antigravity-workflows

🥇Gold

@harikrishna8121999

Antigravity Workflows offers community-driven automation and reusable prompts for AI coding assistants, enhancing productivity for developers. Similar to Claude Skills, it streamlines coding tasks and integrates seamlessly with popular tools.

2010Updated 1w ago

Intermediate15 minutes to implementdevelopment

Saves ~30 min per use

Quick InstallView Source

claude install harikrishna8121999/antigravity-workflows

Works with:

VS CodeGitHub Copilot

Overview

About This Skill

Use Cases

Automate repetitive coding tasks

Create reusable prompts for AI coding assistants

Integrate workflows within VS Code

Enhance developer productivity with community-driven tools

Best For

GrowthDeveloper

Setup & Installation

Quick Install

Terminal

claude install harikrishna8121999/antigravity-workflows

Alternative Install (Git Clone)

git clone https://github.com/harikrishna8121999/antigravity-workflows

Requirements

Claude Code or compatible AI agent
Works with: VS Code, GitHub Copilot

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 a Python script to [TASK] using [LIBRARY]. The script should include error handling and logging. Use the following code structure: [STRUCTURE]. Make sure it follows PEP 8 guidelines and is well-commented.

Example Output

# Python Script for Data Analysis
# Task: Analyze sales data and generate a summary report
# Library: pandas, matplotlib

import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
try:
    df = pd.read_csv('sales_data.csv')
except FileNotFoundError:
    print("Error: File not found.")
    exit()

# Data Cleaning
# Remove rows with missing values
df.dropna(inplace=True)

# Data Analysis
# Group by product category and calculate total sales
sales_summary = df.groupby('ProductCategory')['Sales'].sum()

# Generate a bar plot
plt.figure(figsize=(10, 6))
sales_summary.plot(kind='bar', color='skyblue')
plt.title('Total Sales by Product Category')
plt.xlabel('Product Category')
plt.ylabel('Total Sales')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('sales_summary.png')

# Save the summary to a CSV file
sales_summary.to_csv('sales_summary.csv')

print("Data analysis completed successfully.")