Skill UI

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Creates and validates Mermaid diagrams across 20+ types, renders SVG/PNG/ASCII, enforces curated styling, and supports batch rendering with themes; ideal for architecture, flow, and ERD visualization requests.

A comprehensive toolkit for navigating, optimizing, and managing large monorepos built with JavaScript/TypeScript. It supports industry-leading tools like Turborepo, Nx, pnpm, and Lerna. Key features include cross-package impact analysis, selective builds, remote caching, dependency graph visualization, and structured migrations, ensuring fast, reliable CI/CD pipelines for complex codebases.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

Provides charting, mapping, and dashboard techniques to turn datasets into clear insights for sharing across teams.

This comprehensive tool helps users set, track, and optimize personal health goals using deep data analysis. It validates goals against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) and tracks progress by correlating diverse health data, including nutrition, exercise, and sleep patterns. Key features include habit formation analysis, motivation assessment, identifying roadblocks, and generating interactive reports with ECharts visualizations, providing a holistic view of wellness journeys.

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

Master the creation and manipulation of 3D geometry within the Three.js framework. This guide covers built-in primitive shapes (Box, Sphere, Cylinder), advanced procedural geometries (Extrude, Tube, TorusKnot), and text visualization. Crucially, it details the use of BufferGeometry, allowing developers to store vertex, normal, UV, and color data in optimized typed arrays for high-performance rendering and custom mesh construction.

Umap provides uniform manifold approximation for dimension reduction and visualization, helping data scientists explore high-dimensional embeddings through interpretable 2D/3D layouts.

Vizcom is an advanced AI platform designed to accelerate the product ideation and visualization process. It transforms rough inputs, such as hand-drawn sketches, basic line art, or simple text descriptions, into high-fidelity, photorealistic 3D renders. It is ideal for concept generation in physical products like furniture, electronics, and consumer goods, allowing designers to explore aesthetic variations and generate stunning marketing visuals.