data-validator

Validate data against JSON schemas, business rules, and quality standards.

27|4|Updated Oct 20, 2025

Search Tags:#data integrity #data quality #compliance #data validation #schema validation #business rules

Ready to run

npx skills add https://github.com/CuriousLearner/devkit --skill data-validator-curiouslearner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validator
Source: https://github.com/CuriousLearner/devkit/tree/main/skills/data-validator
Command: npx skills add https://github.com/CuriousLearner/devkit --skill data-validator-curiouslearner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jsonschema, pandas, ajv-formats, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need to ensure data accuracy, consistency, and adherence to predefined standards, preventing errors and improving the reliability of data-driven processes.

Core Features & Use Cases

Schema Validation: Validates data against JSON schemas, database schemas, and API formats.
Business Rules Validation: Enforces custom, domain-specific logic and constraints.
Data Quality Checks: Identifies completeness issues, duplicates, outliers, and format inconsistencies.
Use Case: Before ingesting a new customer dataset, use this Skill to validate that all required fields are present, email formats are correct, and customer IDs are unique, preventing bad data from entering your system.

Quick Start

Validate the data in 'customer_data.csv' against the 'customer_schema.json' file.

Frequently Asked Questions about data-validator

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

FAQPage Schema

How do I validate JSON data against a schema in Python?▼

To validate JSON data against a schema in Python, you can use this Skill to enforce predefined schemas, API formats, and business rules. It checks data structures and generates detailed validation reports with error messages and fix suggestions.

Can I check CSV data for duplicates and format inconsistencies before database ingestion?▼

Yes, you can check CSV data for duplicates and format inconsistencies before database ingestion. This Skill performs data quality checks to identify completeness issues, outliers, and duplicates using pandas and scipy.

How do I enforce custom business rules and constraints on a dataset?▼

You enforce custom business rules and constraints on a dataset by defining domain-specific logic within the validation process. This Skill checks your data against these custom rules and outputs detailed compliance reports.

Does this data validation approach support API request and response format verification?▼

Yes, this data validation approach supports API request and response format verification. It validates payloads against predefined API formats using jsonschema and ajv-formats to ensure strict compliance.

What is the best way to generate detailed data quality reports with fix suggestions?▼

The best way to generate detailed data quality reports with fix suggestions is to run your datasets through this validation process. It outputs comprehensive reports highlighting schema violations, business rule failures, and actionable error messages.

Why does JSON schema validation fail for missing required fields during data ingestion?▼

JSON schema validation fails for missing required fields during data ingestion because the data lacks mandatory elements defined in the schema. This Skill detects these completeness issues, halts bad data entry, and suggests specific fixes.