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data-transform

Convert structured data between CSV and JSON file formats.

Updated Apr 17, 2026
Search Tags:#data#json#etl#csv#convert#formats
One-click install
npx skills add https://github.com/xiaoli123/agentscope-core --skill data-transform-xiaoli123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-transform
Source: https://github.com/xiaoli123/agentscope-core/tree/main/agentscope-core/src/test/resources/e2e-skills/data-transform
Command: npx skills add https://github.com/xiaoli123/agentscope-core --skill data-transform-xiaoli123

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Converting and reformatting structured data between file formats (CSV, JSON, XML, YAML) to save time and reduce manual errors in data pipelines.

Core Features & Use Cases

  • CSV to JSON: convert CSV files into JSON arrays for downstream processing.
  • JSON to CSV: flatten a JSON array into a CSV for tabular analysis.
  • Data reformatting: quickly switch between common formats during data munging and ETL tasks.

Quick Start

Use the provided scripts to convert CSV to JSON or JSON to CSV by supplying input and output file paths.

Frequently Asked Questions about data-transform

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert CSV to JSON for downstream data processing?▼

To convert CSV to JSON, you use Python scripts that read UTF-8 encoded CSV files and write the structured data into a JSON array format. This simplifies data interoperability for downstream processing and ETL tasks by flattening tabular rows into key-value pairs.

What is the best way to flatten a JSON array into CSV for tabular analysis?▼

Flattening a JSON array into CSV involves reading structured JSON data and extracting nested values into tabular rows. This approach is best for small-to-medium ETL tasks, allowing quick reformatting during data munging to enable straightforward spreadsheet analysis.

Can I use Python scripts to automate data migration between file formats?▼

Yes, you can use Python scripts to automate data migration by supplying input and output file paths to convert structured data between formats. This method quickly switches data between CSV and JSON during development and testing to reduce manual errors.

Does converting data formats with Python scripts support UTF-8 encoded CSV and JSON files?▼

Yes, converting data formats with Python scripts supports UTF-8 encoded CSV and JSON files. The scripts reliably read UTF-8 CSV or JSON inputs and write corresponding outputs, ensuring data interoperability without character encoding issues during ETL tasks.

What are the limitations of using Python scripts for quick data reformatting during ETL?▼

The limitations of using Python scripts for quick data reformatting include suitability primarily for small-to-medium ETL tasks rather than large-scale data pipelines. Additionally, it currently relies on reading structured UTF-8 CSV and JSON formats.

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