--- name: "data-partitioner" description: | Process data partitioner operations. Auto-activating skill for Data Pipelines. Triggers on: data partitioner, data partitioner Part of the Data Pipelines skill category. Use when working with data partitioner functionality. Trigger with phrases like "data partitioner", "data partitioner", "data". allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep" version: 1.0.0 license: MIT author: "Jeremy Longshore " compatible-with: claude-code --- # Data Partitioner ## Overview This skill provides automated assistance for data partitioner tasks within the Data Pipelines domain. ## When to Use This skill activates automatically when you: - Mention "data partitioner" in your request - Ask about data partitioner 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 partitioner 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 partitioner" 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