Data Validator

Perform comprehensive data quality checks on datasets — validate schemas, detect anomalies, find duplicates, and enforce data contracts. Essential for ETL pipelines where bad data silently corrupts downstream analytics and dashboards.

Overview

The Data Validator is a specialized skill for AI agents, part of the TerminalSkills/skills repository on GitHub. This tool addresses the critical need for data integrity within automated workflows and ETL pipelines. By enabling agents like Claude, Gemini, and Codex to perform comprehensive quality checks, it helps prevent the silent corruption of downstream analytics and dashboards. The skill facilitates schema validation, anomaly detection, and duplicate identification while enforcing strict data contracts. As part of a repository with 72 stars, this skill provides a structured approach to maintaining dataset health. It is designed for developers using coding-focused agents to ensure that incoming data meets predefined standards before further processing or visualization occurs.

Use Cases

Verifying dataset schemas against predefined contracts during ETL pipeline execution.
Identifying statistical anomalies and duplicate records in raw data files.
Ensuring data quality before feeding information into analytics dashboards.

Install Notes

# Review source first
open https://github.com/TerminalSkills/skills/blob/main/skills/data-validator/SKILL.md

Copy or clone the skill folder into your agent skills directory after reviewing its instructions and scripts.

Security Notes

Users should ensure that the AI agent has appropriate read permissions for the datasets being analyzed. When processing sensitive or regulated information, verify that the agent's environment complies with local data privacy standards, as the skill interacts directly with dataset contents to perform validation and anomaly detection.

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