Modal is a serverless platform for running Python code in the cloud. It provides instant access to GPUs, automatic scaling, and pay-per-use billing. Deploy ML models, run batch processing jobs, and serve APIs without managing infrastructure.

Drug discovery requires filtering large compound libraries for drug-like properties. This skill applies established medicinal chemistry rules including Rule of Five, Veber, and PAINS filters to prioritize compounds and identify structural alerts efficiently.

Matplotlib provides complete control over every visual element for creating publication-quality figures. Master both pyplot and object-oriented interfaces to build any chart type from simple line plots to complex multi-panel scientific visualizations.

Scientists and engineers often struggle with MATLAB syntax and best practices. This skill provides comprehensive reference documentation for matrix operations, visualization, and numerical analysis.

Deploy production-ready bioinformatics workflows without managing infrastructure. Create serverless pipelines using Python decorators with automatic containerization, GPU support, and integrated cloud storage.

Biological research generates complex datasets that are difficult to track, query, and reproduce. LaminDB provides a unified framework for managing biological data with automatic lineage tracking, ontology-based annotations, and seamless integration with workflow managers.

This skill provides access to the Human Metabolome Database containing over 220,000 metabolite entries. Researchers can search by chemical name, structure, or ID to retrieve properties, spectra, and biomarker data for metabolomics studies.

Histolab automates tissue detection and tile extraction from gigapixel whole slide images. It processes WSI files to extract informative tiles for deep learning pipelines and medical research.

Access comprehensive genome-wide association study data from the NHGRI-EBI GWAS Catalog. Search variants by rs ID, trait, or gene to retrieve p-values, effect sizes, and summary statistics for genetic research.

Process and analyze genomic interval data for bioinformatics research. Gtars provides high-performance tools for overlap detection, coverage analysis, and ML tokenization of genomic sequences.

gget provides unified access to 20+ genomic databases through a simple CLI or Python interface. Query gene information, retrieve sequences, perform BLAST searches, and run enrichment analysis without managing multiple API connections.

Scientific computing tasks need hardware information to select optimal computational strategies. This skill automatically detects CPU cores, GPU availability, memory, and disk space, then generates recommendations for parallel processing and GPU acceleration.

GeoPandas extends pandas for geospatial operations. It enables reading shapefiles, GeoJSON, and PostGIS data. Perform spatial joins, buffer analysis, coordinate transformations, and create choropleth maps with matplotlib integration.

Flow cytometry data requires specialized file handling. FlowIO parses FCS files, extracts event data as NumPy arrays, and enables conversion to CSV formats for downstream analysis pipelines.

Access comprehensive FDA regulatory data including drug adverse events, medical device recalls, and safety information through the official openFDA API. Build pharmacovigilance and regulatory research workflows with Python.

Scientific data files come in hundreds of formats. This skill automatically detects file type, extracts metadata, assesses data quality, and generates comprehensive markdown reports with format-specific analysis recommendations.

DrugBank is a comprehensive bioinformatics database containing detailed drug information. This skill enables programmatic access to drug properties, interactions, targets, pathways, and chemical structures for pharmacology research and drug discovery.

Process and visualize next-generation sequencing data. Convert BAM to bigWig, generate quality control plots, and create publication-ready heatmaps for ChIP-seq, RNA-seq, and ATAC-seq experiments.

Predict molecular properties like solubility, toxicity, and binding affinity using DeepChem. Train graph neural networks or use pretrained models like ChemBERTa for drug discovery and materials science applications.

Access global statistical data from Data Commons including demographics, economics, health, and environmental indicators. Query population figures, GDP, unemployment rates, and geographic relationships using Python client methods.