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home / skills / jeremylongshore / claude-code-plugins-plus-skills / file-format-converter

file-format-converter skill

/skills/11-data-pipelines/file-format-converter

This skill automates file format converter tasks in data pipelines, generating production-ready code, configurations, and validation guidance.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill file-format-converter

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.1 KB
---
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 <[email protected]>"
---

# 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

Overview

This skill automates file format conversion tasks within data pipelines, helping convert between formats like CSV, JSON, Parquet, Avro, and more. It generates code, configuration snippets, and validation checks to make conversions production-ready. Use it to standardize formats, optimize storage, and enable downstream analytics.

How this skill works

The skill analyzes the source and target formats, inspects schema and metadata, and recommends or generates transformation steps and tooling commands. It can produce code (Python, Spark, or CLI), configuration for pipeline orchestration, and validation routines to ensure schema compatibility and data integrity. It also surfaces common errors and remediation steps so conversions run reliably in CI/CD and scheduled workflows.

When to use it

  • When you need to convert datasets between CSV, JSON, Parquet, Avro, ORC, or similar formats
  • During ETL development to standardize storage and query performance
  • When onboarding new data sources that use incompatible formats
  • To generate pipeline-ready code and configs for Airflow, Spark, or serverless jobs
  • When validating converted outputs against schema or format standards

Best practices

  • Prefer columnar formats (Parquet/ORC) for analytical workloads to improve performance and reduce cost
  • Capture and propagate schema and metadata to avoid silent data drift
  • Include validation steps (row counts, checksum, schema match) after conversion
  • Use streaming-aware converters for real-time ingestion to preserve latency and ordering
  • Add idempotency and retry logic in pipeline tasks to handle transient failures

Example use cases

  • Convert nightly CSV exports into partitioned Parquet for data warehouse ingestion
  • Generate Spark or pandas code to transform JSON logs into flattened Parquet with type coercion
  • Create Airflow task templates that run format conversions as part of ETL DAGs
  • Validate Avro files against a schema registry and emit alerts on schema mismatch
  • Automate conversion of legacy file dumps into modern columnar formats for analytics

FAQ

What formats does this skill support?

Common formats such as CSV, JSON, Parquet, Avro, ORC, and other delimited or binary formats; recommendations adapt to toolchain and use case.

Can it produce production-ready code?

Yes. It generates code snippets and configuration tailored to Spark, pandas, CLI tools, or orchestration platforms and includes validation steps.

How does it handle schema differences?

It detects schema mismatches, suggests field mappings and type coercions, and recommends validation and fallback strategies to preserve data quality.