Censored time-to-event data requires specialized preparation, modeling, and evaluation. This skill guides practical scikit-survival workflows from outcome encoding through model comparison.

Classical machine learning projects require careful preprocessing, model selection, and evaluation. This skill provides scikit-learn guidance, reusable pipeline patterns, and complete examples.

Bioinformatics workflows require precise handling of sequences, trees, diversity, and ecological statistics. This skill guides Claude, Codex, and Claude Code through practical scikit-bio methods.

Scientific figures often fail journal checks for size, color, typography, or export format. This skill guides Claude, Codex, and Claude Code through publication-ready plotting workflows.

Single-cell RNA-seq workflows require careful QC, normalization, clustering, and documentation. This skill gives Scanpy workflows, scripts, templates, and references for reproducible analysis.

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

Genomic formats and coordinate rules make sequencing workflows difficult to implement correctly. This skill provides focused Pysam guidance and practical analysis patterns.

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.

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

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.