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데이터 및 연구 · SKILL.md

data-quality-checker

data-quality-checker

Validate data quality checker operations. Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category. Use when working with data quality checker functionality. Trigger with phrases like "data quality checker", "data checker", "data".

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: "data-quality-checker" description: | Validate data quality checker operations. Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category. Use when working with data quality checker functionality. Trigger with phrases like "data quality checker", "data checker", "data". allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep" version: 1.0.0 license: MIT author: "Jeremy Longshore jeremy@intentsolutions.io"

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

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