ml-ralph

🥈Silver

ML-Ralph is an autonomous ML agent that automates experiment workflows for machine learning engineers. It uses Claude or Codex to run experiments through a cognitive loop: orient, research, hypothesize, execute, analyze, validate, and decide. ML-Ralph connects to Python workflows and integrates with tools like Weights & Biases for tracking experiments.

1710Updated 1w ago

Intermediate30min to implementautomation

Saves ~240 min per use

Quick InstallView Source

git clone https://github.com/pentoai/ml-ralph.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/pentoai/ml-ralph

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

I need ML-Ralph to help me with a machine learning experiment for [COMPANY] in the [INDUSTRY] sector. The goal is to [SPECIFIC GOAL, e.g., improve customer churn prediction]. I have [DATA] available and want to use [SPECIFIC TOOLS, e.g., TensorFlow, PyTorch] for this experiment. Can you guide me through the process?

Example Output

# ML-Ralph Experiment Workflow for Customer Churn Prediction

## Orient
- **Objective**: Improve customer churn prediction for TechSolutions Inc. in the SaaS industry.
- **Data**: 5 years of customer interaction data, including usage patterns, support tickets, and billing history.
- **Tools**: TensorFlow, Scikit-learn, Weights & Biases.

## Research
- **Key Findings**: Customer churn is highly correlated with support ticket resolution time and feature usage frequency.
- **Relevant Papers**: "Predicting Customer Churn with Deep Learning" (2022), "Feature Importance in Churn Models" (2021).

## Hypothesis
- **Hypothesis**: A model combining LSTM for temporal patterns and XGBoost for feature importance will outperform traditional logistic regression.

## Execute
- **Data Preprocessing**: Normalized numerical features, encoded categorical variables, and handled missing values.
- **Model Training**: Trained LSTM and XGBoost models separately and then combined their predictions.

## Analyze
- **Performance Metrics**: Combined model achieved an AUC-ROC of 0.89, compared to 0.82 for logistic regression.
- **Feature Importance**: Support ticket resolution time and feature usage frequency were the top predictors.

## Validate
- **Cross-Validation**: Model performance was consistent across different validation sets.
- **Business Impact**: Potential to reduce churn by 15% if implemented.

## Decide
- **Recommendation**: Deploy the combined LSTM-XGBoost model for real-time churn prediction.
- **Next Steps**: A/B test the model's predictions against current churn rates.