ralph-wiggum

🥈Silver

Ralph Wiggum enables autonomous AI coding with spec-driven development. Operations teams benefit by automating code generation and maintenance. It connects to shell-based workflows and is supported by Claude agents.

17780Updated Today

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/fstandhartinger/ralph-wiggum.git

Works with:

Claude

Overview

About This Skill

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/fstandhartinger/ralph-wiggum

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

Act as a developer and write code for a [COMPANY] in the [INDUSTRY] industry. The code should be based on the following specifications: [SPECIFICATIONS]. Ensure the code is well-documented, follows best practices, and is ready for deployment.

Example Output

# Ralph Wiggum: Autonomous AI Coding

## Project: Customer Data Analyzer

### Specifications
- **Input**: CSV file containing customer data
- **Output**: Analyzed data with insights
- **Features**: Data cleaning, summary statistics, visualization

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

# Load the data
data = pd.read_csv('customer_data.csv')

# Data cleaning
data = data.dropna()

# Summary statistics
summary_stats = data.describe()

# Visualization
plt.hist(data['Age'], bins=10)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Age Distribution')
plt.show()

# Save the analyzed data
data.to_csv('analyzed_customer_data.csv', index=False)
```

### Documentation
- **Input File**: `customer_data.csv`
- **Output File**: `analyzed_customer_data.csv`
- **Dependencies**: pandas, matplotlib
- **Usage**: Run the script to analyze customer data and generate insights.