Mle Workflow

A comprehensive framework for taking ML work from notebooks to production with explicit data contracts, reproducible training, and operational monitoring. Covers the full lifecycle: product framing, metric design, baseline models, feature engineering, evaluation gates, deployment, and rollback paths. The real value is in the MLE-to-SWE translation table that maps ranking systems, classifiers, and forecasting pipelines onto existing code review, testing, and deployment patterns instead of inventing parallel infrastructure. Best for teams hardening existing models or planning production ML features where you need reviewable artifacts and clear promotion criteria. Overkill for exploratory work, but that's the point.

skills.sh installs: 1.6K
GitHub stars: 229.1K
last updated: Jul 14, 2026

View on GitHub

Install

$ npx -y skills add affaan-m/everything-claude-code --skill mle-workflow --agent claude-code

Installs into .claude/skills of the current project.

Files

Machine Learning Engineering Workflow

Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

When to Activate

Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline
Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline
Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths
Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic
Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks

Scope Calibration

Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them.

SKILL.md on GitHub