Which machine learning frameworks are supported?

The skill provides guidance and code generation compatible with major frameworks including PyTorch, TensorFlow, and Scikit-learn.

Does it provide production-ready code?

Yes, the skill is designed to generate production-ready code and configurations for common explainability tools and visualization libraries.

Can this tool help with AI regulatory compliance?

Yes, it provides the necessary patterns and visualization code to document and explain model decision-making, which is essential for meeting transparency standards.

How does this skill activate in Claude Code?

The skill activates automatically when you mention 'model explainability tool' or ask questions regarding machine learning model interpretation and patterns.

What is the Model Explainability Tool?

It is a specialized Claude Code skill designed to help developers implement and automate model interpretation techniques within their machine learning workflows.

Model Explainability Tool

Name: Model Explainability Tool
Author: jeremylongshore

byjeremylongshore

•

983

Data Science & ML

Streamlines machine learning model interpretation by providing automated guidance and code for advanced explainability techniques.

About

The Model Explainability Tool is a specialized Claude Code skill designed to demystify 'black box' machine learning models during the training process. It provides developers with automated assistance in implementing interpretability frameworks, generating production-grade visualization code, and ensuring model decisions are transparent and validated against industry standards. Whether you are debugging feature importance or meeting strict regulatory compliance for AI transparency, this skill integrates seamlessly into PyTorch, TensorFlow, and Scikit-learn workflows to foster trust and clarity in your AI outputs.

Key Features

Production-ready code generation for explainability visualizations
Automated implementation of model interpretability frameworks
Validation of model outputs against common transparency standards
Step-by-step guidance on XAI (Explainable AI) best practices
983 GitHub stars
Seamless integration with ML training pipelines and experiment tracking

Use Cases

Debugging complex machine learning models to identify and understand key feature influences
Communicating model decision-making logic to non-technical stakeholders and business users
Generating documentation and visualizations required for regulatory AI compliance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-explainability-tool

For use in Claude.ai and ChatGPT

Download Skill

GitHub