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ML Engineering Workflow

Guides an agent to build, review, and harden production ML systems with data contracts, evals, and rollback plans.

Source Download skill
Repository
affaan-m/ECC
Stars
295,127
Licence
MIT
Files
2 files

What it does

This skill gives the agent a structured playbook for taking machine learning work from a one-off notebook to a production system. When active, the agent checks data contracts, reproducible training, evaluation gates, deployment artifacts, monitoring, and rollback paths, and it points to related skills for testing, deployment, and security. It is meant to be scaled to the project rather than forcing heavy MLOps tooling everywhere.

Great for

  • Engineers converting notebook models into reusable training, evaluation, or inference pipelines
  • Teams reviewing ML changes for leakage, drift, reproducibility, or promotion risks
  • Developers planning canary rollouts, shadow traffic, or post-deploy monitoring for a model

Skip it if

  • Simple one-off data exploration where production rigor is unnecessary, since the skill itself warns against adding heavy machinery
  • Anyone expecting it to train models or run experiments by itself; it is a guidance workflow, not an ML library
  • Non-technical users without an existing ML codebase, as most guidance assumes project infrastructure already exists

Try asking

  • “Review this training pipeline for label leakage and reproducibility problems before we promote the model.”
  • “Turn this Jupyter notebook into a reusable training and batch inference pipeline with a data contract.”
  • “Plan a canary rollout and rollback path for our new ranking model.”
  • “Help me define promotion criteria and offline evals for this classifier.”
Step-by-step guide: how to use ML Engineering WorkflowInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 2 original files, the MIT licence and an attribution note.

    mle-workflow.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip mle-workflow.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip mle-workflow.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent your project's real data sources, metrics, and serving mode so it replaces the generic examples with your stack.
  • Ask it to state assumptions explicitly when labels, delayed outcomes, or monitoring owners are missing.
  • Review any scripts or commands the agent proposes with Bash before running them against production data or credentials.

What's inside

  • agents/openai.yaml
  • SKILL.md

Redistributed unmodified from affaan-m/ECC under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.