Can it handle large datasets?

It is designed to handle datasets up to 100MB and includes built-in support for CSV, Parquet, and JSON formats commonly used in data projects.

Does it automatically deploy models to production?

For safety, the skill prepares models for deployment and generates the necessary serialization files, but requires human approval before performing final deployment actions.

What machine learning libraries are supported?

While optimized for core libraries like Scikit-Learn, Pandas, and NumPy, it can assist with any Python-based ML framework including TensorFlow and PyTorch.

Does this skill support Jupyter Notebooks?

Yes, the skill is specifically configured to read and edit .ipynb files and can manage cell-based workflows common in data science.

Machine Learning Model Developer

Name: Machine Learning Model Developer
Author: nbossn

bynbossn

0•

数据科学与机器学习

Automates the end-to-end machine learning lifecycle from data preprocessing and feature engineering to model training and deployment.

The agent-data-ml-model skill transforms Claude into a specialized ML engineer capable of handling complex data science workflows within your local environment. It provides domain-specific guidance for exploratory data analysis, pipeline construction using standard libraries like Scikit-Learn, and the development of sophisticated neural networks or classification models. By automating repetitive tasks like feature scaling, cross-validation, and model serialization while maintaining rigorous evaluation standards, it enables developers to build robust, production-ready models with optimized performance and clearly documented limitations.

主要功能

01Full compatibility with Jupyter Notebooks (.ipynb) and standard Python ML libraries

02End-to-end ML pipeline automation including preprocessing and feature scaling

030 GitHub stars

04Automated model serialization and deployment preparation workflows

05Sophisticated model selection, hyperparameter tuning, and ensemble methods

06Comprehensive evaluation metrics featuring ROC/AUC curves and confusion matrices

使用场景

01Automating exploratory data analysis and feature engineering on raw datasets

02Building classification models for business logic such as customer churn prediction

03Developing and training neural networks for specialized image or text tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agent-data-ml-model

For use in Claude.ai and ChatGPT

主要功能

01Full compatibility with Jupyter Notebooks (.ipynb) and standard Python ML libraries

02End-to-end ML pipeline automation including preprocessing and feature scaling

030 GitHub stars

04Automated model serialization and deployment preparation workflows

05Sophisticated model selection, hyperparameter tuning, and ensemble methods

06Comprehensive evaluation metrics featuring ROC/AUC curves and confusion matrices

使用场景

01Automating exploratory data analysis and feature engineering on raw datasets

02Building classification models for business logic such as customer churn prediction

03Developing and training neural networks for specialized image or text tasks