Which tools can this skill access?

The skill has permission to use Read, Write, Edit, Bash, and Grep tools to help manage and validate your project files.

Does it generate production-ready code?

Yes, it is designed to generate production-grade configurations and code snippets that follow industry-standard schema validation patterns.

When does this skill activate?

The skill activates automatically when you mention 'schema validator' or ask for help with data pipeline patterns, ETL processes, or workflow orchestration.

Can I use this for Airflow or Spark projects?

Yes, the skill includes patterns and best practices specifically for data engineering tools like Airflow, Spark, and various streaming data platforms.

What is the Schema Validator skill for Claude Code?

It is a specialized capability that provides automated assistance for designing, implementing, and validating data schemas within data engineering pipelines.

Schema Validator

Name: Schema Validator
Author: jeremylongshore

byjeremylongshore

•

983

Gestión de Bases de Datos

Validates and manages data schemas within ETL and data pipeline workflows to ensure structural integrity and reliability.

Acerca de

The Schema Validator skill offers automated assistance for developers building complex data pipelines, focusing on data quality and consistency. It provides expert guidance for implementing schema checks in ETL processes, orchestration tools like Airflow, and high-volume streaming data systems. By generating production-ready code and following industry best practices, this skill helps reduce pipeline failures caused by unexpected data formats and ensures seamless data transformations across various data engineering environments.

Características Principales

Automated schema validation for ETL pipelines
Production-ready configuration generation
Step-by-step guidance for data transformations
Real-time data stream validation support
Industry-standard data engineering patterns
983 GitHub stars

Casos de Uso

Implementing data integrity checks in Spark or Airflow workflows
Designing robust data transformation logic for streaming data
Automating structural validation for large-scale data migrations

Acerca de

Características Principales

Automated schema validation for ETL pipelines
Production-ready configuration generation
Step-by-step guidance for data transformations
Real-time data stream validation support
Industry-standard data engineering patterns
983 GitHub stars

Casos de Uso

Implementing data integrity checks in Spark or Airflow workflows
Designing robust data transformation logic for streaming data
Automating structural validation for large-scale data migrations