Skill UI

Guides Power BI visualization and UX decisions—capturing business context, selecting optimal charts, specifying layouts, color, typography, and interaction patterns to create accessible, insight-driven dashboards across audiences.

DeepTools is a comprehensive Python command-line toolkit for processing and analyzing high-throughput sequencing data. It supports quality control, normalization, sample comparison, and generating publication-quality visualizations for ChIP-seq, RNA-seq, and ATAC-seq experiments. Key features include converting BAM to bigWig, assessing sequencing depth, creating heatmaps around genomic features, and performing correlation analysis. Ideal for bioinformatics workflows requiring robust genomic data processing and visualization.

A comprehensive skill for creating technical and scientific documentation using Markdown as the source format, enhanced by embedded Mermaid diagrams. This method establishes text-based diagrams as the canonical standard, ensuring that all visualizations are clean, editable, and highly readable in version control systems like Git. It is ideal for generating reports, architectural diagrams, workflows, and any content that requires cross-platform rendering and version history.

Matplotlib is a foundational Python library for creating static, animated, and interactive plots with full customization. It supports various plot types like line, scatter, bar, and heatmaps, suitable for scientific and statistical visualizations. Users can customize appearance, create multi-panel figures, and export to PNG, PDF, or SVG. Ideal for publication-quality graphics and integrating with Jupyter notebooks.

This skill enables the construction and analysis of phylogenetic trees using tools like MAFFT for alignment, IQ-TREE 2 for maximum likelihood inference, and FastTree for large datasets. It supports evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies. Visualization is handled via ETE3 or FigTree. Ideal for inferring evolutionary relationships, detecting horizontal gene transfer, and reconstructing ancestral sequences in biological research.

Perform differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Python framework for single- and multi-objective optimization with NSGA-II/III, MOEA/D, and SPEA2. Supports constraints, ZDT/DTLZ benchmarks, Pareto front analysis, and visualization for engineering design.

Python cheminformatics toolkit for fine-grained molecular control: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D coordinate generation, similarity, reactions, and visualization.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, and visualization. It supports converting R formats to h5ad and includes ready-to-run CLI scripts for end-to-end workflows, making it ideal for exploratory scRNA-seq analysis and publication-quality plotting.

Create truthful, accessible, publication-ready scientific figures using Matplotlib, Seaborn, or Plotly. Ensures data integrity, proper encoding, accessibility compliance, and journal export planning for research publications. Includes guidelines for uncertainty displays, color/contrast review, and metadata validation.

Seaborn is a Python visualization library built on matplotlib for creating statistical graphics. It integrates with pandas for dataset-oriented plotting, offering publication-quality defaults for distributions, relationships, and categorical comparisons. Ideal for exploratory data analysis, multivariate analysis, and complex multi-panel figures. Supports various plot types like box plots, violin plots, and heatmaps.

This skill covers using the SHAP library to explain and audit machine learning predictions. It guides users on selecting explainers, computing feature attributions, and validating results. It includes workflows for local and global visualizations, handling multi-output models, and ensuring model behavior descriptions are accurate without implying causality. Requires Python 3.12+ and specific SHAP versions.