Scientific

torch-geometric skill guide for PyTorch Geometric graph neural networks. Use it for torch-geometric install help, torch-geometric usage, graph classification, node classification, link prediction, heterogeneous graphs, custom MessagePassing layers, and scaling GNNs for Machine Learning workflows.

Use the sympy skill for exact symbolic math in Python, including algebra, calculus, matrices, physics formulas, number theory, geometry, and code generation. It helps you keep expressions exact, choose the right SymPy modules, and avoid float-heavy mistakes. Best for users who need a practical sympy guide for symbolic workflows and sympy for Data Analysis.

The rdkit skill helps with precise cheminformatics workflows: parsing SMILES, SDF, MOL, PDB, and InChI; calculating descriptors; generating fingerprints; running substructure search; handling reactions; and building 2D/3D coordinates. Use this rdkit guide for advanced control, custom sanitization, and rdkit for Data Analysis workflows.

qutip is a Python quantum physics simulation skill for open quantum systems, dissipation, time evolution, and quantum optics. Use this qutip guide for master equations, Lindblad dynamics, decoherence, cavity QED, state/operator simulation, and Scientific Python examples. Not for circuit-based quantum computing.

qiskit is an IBM quantum computing skill for building circuits, choosing backends, transpiling for hardware, and running jobs on simulators or IBM Quantum devices. It is a strong fit for qiskit usage in chemistry, optimization, and machine learning, especially when you need practical install-and-run guidance rather than a theory-only qiskit guide.

paper-lookup is a research retrieval skill for Academic Research, helping you find scholarly papers, preprints, citations, DOI/PMID matches, abstracts, full text, and open access copies across 10 academic databases. Use it for paper-lookup usage when you need the right source first, not a generic web search. The paper-lookup guide points to PubMed, PMC, Crossref, OpenAlex, Semantic Scholar, CORE, arXiv, bioRxiv, medRxiv, and Unpaywall.

hypogenic is a skill for generating and testing hypotheses on tabular or text-derived datasets with LLM support. It helps with hypogenic for Data Analysis by turning empirical questions into structured, testable workflows for classification interpretation, content analysis, and deception detection. Use it when you need evidence-backed hypotheses, not just brainstorming.

The hugging-science skill helps you find and use scientific AI resources from the Hugging Science catalog and the `hugging-science` Hugging Face org. It fits biology, chemistry, climate, genomics, materials, astronomy, and similar work when you need a dataset, model, Space, or blog post you can actually run or cite. Use it for hugging-science usage and hugging-science guide workflows instead of generic search.

histolab is a Python skill for whole-slide image preprocessing in digital pathology. It supports tissue detection, tile extraction, and stain normalization for H&E slides, making it useful for dataset prep, quick tile-based analysis, and lightweight data analysis workflows. Install and use histolab with practical guidance on masks, tilers, and slide management.

The statsmodels skill helps you use statsmodels for data analysis in Python when you need statistical models, inference, and diagnostics. It fits OLS, GLM, discrete outcomes, time series, and mixed models, with coefficient tables, p-values, confidence intervals, and assumption checks. Use this statsmodels guide for econometrics, forecasting, and defensible reporting.

The statistical-analysis skill helps you choose, run, and report defensible tests for Data Analysis, including assumptions, effect sizes, power, and APA-style results. Use it for academic research, experiments, and observational studies when test selection and clear reporting matter more than coding a specific model.

scvi-tools is a Python framework for probabilistic single-cell analysis. Use this scvi-tools skill for batch correction, latent embeddings, differential expression with uncertainty, transfer learning, and multimodal integration. It is a strong fit for single-cell RNA-seq, ATAC, CITE-seq, multiome, and spatial workflows, especially for advanced Machine Learning use cases.

scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.

scientific-writing is a core skill for the deep research and writing tool. It turns research notes, outlines, and source findings into publication-ready scientific prose with IMRAD structure, full paragraphs, citation styles like APA/AMA/Vancouver, and reporting guidelines such as CONSORT, STROBE, and PRISMA. Use it for journal papers, revisions, abstracts, and submission-ready drafts.

scientific-visualization is a meta-skill for publication-ready figures. Use it for journal submission plots with multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and Nature/Science/Cell-style formatting. It orchestrates matplotlib, seaborn, and plotly for scientific-visualization for Data Visualization work.

Build slide decks and presentations for research talks with the scientific-slides skill. Use it for conference presentations, seminar talks, thesis defenses, lab updates, and other scientific slide decks. It emphasizes clear narrative, minimal text, visual hierarchy, citations, and talk-ready structure for PowerPoint or LaTeX Beamer.

scientific-critical-thinking helps evaluate scientific claims, study design, bias, confounding, and evidence quality. Use it for critical analysis, literature review support, GRADE or Cochrane risk-of-bias checks, and scientific-critical-thinking for Peer Review-style assessment of what a paper can truly support.

scholar-evaluation helps evaluate scholarly and research work with structured scoring across problem formulation, methodology, analysis, writing, and publication readiness. Use it for academic review, revision planning, and consistent feedback on papers, proposals, literature reviews, and other scholarly drafts.

scientific-brainstorming is a research ideation skill for open-ended scientific thinking. Use it to explore interdisciplinary links, challenge assumptions, identify research gaps, and shape early-stage project ideas before you have a tight dataset or final hypothesis.

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. The rowan skill is best for batch pKa prediction, conformer and tautomer ensembles, docking, cofolding, molecular dynamics, permeability, and descriptor workflows when you want reproducible, programmatic runs without managing local HPC or GPU infrastructure.

pytdc is a skill for Therapeutics Data Commons, giving AI-ready drug discovery datasets and benchmarks for ADME, toxicity, DTI, DDI, generation, scaffold splits, and pharmacological prediction.

pyopenms is a Python-based mass spectrometry skill for proteomics and metabolomics workflows. Use it to install pyopenms, load and inspect mzML and related files, process spectra, detect features, identify peptides and proteins, and build reproducible LC-MS/MS data analysis pipelines.

pymoo is a Python skill for single- and multi-objective optimization, Pareto fronts, constrained problems, and benchmark tests. Use this pymoo guide to choose algorithms like NSGA-II, NSGA-III, and MOEA/D, follow the install and usage workflow, and apply pymoo for Data Analysis when multiple metrics must be balanced.

PyMC is a Bayesian modeling skill for building, fitting, checking, and comparing probabilistic models in Python. Use pymc for hierarchical regression, multilevel analysis, time series, missing data, measurement error, and model comparison with LOO or WAIC.