data-validation

Name: data-validation
Availability: InStock
Author: badhope

Validate data quality in pipelines using Python frameworks like pydantic and great_expectations.

Updated Aug 27, 2026

Search Tags:#pydantic #pandas #etl #data-quality #great-expectations #data-validation #schema-validation

Ready to run

npx skills add https://github.com/badhope/skill --skill data-validation-badhope

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/badhope/skill/tree/main/.trae/skills/domains/data/data-validation
Command: npx skills add https://github.com/badhope/skill --skill data-validation-badhope

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data validation and quality assurance are critical for data pipelines to prevent dirty data from entering analytics or production systems. This skill provides structured patterns and best practices to enforce data integrity across ingestion, transformation, and storage using Python-based tools such as pydantic, great_expectations, pandera, and cerberus.

Core Features & Use Cases

Schema validation, data quality checks, anomaly detection, data profiling, and validation rules across ETL workflows.
API input validation, ETL data quality gates, database constraint validation, configuration validation, and message queue data validation.

Quick Start

Install and configure the recommended validation framework stack (pydantic, great-expectations, pandera, cerberus) and integrate with your ETL pipeline to automatically validate incoming data and surface violations.

Frequently Asked Questions about data-validation

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

FAQPage Schema

How do I validate data quality in ETL pipelines using Python?▼

Validate data quality in ETL pipelines by enforcing schema validation, data profiling, anomaly detection, and rule-based checks using Python frameworks like pydantic, great_expectations, pandera, and cerberus to prevent dirty data from entering production systems.

What is the best way to enforce schema validation for API inputs?▼

Enforce schema validation for API inputs using pydantic to define structured data models and validate incoming payloads, ensuring end-to-end data integrity across ingestion before data reaches transformation or storage layers.

Does pydantic work with great_expectations for data profiling and anomaly detection?▼

Yes, pydantic works alongside great_expectations, pandera, and cerberus to provide a comprehensive validation stack, combining schema validation for API inputs with data profiling and anomaly detection for ETL workflows.

Can I use pandera for database constraint validation in data pipelines?▼

You can use pandera for database constraint validation and data quality gates, applying rule-based checks and schema validation across ingestion, transformation, and storage stages to maintain end-to-end pipeline integrity.

When do I need data validation frameworks in ETL workflows?▼

You need data validation frameworks in ETL workflows when preventing dirty data from entering analytics or production systems, requiring schema validation, data profiling, anomaly detection, and rule-based checks across ingestion and transformation.

Why does my ETL pipeline need data quality gates and configuration validation?▼

ETL pipelines need data quality gates and configuration validation to automatically validate incoming data, surface violations, and enforce database constraints, preventing dirty data from entering analytics or production systems.