ML Model Debugger

4.6

3800 Downloads

280 Stars

Quickly diagnose machine learning model issues with detailed debugging info and fix suggestions

Machine Learning Debugging Model Optimization TensorFlow PyTorch

Overview

ML Model Debugger is a debugging tool designed specifically for machine learning engineers, helping you quickly locate and resolve various issues during model training and inference.

Core Features

Training Diagnostics: Automatically detect gradient vanishing/explosion, overfitting, and other issues
Performance Analysis: Identify model inference performance bottlenecks
Data Validation: Check data quality and distribution
Visualization: Provide rich visualization tools
Framework Support: Support mainstream frameworks like TensorFlow, PyTorch, JAX

Use Cases

Model training not converging
Inference speed too slow
Abnormal model performance
Data preprocessing issues

Advanced Features

Automatic Fix Suggestions

Generate fix code suggestions based on detected issues.

Real-time Monitoring

Monitor key metrics in real-time during training.

Installation

claude-code skill install ml-model-debugger

Examples

Diagnose Training Issues

Analyze issues during model training

@debug-ml check-training model.py

Performance Analysis

Analyze model inference performance

@debug-ml profile inference.py

Skill.md

这是原始的 Skill 定义文档，包含了 Skill 的完整技术规格和配置信息。

Front Matter

---
title: "ML Model Debugger"
description: "Quickly diagnose machine learning model issues with detailed debugging info and fix suggestions"
date: 2026-01-14
author: "AI Tools"
version: "1.5.2"
license: "Apache 2.0"
rating: 4.6
downloads: 3800
stars: 280
repository: "https://github.com/example/ml-model-debugger"
category: "Machine Learning"
tags:
  - "Machine Learning"
  - "Debugging"
  - "Model Optimization"
  - "TensorFlow"
  - "PyTorch"
topics:
  - "AI Engineer"
  - "Data Scientist"
installation: "claude-code skill install ml-model-debugger"
examples:
  - title: "Diagnose Training Issues"
    description: "Analyze issues during model training"
    code: "@debug-ml check-training model.py"
  - title: "Performance Analysis"
    description: "Analyze model inference performance"
    code: "@debug-ml profile inference.py"
---

Markdown Content


## Overview

ML Model Debugger is a debugging tool designed specifically for machine learning engineers, helping you quickly locate and resolve various issues during model training and inference.

### Core Features

- **Training Diagnostics**: Automatically detect gradient vanishing/explosion, overfitting, and other issues
- **Performance Analysis**: Identify model inference performance bottlenecks
- **Data Validation**: Check data quality and distribution
- **Visualization**: Provide rich visualization tools
- **Framework Support**: Support mainstream frameworks like TensorFlow, PyTorch, JAX

## Use Cases

- Model training not converging
- Inference speed too slow
- Abnormal model performance
- Data preprocessing issues

## Advanced Features

### Automatic Fix Suggestions

Generate fix code suggestions based on detected issues.

### Real-time Monitoring

Monitor key metrics in real-time during training.

Information

Author: AI Tools
Version: 1.5.2
License: Apache 2.0
Updated: 2026-01-14
Category: Machine Learning