Guide · Agent skill
How to use the Machine Learning Engineer agent skill (ml-engineer)
Updated · written with AI from the project's own documentation and checked against our catalogue data
The ml-engineer skill is a set of written guidance that tells your AI agent to answer machine learning engineering questions the way an experienced practitioner would, with a focus on PyTorch, scikit-learn, model evaluation and MLOps. It is aimed at data scientists, ML engineers and students who want checklist-style reviews of training and evaluation work. By the end of this guide you will know how to install the skill, how to ask for it, and what to expect from it.
Machine Learning EngineerRightNow-AI/openfang · Apache-2.0 Download the skill Quick start
- 1Download the ZIP. Download the ml-engineer ZIP from this page.
- 2Unzip the skill. Unzip the file so that SKILL.md sits inside a skill folder.
- 3Choose a location. Place the folder in ~/.claude/skills/ for personal use, or in .claude/skills/ inside a project for project-only use.
- 4Or upload in the Claude apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the skill.
- 5Ask a plain question. Describe your ML task, data, metric and what you have tried, in ordinary language.
- 6Review the answer. Check the advice against official library documentation, especially API details and versions.
What the ml-engineer skill makes the agent do
When the skill is active, the agent follows a consistent approach to ML work. It starts with a simple baseline before suggesting complex architectures, chooses evaluation metrics that match the business goal rather than accuracy alone, guards against train/test leakage, and recommends versioning datasets, code, hyperparameters and models. It also covers monitoring models in production for drift and degradation, and keeping a rollback plan.
- Concrete guidance on PyTorch training loops, including the optimizer.zero_grad(), loss.backward(), optimizer.step() pattern and DataLoader settings such as num_workers and pin_memory
- scikit-learn Pipeline and ColumnTransformer patterns that fit preprocessing on training data only
- Hyperparameter tuning with GridSearchCV, RandomizedSearchCV or Optuna
- Evaluation with classification_report, roc_auc_score and confusion_matrix
- Experiment tracking with MLflow or Weights and Biases, and promoting models through MLflow Model Registry stages
Install the ml-engineer skill
The skill is a single file, SKILL.md. Download the ZIP from this page and unzip it. You can install it for yourself across all projects, or for one project only.
For a personal install, unzip it into ~/.claude/skills/. For a single project, unzip it into the project's .claude/skills/ folder. If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.
- Personal: place the skill folder inside ~/.claude/skills/
- Project only: place the skill folder inside .claude/skills/ in your project
Trigger the skill with natural requests
You do not need a special command. Ask about ML engineering in plain language and describe your situation. The skill is written around questions about training, evaluation, experiment tracking, deployment and monitoring, so requests that touch those topics are the ones it is built for.
Give the agent your context: the task, the data type, the metric your business cares about and what you have already tried. The more specific the request, the more specific the review.
- "Review my training loop for this PyTorch classifier and tell me what is missing."
- "My fraud model has 99% accuracy. Which metrics should I check instead?"
- "Set up an sklearn pipeline that scales and one-hot encodes my columns without leaking test data."
- "Plan experiment tracking and a model registry with a rollback path for this project."
Examples of what to expect
Suppose you ask whether a churn model is good enough. A useful answer under this skill would ask what the business cost of a missed churner is, look at precision and recall per class rather than accuracy, check the confusion matrix, and confirm the test set was never used during tuning. It would also suggest comparing against a simple logistic regression baseline first.
For a PyTorch project, expect advice on how the DataLoader and training loop are structured, and reminders to log hyperparameters and seeds with each run so results can be reproduced.
Safety notes: scripts and network access
The skill is instructional text only. According to its listing, it runs no scripts, uses no network access, and lists no requirements. It does not add tools and does not run anything by itself. The agent's guidance comes from the text in SKILL.md.
The skill does not check library versions or documentation. Treat its code patterns as starting points and confirm current API details in the official docs of PyTorch, scikit-learn or MLflow before relying on them in production.
Tips for getting the most out of it
Use the skill for review and checklists: ask it to audit a workflow you have already written. Keep your own data and environment facts in the request, because the skill does not inspect your files or check your setup.
- Ask for the baseline first, then the complex model
- State the business goal so metric choice is grounded in it
- Ask the agent to confirm library API details if you need exact syntax
- Keep the previous model version available so you can roll back
What people use it for
- Auditing a PyTorch training loop for missing zero_grad, backward or optimizer steps
- Choosing metrics such as precision, recall, F1 or AUC-ROC for an imbalanced dataset
- Structuring a scikit-learn Pipeline so preprocessing is fit on training data only
- Planning experiment tracking and model registry stages in MLflow
- Drafting a rollback and drift-monitoring plan for a production model
Frequently asked questions
Is the ml-engineer skill free?
The skill is published under the Apache-2.0 licence, as listed on this page. The source does not mention pricing, so check your own Claude plan for any costs related to using skills.
What do I need to install the ml-engineer skill?
The listing states no requirements. You need a place to put the skill folder, either ~/.claude/skills/ or a project's .claude/skills/, or access to upload it under Settings → Capabilities → Skills in the Claude apps.
Does the ml-engineer skill run code or access the internet?
No. According to the listing it runs no scripts and uses no network access. It is a single instructional file, SKILL.md, and does not add tools.
Will the skill write runnable PyTorch or scikit-learn code for me?
It gives principles and short patterns rather than full runnable examples. It is better for reviewing a workflow than for producing a complete project from scratch.
Does the skill check current library versions or API docs?
No. The skill does not check versions or documentation, so the agent may still need to confirm details. Verify exact syntax in the official PyTorch, scikit-learn or MLflow docs.
What is a good alternative if I need step-by-step ML tutorials?
This skill is aimed at checklist-driven review. If you need a beginner walkthrough with complete runnable code, look for a course or tutorial that provides full examples, and use the skill alongside it for review.
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Our verdict
Use the ml-engineer skill if you are a data scientist, ML engineer or student who wants a consistent, checklist-style review of a training, evaluation or tracking workflow. Pick something else if you need verified, up-to-date library APIs for production decisions, or if you want a beginner tutorial with full runnable code.
Download the skill