Therapeutic machine learning requires reliable datasets, meaningful splits, and consistent evaluation. This skill guides PyTDC workflows from dataset selection through molecular optimization.

Conflicting objectives make algorithm selection, constraint handling, and trade-off analysis difficult. This skill provides practical pymoo workflows, examples, and reference guidance.

Mass spectrometry workflows require careful handling of complex formats, algorithms, and parameters. This skill guides PyOpenMS analysis for proteomics, metabolomics, signal processing, identification, and quantitation.

Bayesian analysis requires careful priors, diagnostics, and model validation. This skill guides PyMC model construction, inference, predictive checks, and comparison.

Clinical machine learning projects require specialized data, task, model, and evaluation choices. This skill provides structured PyHealth guidance from dataset preparation through validation.

Large DataFrame workflows can become slow and memory intensive. This skill provides Polars patterns for efficient transformations, lazy execution, I/O, and pandas migration.

Complex chart APIs make it difficult to choose chart types, configure interactions, and produce polished results. This skill provides focused Plotly guidance and examples.

Finding relevant macromolecular structures and metadata can require several RCSB interfaces. This skill guides focused searches, retrieval, downloads, and batch workflows.

Drug discovery teams need fast access to target, disease, evidence, and drug data. This skill guides Claude, Codex, and Claude Code through Open Targets GraphQL workflows.

Microscopy teams need reliable access to OMERO data and analysis workflows. This skill provides practical Python patterns for connections, images, metadata, ROIs, tables, and batch scripts.

Neuropixels analysis requires many linked preprocessing, sorting, and curation steps. This skill provides practical SpikeInterface workflows, scripts, and references for reproducible neural recording analysis.

Physiological signal analysis requires many specialized processing steps. This skill guides Claude and Claude Code through NeuroKit2 workflows for ECG, EEG, EDA, respiration, PPG, EMG, EOG, HRV, and event analysis.

Finding consistent metabolite, study, and mass-spectrometry records across a large repository can require detailed API knowledge. This skill guides precise Metabolomics Workbench REST queries and structured interpretation.

Plotting with Matplotlib can be hard to structure, style, and export consistently. This skill guides chart design, figure layout, styling, troubleshooting, and export choices.

Bioinformatics teams need reproducible workflows without managing complex infrastructure. This skill guides Latch workflow creation, cloud data handling, resource configuration, and deployment.

Genomic interval workflows require precise commands and APIs across several data formats. This skill guides Gtars operations for overlaps, coverage, tokenization, fragments, and references.

Bioinformatics work often requires switching between many databases and command formats. This skill helps Claude use gget to query genes, sequences, structures, expression data, and disease associations through one workflow.

Geospatial work often fails when file formats, coordinate systems, and spatial operations are unclear. This skill provides GeoPandas patterns for loading, transforming, analyzing, and mapping vector data.

Researchers need reliable access to public gene expression studies before analysis. This skill guides GEO search, download, metadata review, and transcriptomics workflows.

Genomic interval analysis needs consistent tokenization, embedding training, and quality checks across BED datasets. This skill guides Geniml workflows for Region2Vec, BEDspace, scEmbed, consensus peaks, and evaluation.