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data-wizard

Automate ETL and data transformation for CSV, JSON, and database sources using Python and pandas.

12|4|Updated Jan 22, 2026
Search Tags:#python#pandas#transformation#etl#data-quality#data-processing
One-click install
npx skills add https://github.com/TurnaboutHero/oh-my-antigravity --skill data-wizard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-wizard
Source: https://github.com/TurnaboutHero/oh-my-antigravity/tree/main/skills/data-wizard
Command: npx skills add https://github.com/TurnaboutHero/oh-my-antigravity --skill data-wizard

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates data processing and transformation, turning raw data into clean, usable datasets ready for analysis and visualization.

Core Features & Use Cases

  • ETL pipelines: Extract, transform, and load data from CSV, JSON, and databases with validation and quality checks.
  • Data Transformation & Quality: Clean, deduplicate, fill missing values, and engineer features like age_group for analytics-ready data.
  • Use Case: Imagine ingesting customer data from multiple sources and producing a single, clean dataset with consistent types and a derived age_group for segmentation.

Quick Start

Process raw_sales.csv into cleaned_data.parquet by running the ETL pipeline: deduplicate, fill missing ages, convert dates, and create an age_group feature.

Frequently Asked Questions about data-wizard

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

FAQPage Schema
How do I automate ETL pipelines to clean and prepare data for analytics?▼

ETL pipelines extract, transform, and load data from CSV, JSON, and databases while applying validation and quality checks. This Skill automates the full workflow—deduplication, missing-value handling, type conversion, and feature engineering—to deliver analytics-ready datasets without manual processing.

Can I use pandas to transform raw data from multiple sources into a single clean dataset?▼

Yes. This Skill uses pandas to ingest data from multiple sources, standardize types, deduplicate records, and engineer derived features like age_group. The result is a unified, consistent dataset ready for reporting and dashboards.

What's the best way to handle missing values and data quality issues in batch data processing?▼

Data transformation includes filling missing values, deduplicating records, and converting incompatible types during ETL. This Skill applies these quality checks automatically across batch and streaming analytics tasks to ensure reliable downstream analysis.

How do I convert raw CSV and JSON files into clean parquet for faster analysis?▼

Extract source files in CSV or JSON format, apply transformation logic to clean and deduplicate, engineer features, and load the result as parquet. This Skill orchestrates the full pipeline, reducing manual data prep time.

Does this work for both batch and streaming data sources?▼

Yes. This Skill supports batch and streaming analytics tasks across CSV, JSON, and database sources, enabling consistent data preparation workflows regardless of source type or ingestion pattern.

What Python libraries and prerequisites do I need for data transformation workflows?▼

Python and pandas are required. The Skill handles extract, transform, load, data cleaning, type conversion, and feature engineering as demonstrated in code samples, with no additional external dependencies.

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