Quantum computing projects require coordinated circuit design, transpilation, execution, and result analysis. This skill provides practical Qiskit guidance for simulations, algorithms, visualizations, and supported quantum hardware.

Deep learning projects become difficult to maintain when training, data, logging, and scaling logic are mixed. This skill provides structured PyTorch Lightning guidance and templates.

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

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.

Materials workflows often require complex structure, thermodynamic, and electronic analysis. This skill provides pymatgen guidance, reusable workflows, and focused scripts for common tasks.

Laboratory automation code can be difficult to structure across devices and labware. This skill helps Claude, Codex, and Claude Code create PyLabRobot protocols with clear resources, backends, simulation, and safety checks.

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

Biomedical researchers need precise, reproducible searches across a large literature database. This skill builds PubMed queries and guides E-utilities retrieval workflows.

Chemical identifiers and assay records are difficult to retrieve and compare consistently. This skill guides Claude, Codex, and Claude Code through focused PubChem queries and structured interpretation.

Scientific teams need reliable guidance for complex protocols.io API workflows. This skill maps authentication, protocol, workspace, discussion, file, and experiment tasks to documented API operations.

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.

Quantum workflows require careful circuit, device, gradient, and framework choices. This skill provides PennyLane patterns for building, training, and evaluating those workflows.

Scientific peer review requires consistent checks across methods, statistics, ethics, figures, and reporting standards. This skill guides Claude, Codex, and Claude Code through structured review workflows with actionable feedback.

Complex PDF workflows often need form fields, table extraction, OCR, and validation. This skill provides documented workflows and scripts for reliable PDF processing.

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

Academic papers are hard to turn into clear promotion assets. This skill guides Paper2All workflows for websites, videos, and posters.