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

File Format Converter

Overview

This skill provides automated assistance for file format converter tasks within the Data Pipelines domain.

When to Use

This skill activates automatically when you:

• Mention "file format converter" in your request
• Ask about file format converter 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 file format converter
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 file format converter" 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