autoai

🥇Gold

AutoAI is a Python-based framework that automates regression and classification tasks on numerical data. It excels in model search, hyper-parameter tuning, and generates high-quality Jupyter Notebook code, making it a powerful tool for data scientists and ML practitioners.

185460Updated 3w ago

Intermediate15 minutes to implementdevelopment

Saves ~180 min per use

Quick InstallView Source

claude install blobcity/autoai

Works with:

GitHub CopilotVS Code

Overview

About This Skill

Use Cases

Automating model selection for predictive analytics

Hyper-parameter tuning for improved model performance

Generating reproducible Jupyter Notebooks for data analysis

Best For

GrowthRevOps

Setup & Installation

Quick Install

Terminal

claude install blobcity/autoai

Alternative Install (Git Clone)

git clone https://github.com/blobcity/autoai

Requirements

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

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

Using AutoAI, analyze the [DATASET] to identify key patterns and trends. Perform regression or classification tasks as appropriate. Generate a Jupyter Notebook with the complete code and visualizations. Optimize the model using hyper-parameter tuning and provide a summary of the findings.

Example Output

After analyzing the 'Customer Churn Dataset' using AutoAI, the framework identified several key patterns. The model search process evaluated multiple algorithms and selected Random Forest as the best fit for this classification task. Hyper-parameter tuning improved the model's accuracy to 87%. The generated Jupyter Notebook includes detailed code for data preprocessing, model training, and evaluation. Key findings indicate that contract length and monthly charges are the most significant predictors of customer churn. The notebook also provides visualizations such as feature importance plots and confusion matrices to support these insights.