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data-validator

Validate, cleanse, and enrich data against JSON schemas or Pydantic models.

Updated Feb 18, 2026
Search Tags:#pydantic#data quality#data validation#schema validation#json schema#data cleansing
One-click install
npx skills add https://github.com/Greenmamba29/skillsdotmd_web --skill data-validator-greenmamba29
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validator
Source: https://github.com/Greenmamba29/skillsdotmd_web/tree/main/.agents/skills/data-validator
Command: npx skills add https://github.com/Greenmamba29/skillsdotmd_web --skill data-validator-greenmamba29

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that incoming data is accurate, consistent, and conforms to predefined standards before it is processed or stored, preventing errors and data corruption.

Core Features & Use Cases

  • Schema Validation: Validates data against JSON Schema or Pydantic models.
  • Data Cleansing: Removes inconsistencies like extra whitespace and normalizes formats (e.g., dates).
  • Data Enrichment: Adds computed fields to valid records based on configuration.
  • Error Reporting: Clearly flags invalid records with specific error messages.
  • Use Case: Automatically validate and clean customer sign-up data from a web form before adding it to your CRM.

Quick Start

Use the data-validator skill to validate the incoming JSON payload against the user schema.

Frequently Asked Questions about data-validator

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

FAQPage Schema
How do I validate incoming JSON payloads against a schema before persisting them?▼

You can validate incoming JSON payloads by checking records against specified JSON schemas or Pydantic models to ensure data quality prior to persistence or further processing.

What's the best way to automatically cleanse and normalize user sign-up data?▼

Automatically cleansing user sign-up data involves removing inconsistencies like extra whitespace and normalizing formats such as dates before adding the valid records to your CRM.

Can I enrich valid records with computed fields during the data validation process?▼

Yes, you can enrich valid records by adding computed fields based on your configuration during the data validation process.

Does this data validation approach work with Python 3.12 and webhook triggers?▼

Yes, this data validation approach supports Python 3.12 runtime environments and can be triggered via webhooks for real-time data validation.

How are invalid records handled when schema validation fails?▼

When schema validation fails, invalid records are clearly flagged with specific error messages to identify exactly what data quality standards were not met.

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