Skill UI

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.

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.

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.

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.

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.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Extracts business domain knowledge from a codebase, optionally reusing an existing knowledge graph, and generates an interactive horizontal flow graph for dashboard visualization, with lightweight scanning and validation steps.

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

Guides zero-to-live Star Office visualization for Aion/OpenClaw by installing, starting, connecting, and troubleshooting the local UI service, checking ports/auth, and offering alternative open-source monitors when needed.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.

Analyzes diverse codebases to auto-detect technology stacks, microservices/monorepo traits, and frontend presence, then documents consistent folder conventions, visualization options, and template guidance to keep large projects organized.

A systematic tool for scientific inquiry, guiding users through the entire research process. It helps formulate testable, mechanistic hypotheses from raw observations or preliminary data, following the established scientific method. The workflow includes comprehensive literature review, evidence synthesis, generating competing explanations, and designing experiments, all while mandating the creation of scientific schematics for visualization.