Agent skill · software engineering · affaan-m
mle-workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
Why this skill is useful
Adds detailed workflows for production ML systems, including monitoring, rollback, and evaluation scripts that extend the AI's capabilities.
What it needs
Requires docker, python, pytorch installed locally. About 11k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
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. Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback. Do not add heavyweight MLOps machinery when a data contract, baseline, eval script, and rollback note would make the change reviewable. Do make assumptions explicit when the project lacks labels, delayed outcomes, slice definitions, production traffic, or monitoring ownership. Treat examples as interchangeable scaffolds. Replace metrics, serving mode, data stores, and rollout mechanics with the project-native equivalents. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/mle-workflow--c3978e