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Standard single-cell RNA-seq analysis pipeline for quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Ideal for exploratory scRNA-seq analysis using established workflows.

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Gemini 3.1 Pro Preview ensures quality, regenerating only if below your document's threshold, specializing in neural networks, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

A meta-skill for creating publication-ready figures, including multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (e.g., Nature, Science, Cell). It orchestrates matplotlib/seaborn/plotly with publication styles, but for quick exploration, use seaborn or plotly directly.

A biological data toolkit for microbiome analysis, offering sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, and FASTA/Newick I/O.

A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill for working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, and handling competing risks.

scVelo performs RNA velocity analysis to estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data, complementing Scanpy/scVI-tools for trajectory inference.

Deep generative models for single-cell omics are ideal for advanced modeling, batch effects, and multimodal data, offering probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, and multi-modal integration (TOTALVI, MultiVI). For standard analysis pipelines, use scanpy.

Statistical visualization with pandas integration for quick exploration of distributions, relationships, and categorical comparisons. It's best for box plots, violin plots, pair plots, and heatmaps, built on matplotlib.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

Perform zero-shot time series forecasting with Google's TimesFM foundation model. It handles any univariate time series (sales, sensors, energy, vitals, weather) without custom model training, supporting CSV/DataFrame/array inputs for point forecasts and prediction intervals, and includes a preflight system checker.