vibeproxy

🥈Silver

VibeProxy is a native macOS menu bar app that enables developers to use their Claude Code and ChatGPT subscriptions with AI coding tools without requiring API keys. It benefits developers and operations teams by simplifying access to AI coding assistants. The app integrates with macOS and connects to AI coding tools, enhancing productivity in software development workflows.

1,164760Updated 1mo ago

Intermediate30min to implementautomation

Saves ~45 min per use

Quick InstallView Source

git clone https://github.com/automazeio/vibeproxy.git

Works with:

Claude

Overview

About This Skill

https://github.com/automazeio/vibeproxy

Use Cases

Authenticate and manage multiple AI service accounts from a single menu bar application.

Quickly switch between different AI models like Claude Code and Gemini for diverse coding tasks.

Utilize powerful AI coding tools like Factory Droids without needing to manage separate API keys.

Automatically route requests through Vercel AI Gateway for enhanced security and reliability.

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/automazeio/vibeproxy

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

Use VibeProxy to automate your coding tasks. For [COMPANY] in the [INDUSTRY] sector, input the following [DATA] to streamline your workflow: 'Generate a Python script for data analysis based on the provided dataset.'

Example Output

### Generated Python Script

```python
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(file_path):
    # Load the dataset
    data = pd.read_csv(file_path)
    # Display basic statistics
    print(data.describe())
    # Plotting
    plt.figure(figsize=(10, 6))
    plt.hist(data['column_name'], bins=30)
    plt.title('Data Distribution')
    plt.xlabel('Values')
    plt.ylabel('Frequency')
    plt.show()

# Call the function with your dataset
analyze_data('path/to/your/data.csv')
```

This script loads a CSV file, displays basic statistics, and plots a histogram of a specified column.