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data-validation

Validate data quality, types, schemas, and consistency across systems using Python.

4|2|Updated Feb 26, 2026
Search Tags:#data integrity#data quality#data validation#schema validation#data pipelines#anomaly detection
One-click install
npx skills add https://github.com/wojons/skills --skill data-validation-wojons
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/wojons/skills/tree/main/skills/data-validation
Command: npx skills add https://github.com/wojons/skills --skill data-validation-wojons

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for accurate, consistent, and reliable data by providing comprehensive validation capabilities across various systems and data formats.

Core Features & Use Cases

  • Schema Validation: Ensures data conforms to predefined structures (JSON Schema, Avro, Protobuf, XML Schema).
  • Data Quality Checks: Verifies completeness, accuracy, consistency, and timeliness of data.
  • Anomaly Detection: Identifies unusual patterns or outliers in datasets.
  • Use Case: When migrating a large customer database, use this Skill to validate that all records adhere to the new schema, check for missing essential fields like email addresses, and ensure data consistency between the old and new systems before the final cutover.

Quick Start

Use the data-validation skill to validate the attached file 'customer_data.csv' against the schema defined in 'customer_schema.json'.

Frequently Asked Questions about data-validation

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

FAQPage Schema
How do I validate JSON Schema, Avro, Protobuf, and XML Schema structures?▼

You validate data quality and schemas across various systems using Python scripts and libraries. It supports schema validation against JSON Schema, Avro, Protobuf, and XML Schema, alongside data quality checks and anomaly detection.

What's the best way to detect anomalies and outliers in my datasets?▼

Anomaly detection identifies unusual patterns or outliers in datasets using Python scripts. This approach helps maintain data quality by catching inconsistencies during data pipeline processing or before system migrations.

How do I check data completeness and consistency before a database migration?▼

Data quality checks verify completeness, accuracy, consistency, and timeliness of data before migration. You can validate that all records adhere to the new schema and check for missing essential fields like email addresses.

Can I validate referential integrity across different systems using Python?▼

Referential integrity validation across systems is supported using Python scripts and libraries. This ensures data consistency and reliability when moving records between different platforms or databases.

Does this data validation approach work with CSV files and JSON schemas?▼

Yes, data validation works with CSV files against schemas defined in JSON. You can validate attached files like customer_data.csv against a schema defined in customer_schema.json to ensure data integrity.

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