Process proteomics and metabolomics data with comprehensive mass spectrometry tools. This skill provides access to OpenMS algorithms for file format handling, spectral processing, feature detection, and peptide identification workflows.

Multi-objective optimization involves finding trade-off solutions when multiple conflicting objectives must be satisfied. This skill provides comprehensive access to pymoo algorithms including NSGA-II, NSGA-III, and MOEA/D for finding Pareto-optimal solutions and visualizing trade-off fronts.

Build, fit, and validate Bayesian models using PyMC. Create hierarchical models, run MCMC sampling, and compare models with LOO and WAIC metrics.

Healthcare researchers and data scientists struggle with complex clinical data formats and coding systems. PyHealth provides a unified toolkit for loading medical datasets, processing EHR data, training predictive models, and working with standard medical codes.

Process DICOM medical images including CT, MRI, X-ray, and ultrasound. Read, write, anonymize, convert, and extract metadata from healthcare imaging files.

PyDESeq2 enables differential gene expression analysis from bulk RNA-seq count data. Perform statistical testing, multiple comparison correction, and generate publication-ready volcano and MA plots for your genomics research.

Training RL agents requires high-performance parallel environments and efficient algorithms. PufferLib provides optimized PPO+LSTM training with 2-10x speedups through vectorization, shared memory buffers, and multi-agent support.

Process large datasets in memory with Polars, the high-performance DataFrame library. Features lazy evaluation, parallel execution, and Apache Arrow backend for 10x faster operations than pandas.

Data visualization often requires complex code and lacks interactivity for exploration. This skill provides comprehensive guidance for creating interactive, publication-quality charts with Plotly, featuring hover tooltips, zoom, pan, and 40+ chart types for dashboards and data analysis.

Find therapeutic targets and drug candidates using Open Targets Platform data. Access genetic evidence, tractability assessments, safety data, and known drugs for target-disease associations.

This skill provides Python API access to OMERO, a scientific image management platform for microscopy and high-content screening. Connect to OMERO servers, retrieve images, analyze pixel data, and manage annotations programmatically.

This skill provides comprehensive analysis of Neuropixels high-density neural recordings. It handles the complete workflow from raw data loading to publication-ready curated units using SpikeInterface and Kilosort4 algorithms.

Working with graph and network data requires specialized tools for creating structures, computing algorithms, and generating visualizations. This skill provides comprehensive access to NetworkX capabilities for social network analysis, biological pathway mapping, transportation optimization, and relationship modeling.

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.