data-quality-enforcement

Enforce data quality rules on pilot data streams and repositories.

1|Updated Jan 20, 2026

Search Tags:#validation #schema #data quality #auditing #anomalies #data streams

Ready to run

npx skills add https://github.com/profmikegreene/gotei --skill data-quality-enforcement

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-enforcement
Source: https://github.com/profmikegreene/gotei/tree/main/Gotei_Skills/data-quality-enforcement
Command: npx skills add https://github.com/profmikegreene/gotei --skill data-quality-enforcement

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data quality issues in pilot data streams and repositories can lead to inaccurate analytics, inconsistent reporting, and regulatory gaps. This skill provides a structured approach to detect missing values, enforce schema compliance, and identify data anomalies before analysis and decision-making.

Core Features & Use Cases

Automated checks for missing values, schema validation, and cross-field consistency across pilot data pipelines.
Anomaly detection and audit reporting to guide remediation and governance.
Use Case: Validate a new data feed before analytics to ensure reliable dashboards and compliant reports.
Integration with pre-processing pipelines to enforce data quality at ingest and transformation stages.

Quick Start

Run data quality checks on your pilot data source and review the resulting report to remediate issues.

Frequently Asked Questions about data-quality-enforcement

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

FAQPage Schema

How do I validate data schemas and detect anomalies in pilot data streams?▼

Schema validation and anomaly detection run directly on pilot data streams to identify missing values, schema deviations, and cross-field inconsistencies before analytics. This produces actionable quality reports and remediation guidance.

What is data quality enforcement and when do I need it for my data pipelines?▼

Data quality enforcement validates data types, value ranges, and cross-field consistency across ingestion, transformation, and analytics workflows. You need it when pilot data streams require reliable, auditable datasets and governance-ready reports.

Can I enforce data quality checks during data ingestion and transformation stages?▼

Yes, data quality rules integrate with pre-processing pipelines to enforce validation at both data ingestion and transformation stages. This ensures schema compliance and detects anomalies before data reaches analytics dashboards.

What's the best way to audit data quality and remediate missing values in repositories?▼

Auditing data quality involves running automated checks for missing values, schema deviations, and cross-field consistency on data repositories. This generates audit reports that guide remediation and ensure governance-ready datasets.

Does data quality enforcement work for cross-field consistency validation in data streams?▼

Yes, cross-field consistency validation is a core function applied across pilot data streams and repositories. It enforces data quality rules to detect anomalies and schema deviations, producing actionable quality reports for remediation.

What are the limitations of automated data quality checks for pilot data repositories?▼

Automated data quality checks focus on schema validation, value ranges, and cross-field consistency but require integration at ingest and transformation stages. They produce remediation guidance, meaning manual intervention is still needed to resolve detected anomalies.