| name | pyspark-transformer |
| description | Transform pyspark transformer operations. Auto-activating skill for Data Pipelines.
Triggers on: pyspark transformer, pyspark transformer
Part of the Data Pipelines skill category. Use when working with pyspark transformer functionality. Trigger with phrases like "pyspark transformer", "pyspark transformer", "pyspark".
|
| allowed-tools | Read, Write, Edit, Bash(cmd:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
Pyspark Transformer
Overview
This skill provides automated assistance for pyspark transformer tasks within the Data Pipelines domain.
When to Use
This skill activates automatically when you:
- Mention "pyspark transformer" in your request
- Ask about pyspark transformer 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 pyspark transformer
- 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 pyspark transformer"
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
