antigravity-cooking

🥈Silver

A curated collection of powerful automation scripts, skills, and workflows designed to work with the Antigravity AI agent

600Updated 1mo ago

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/iamneongo/antigravity-cooking.git

Works with:

Claude

Overview

About This Skill

A curated collection of powerful automation scripts, skills, and workflows designed to work with the Antigravity AI agent

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/iamneongo/antigravity-cooking

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Generate an automation script for [COMPANY] in the [INDUSTRY] sector to [SPECIFIC TASK]. The script should integrate with [SPECIFIC TOOLS/PLATFORMS] and use [DATA] to streamline the process. Ensure the script is efficient, scalable, and secure.

Example Output

# Automation Script for Data Processing

## Overview
This script automates the data processing workflow for [COMPANY], a leading firm in the [INDUSTRY] sector. It integrates with [SPECIFIC TOOLS/PLATFORMS] to streamline data extraction, transformation, and loading (ETL) processes.

## Key Features
- **Data Extraction**: Pulls data from [DATA SOURCES] every [TIME INTERVAL].
- **Data Transformation**: Cleans and formats data according to [SPECIFIC STANDARDS].
- **Data Loading**: Loads processed data into [TARGET DATABASE] for further analysis.
- **Error Handling**: Includes robust error handling to ensure data integrity.

## Implementation
```python
# Example Python script for data processing
import pandas as pd

# Data extraction
data = pd.read_csv('[DATA SOURCE]')

# Data transformation
data_cleaned = data.dropna()
data_cleaned['column'] = data_cleaned['column'].apply(lambda x: x.upper())

# Data loading
data_cleaned.to_sql('target_table', con=engine, if_exists='replace')
```