Render WebGL-accelerated data visualizations with Three.js, raw WebGL, deck.gl, luma.gl, PixiJS, Sigma.js, Plotly WebGL traces, ECharts GL, CesiumJS, Babylon.js, or related GPU libraries. Use when the visualization needs true spatial structure, dense 2D or 3D GPU rendering, particle or flow animation, volumetric views, or interactive exploration that adds real analytical value.
npx skills add https://github.com/openai/plugins --skill threejs-data-visualization
Use this skill when the user truly benefits from 3D, GPU-heavy 2D, shader-driven animation, or WebGL-accelerated interaction. Three.js is appropriate for volumetric data, 3D point clouds, spatial trajectories, surfaces, scientific or immersive scenes, and custom particle systems. WebGL or WebGL-backed libraries are also appropriate for dense 2D scatterplots, animated networks, flow maps, particle trails, GPU aggregation, or custom shader effects that exceed practical SVG/DOM limits and are not a good fit for Canvas2D.
Default assumption: use the simplest truthful renderer that meets the scale and interaction requirements. 3D is justified only when depth carries analytical meaning. WebGL is justified when GPU throughput, shader control, large mark counts, or animation quality matter enough to offset accessibility, export, debugging, bundle, and GPU-memory costs. Cosmetic 3D or decorative particles are regressions.
Mobile GPUs, touch gestures, battery, thermal limits, and permissions can change the renderer choice. Use ../../references/foundations/mobile-first-responsive-visualization.md for mobile portrait/landscape contracts, AR/camera/motion/vibration decisions, visual viewport behavior, spotty connection handling, and touch-first controls.
Use particle effects only when they make movement, accumulation, attention, or state more legible:
Do not use particles when they obscure totals, imply individual entities that are not in the data, overstate certainty, glamorize harm, or compete with labels and comparison tasks. Always provide a reduced-motion fallback and a static final or key frame.
BufferGeometry10. Keep generated textures, basemaps, or illustrated backdrops separate from data geometry so they can be reviewed and swapped.
11. For custom shaders, define the data contract first: attributes, uniforms, textures, derived values, picking IDs, and fallbacks.
12. For particles, define emission source, path or field, speed, lifetime, color, opacity curve, decay, selection behavior, and reduced-motion state before coding.
13. For advanced scenes, complete ../../assets/templates/advanced-interactive-visualization-contract.md before implementation. Name one primary scene owner, a true fallback path, coordinate frames, camera states, picking model, animation clock, interaction state machine, render-ready signal, and screenshot QA.
14. Never let a fallback or helper renderer duplicate the primary visual while WebGL is active. One renderer owns the focal scene; fallbacks appear only when the primary scene fails or is intentionally disabled.
15. For mobile, cap DPR or quality when needed, pause offscreen/inactive loops, lazy-load heavy assets with reserved dimensions, and define stale/offline rendering for streamed or tiled data.
../../references/foundations/task-abstraction-and-chart-selection.md../../references/foundations/art-directed-interactive-visual-stories.md../../references/foundations/perception-color-and-encoding.md../../references/foundations/mobile-first-responsive-visualization.md../../references/foundations/implementation-design-and-tradeoffs.md./references/when-3d-is-justified.md./references/scene-architecture-and-encodings.md./references/gpu-scaling-and-interaction.md./references/webgl-library-selection.md./references/webgl-2d-animation-patterns.md./references/particle-effects-and-flow.md./references/scene-readiness-and-interaction-qa.md./references/cutaway-terrain-and-domain-scenes.md../../assets/templates/advanced-interactive-visualization-contract.mdComprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take openai/threejs-data-visualization from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.