How does this skill improve AI-generated code?

It provides Claude with domain-specific context and constraints, ensuring the code it generates adheres to strict type safety and data integrity standards.

Is data quality monitoring part of the implementation?

The skill provides the logic and patterns necessary to implement monitoring hooks within your data validation layer to track quality metrics.

What version of Pydantic does this skill support?

The skill is specifically designed and optimized for Pydantic v2, utilizing its latest performance and syntax improvements.

Does it include templates for API validation?

Yes, it includes Pydantic model templates specifically designed for API request/response validation and error handling.

Can I use this for databases other than PlanetScale?

Yes, while it includes specific optimizations for PlanetScale PostgreSQL, the validation patterns and schema designs are applicable to most SQL-based environments.

Data Validation & Schema Quality

Name: Data Validation & Schema Quality
Author: greyhaven-ai

bygreyhaven-ai

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Gestión de Bases de Datos

Implements robust data validation and schema alignment using Pydantic v2 for PlanetScale and API services.

Acerca de

This skill empowers Claude to architect production-ready data validation layers using Pydantic v2 models. It provides specialized guidance on creating schema contracts, aligning models with PlanetScale PostgreSQL databases, and implementing comprehensive data quality monitoring. By leveraging standardized templates and validation patterns, it ensures that your application maintains high data integrity, type safety, and seamless synchronization between API layers and database structures.

Características Principales

15 GitHub stars
Real-time data quality monitoring and integrity checks
Standardized templates for complex schema contracts
Database schema alignment for PlanetScale PostgreSQL
Advanced validation strategy implementation for APIs
Pydantic v2 model generation and validation patterns

Casos de Uso

Synchronizing application data models with PostgreSQL schemas
Implementing data quality assurance for high-traffic pipelines
Validating incoming API request bodies and outgoing responses

Acerca de

Características Principales

15 GitHub stars
Real-time data quality monitoring and integrity checks
Standardized templates for complex schema contracts
Database schema alignment for PlanetScale PostgreSQL
Advanced validation strategy implementation for APIs
Pydantic v2 model generation and validation patterns

Casos de Uso

Synchronizing application data models with PostgreSQL schemas
Implementing data quality assurance for high-traffic pipelines
Validating incoming API request bodies and outgoing responses