Skill UI

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive collection of versioned public single-cell and spatial transcriptomics data. It enables efficient querying of population-scale cell metadata, gene expression slices, and embeddings without downloading entire datasets. Suitable for analyzing human, mouse, and other organism data across tissues and diseases. Integrates with Scanpy, AnnData, and TileDB-SOMA for large-scale analysis and machine learning workflows.

A Python library for molecular machine learning, offering diverse featurizers, MoleculeNet benchmarks, graph neural networks, and pretrained models for property and ADMET prediction.

This comprehensive geospatial science skill covers remote sensing, GIS, spatial analysis, and machine learning for Earth observation. It supports satellite imagery processing, vector and raster data operations, spatial statistics, and point cloud processing. Users can utilize cloud-native workflows and 8 programming languages with 500+ code examples. Ideal for terrain analysis, hydrological modeling, and marine spatial analysis tasks.

Hardware-agnostic quantum machine learning framework with automatic differentiation for training quantum circuits, building hybrid quantum-classical models, and deploying across IBM, Google, Rigetti, IonQ, and simulators.

PyTDC enables discovery of therapeutic machine learning tasks, access to approved datasets, task-aware data splits, evaluation metrics, benchmark groups, and molecular oracle workflows. Designed for biomedical research and drug discovery applications using Python.

Qiskit is a Python framework for building, simulating, and executing quantum circuits. It supports local simulation, noisy simulation, and IBM Quantum hardware execution. Users can utilize V2 primitives like Sampler and Estimator for variational algorithms and quantum machine learning tasks.

Comprehensive guidance for machine learning in Python using scikit-learn. Covers classification, regression, clustering, preprocessing, model evaluation, and pipelines for building production-ready ML workflows.

This skill covers using the SHAP library to explain and audit machine learning predictions. It guides users on selecting explainers, computing feature attributions, and validating results. It includes workflows for local and global visualizations, handling multi-output models, and ensuring model behavior descriptions are accurate without implying causality. Requires Python 3.12+ and specific SHAP versions.

Vaex is a high-performance Python library designed for processing and analyzing large tabular datasets that exceed available RAM. It supports out-of-core DataFrame operations, lazy evaluation, fast aggregations, and efficient visualization of big data. Ideal for working with massive CSV, HDF5, Arrow, or Parquet files, performing statistics on billions of rows, and building machine learning pipelines without memory constraints.

The AI Dev Jobs MCP provides AI agents with real-time access to a massive index of AI and Machine Learning job listings across hundreds of companies. Users can search by role, location, or company, retrieve full job details, analyze salary ranges, list hiring companies, and get aggregate market statistics. It is essential for advanced job searching, talent matching, and labor market analysis workflows.

A comprehensive checklist and script guide for validating all dependencies before deploying Apple Notes automation on a production macOS machine. It covers TCC permissions, iCloud sync health, application configuration, error handling robustess, and cross-OS compatibility checks, ensuring zero missed steps when automating notes management.

A quick reference guide for diagnosing and resolving common runtime and deployment errors encountered on the Fly.io platform. Covers issues such as health check failures, image build failures, machine startup issues, and connection refusals, providing specific diagnostic commands and configuration fixes.