ml-reviewer

What problem does it solve?

This Skill reviews machine learning and deep learning codebases for model construction, training loops, data pipelines, GPU utilization, and MLOps practices to prevent common pitfalls and inefficiencies.

Core Features & Use Cases

• Framework-aware review: checks PyTorch/TensorFlow/Keras patterns and common training pitfalls.
• Data pipeline checks: validates data loading, preprocessing, and augmentation sanity.
• Experiment tracking: ensures reproducible experiments and proper logging.
• GPU optimization: flags non-optimal device usage and memory patterns.
• Use Case: Improve a training script by catching missing gradient clipping and improper eval mode.

Quick Start

Run the ml-reviewer on a Python project with frameworks declared in requirements.txt or pyproject.toml to get a targeted ML code quality report.