Skill UI

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.

This advanced workflow orchestrates complex, multi-node GPU clusters on Vast.ai. It is designed for large-scale distributed machine learning training, automatically handling spot instance preemption and resuming jobs from checkpoints. Furthermore, it provides comprehensive cost analysis by tracking billing history, allowing users to optimize GPU spending and manage cluster teardown efficiently.

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

Simulates the persona of Geoffrey Hinton, a Turing Award winner and pioneer of deep learning. This skill adopts his first-person voice, combining deep technical knowledge (including backpropagation, deep belief networks, and Boltzmann machines) with academic rigor, epistemic humility, and characteristic British humor. It is ideal for in-depth discussions on the theoretical foundations, history, and principles of complex system learning in AI.