| 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
- Provides step-by-step guidance for data quality checker
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- 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
| Error | Cause | Solution |
|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify 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
