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data-quality-checker

data-quality-checker

github.com/jeremylongshoreData & databases2.9k36 installsUpdated 2d ago
Repository

About data-quality-checker

A skill for data-pipeline work focused on data quality checks. It offers step-by-step guidance, generates code and configurations, and validates outputs against common standards.

Key features

  1. 01Step-by-step data quality guidance
  2. 02Code and configuration generation
  3. 03Best-practice recommendations
  4. 04Output validation

Use cases

  1. 01Planning data quality checks
  2. 02Creating pipeline configurations
  3. 03Reviewing ETL or streaming workflows

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill data-quality-checker

After installing, type/data-quality-checkerin Claude Code to confirm it loaded.

Details

From the project README

Data Quality Checker

Overview

This skill provides automated assistance for data quality checker tasks within the Data Pipelines domain.

When to Use

This skill activates automatically when you:

  • Mention "data quality checker" in your request
  • Ask about data quality checker patterns or best practices
  • Need help with data pipeline skills covering etl, data transformation, workflow orchestration, and streaming data processing.

Instructions

  1. Provides step-by-step guidance for data quality checker
  2. Follows industry best practices and patterns
  3. Generates production-ready code and configurations
  4. Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with data quality checker" Result: Provides step-by-step guidance and generates appropriate configurations

Prerequisites

  • Relevant development environment configured
  • Access to necessary tools and services
  • Basic understanding of data pipelines concepts

Output

  • Generated configurations and code
  • Best practice recommendations
  • Validation results

Error Handling

ErrorCauseSolution
Configuration invalidMissing required fieldsCheck documentation for required parameters
Tool not foundDependency not installedInstall required tools per prerequisites
Permission deniedInsufficient accessVerify credentials and permissions

Resources

  • Official documentation for related tools
  • Best practices guides
  • Community examples and tutorials

Related Skills

Part of the Data Pipelines skill category. Tags: etl, airflow, spark, streaming, data-engineering

Comments

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