

Run data-quality guidance to define an orders Great Expectations suite, a data contract, and the monitoring checks for freshness, volume anomalies, and schema drift.

Frequently Asked Questions about data-quality

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

FAQPage Schema
How do I write Great Expectations suites to catch schema drift and null spikes in my data pipeline?▼

Use Great Expectations suites and checkpoints to validate data at pipeline boundaries, catching schema drift, null spikes, and constraint violations before they break downstream consumers.

What are data contracts and how do they prevent breaking changes between producers and consumers?▼

Data contracts are versioned YAML definitions covering schema, semantics, SLAs, and ownership that prevent breaking changes and clarify escalation paths between data producers and consumers.

How do I set up data monitoring and lineage tracking for impact analysis?▼

Set up freshness and volume anomaly checks alongside OpenLineage-compatible tracking to perform column-level dependency analysis and answer impact questions when schema changes occur.

Can I use this approach for batch processing pipelines or does it only support streaming data?▼

Validation, monitoring, contracts, and lineage tracking apply to production data pipelines, enforcing schemas and detecting anomalies regardless of whether the pipeline processes batch or streaming data.

What is the best way to prevent silent data corruption in downstream dashboards and models?▼

Implement ingress quality gates using schema validation, expectation suites, and data contracts to prevent silent data corruption from propagating into dashboards and training models.

Why does my data pipeline need column-level lineage and expectation suites?▼

Column-level lineage tracks dependencies for impact analysis, while expectation suites validate constraints at boundaries, together preventing silent data corruption and clarifying ownership.

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