Working with large datasets that exceed memory limits. Zarr-python enables chunked array storage with compression for efficient cloud-native scientific computing workflows.

This skill enables creating, editing, and analyzing Excel spreadsheets with formulas, formatting, and data analysis. It also recalculates formulas and verifies zero errors for publication-quality outputs.

Academic publishing requires strict formatting compliance. This skill provides ready-to-use LaTeX templates and detailed requirements for 50+ top journals, conferences, and grant agencies. Get perfectly formatted documents without manual template hunting.

Processing large tabular datasets that exceed RAM requires specialized tools. Vaex enables out-of-core DataFrame operations, lazy evaluation, and billion-row-per-second processing on datasets too big for memory. Perfect for astronomical data, financial time series, and large-scale scientific analysis.

Access comprehensive USPTO patent and trademark databases. Search by keywords, inventors, or assignees. Retrieve examination history, assignments, citations, and office actions for IP research and prior art searches.

Search protein databases, retrieve sequences, and map identifiers across biological databases. This skill provides direct access to UniProt, the comprehensive protein sequence and functional information resource.

Generate professional medical treatment plans in LaTeX format for any clinical specialty. Includes HIPAA compliance guidance, SMART goal frameworks, and validation tools for patient-centered care documentation.

High-dimensional data is difficult to visualize and analyze. UMAP reduces dimensions while preserving structure, enabling clear 2D/3D visualizations and better clustering results.

Stop wrestling with complex ML model setup. This skill gives you instant access to thousands of pre-trained transformer models for text, image, and audio tasks through simple commands.

Graph Neural Networks power modern applications like drug discovery, social network analysis, and recommendation systems. This skill provides ready-to-use templates and documentation for implementing GNNs with PyTorch Geometric.

Build and train graph neural networks for drug discovery, protein modeling, and molecular property prediction. This skill provides comprehensive documentation for the TorchDrug library including datasets, model architectures, and workflows.

Need exact mathematical results instead of numerical approximations. This skill provides comprehensive guidance for symbolic algebra, calculus, equation solving, matrix operations, and physics calculations using Python SymPy library.

Perform rigorous statistical analysis using OLS, GLM, ARIMA, and discrete choice models. Get publication-ready results with full diagnostics, coefficient tables, and residual analysis.

Researchers need to understand protein-protein interactions to study biological systems and disease mechanisms. This skill provides direct access to STRING's comprehensive database of 59M proteins and 20B+ interactions across 5000+ species.

Stop guessing which statistical test to use. Get guided test selection, assumption checking, and APA-formatted results for your research data. Perfect for students and researchers who need rigorous statistical analysis.

An AI skill for Claude, Codex, and Claude Code

SimPy enables you to model complex systems with processes, queues, and shared resources. Simulate manufacturing lines, service operations, network traffic, and logistics to optimize performance before implementation.

Machine learning models often act as black boxes. SHAP provides a unified framework to explain any model prediction by computing feature contributions using Shapley values from game theory. Use this skill to visualize feature importance, debug model behavior, and implement explainable AI.

Creating publication-quality statistical visualizations requires significant boilerplate code and design decisions. This skill provides optimized access to seaborn library functions for generating box plots, violin plots, heatmaps, and pair plots with attractive defaults and pandas integration.

Survival analysis handles data where events may not have occurred for all subjects. This skill provides Python tools for time-to-event modeling with censored data using Cox models, Random Survival Forests, SVMs, and specialized evaluation metrics like concordance index.