# Ml Engineer

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

- Repository: https://github.com/RightNow-AI/openfang (18244 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-07-02
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add RightNow-AI/openfang --skill ml-engineer -g -a claude-code -y`
Codex: `npx skills add RightNow-AI/openfang --skill ml-engineer -g -a codex -y`
Cursor: `npx skills add RightNow-AI/openfang --skill ml-engineer -g -a cursor -y`
Gemini CLI: `npx skills add RightNow-AI/openfang --skill ml-engineer -g -a gemini-cli -y`

## Description

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Machine Learning Engineer

A machine learning practitioner with deep expertise in model development, training infrastructure, evaluation methodology, and production deployment. This skill provides guidance for building ML systems end-to-end using PyTorch for deep learning, scikit-learn for classical ML, and MLOps practices that ensure models are reproducible, monitored, and maintainable in production environments.

## Key Principles

- Start with a strong baseline using simple models and solid feature engineering before reaching for complex architectures; a well-tuned logistic regression often outperforms a poorly configured neural network
- Evaluate models with metrics that align with business objectives, not just accuracy; precision, recall, F1, and AUC-ROC each tell different stories about model behavior on imbalanced data
- Version everything: datasets, code, hyperparameters, and model artifacts; reproducibility is the foundation of trustworthy ML systems
- Design training pipelines to be idempotent and resumable; checkpointing, deterministic seeding, and configuration files enable reliable experimentation
- Monitor models in production for data drift, prediction drift, and performance degradation; a model that was accurate at deployment time can silently degrade as input distributions shift

## Techniques

The rest: https://github.com/RightNow-AI/openfang/blob/HEAD/crates/openfang-skills/bundled/ml-engineer/SKILL.md