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Found 448 Skills
Advanced 3D features including VoxelLayer, PointCloudLayer, weather effects, daylight simulation, glTF model imports, and custom WebGL rendering. Use for volumetric data, LiDAR visualization, and immersive 3D experiences.
Create custom, highly interactive data visualizations with D3.js (Data-Driven Documents)
Use when designing visual interfaces, data visualizations, educational content, or presentations and need to ensure they align with how humans naturally perceive, process, and remember information. Invoke when user mentions cognitive load, visual hierarchy, dashboard design, form design, e-learning, infographics, or wants to improve clarity and reduce user confusion. Also applies when evaluating existing designs for cognitive alignment or choosing between design alternatives.
Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.
Create standalone debugging interfaces that reveal the internal workings of complex systems through interactive visualization. Use when the user wants to understand how something works, debug internal state, visualize data flow, see what happens when they interact with the system, or build a debug panel for any complex mechanism. Triggers on requests like "I don't understand how this works", "show me what's happening", "visualize the state machine", "build a debug view for this", "help me see the data flow", "make this transparent", or any request to understand, debug, or visualize internal system behavior. Applies to state machines, rendering systems, event flows, algorithms, animations, data pipelines, CSS calculations, database queries, or any system with non-obvious internal workings.
Use when implementing globe.gl (Globe.GL) for 3D globe data visualization with WebGL/ThreeJS, including setup, data layers (points, arcs, polygons, labels), and integration patterns in plain HTML or React.
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
This skill should be used whenever domain modeling is taking place. It provides specialized guidance for type-driven and data-driven design based on Rich Hickey and Scott Wlaschin's principles. The skill helps contextualize current modeling within the existing domain model, identifies inconsistencies, builds ubiquitous language, and creates visualizations (Mermaid, Graphviz/DOT, ASCII diagrams) to communicate domain concepts clearly. Use this skill when designing types, modeling business domains, refactoring domain logic, or ensuring domain consistency across a codebase.
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.
Provides comprehensive guidance for Lime ECharts including chart creation, configuration, data visualization, and interactive charts. Use when the user asks about Lime ECharts, needs to create charts, visualize data, or work with ECharts features.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.