data-partitioner

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 <[e-mail]>
tags	["data","data-engineering"]
compatibility	Designed for 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