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Ask data questions in everyday language, from checking a single metric to investigating trends or preparing a formal report. Receive findings supported by queries, tables, or charts, with data validation and clear limitations.
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Choose suitable charts for trends, comparisons, distributions, and relationships, then create them in Python with matplotlib, seaborn, or plotly. Apply guidance on colors, labels, scales, and accessibility to produce publication-quality figures or interactive charts.
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Design and build visual stories where scrolling changes charts, maps, or video. Plan meaningful scene transitions, choose suitable implementation tools, and provide mobile layouts and static alternatives for readers who prefer reduced motion.
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Create interactive views of dense datasets, spatial paths, and flows using WebGL, which uses the GPU for rendering. Choose suitable libraries and design navigation, labels, animation, and static alternatives around the analytical task.
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Build charts and interactive diagrams in TypeScript with explicit data types and clear component boundaries. Guides library selection, data validation, interaction design, and shareable view settings to support maintainable visualization code.
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Integrate charts and architecture diagrams into React or Next.js apps. Plan which parts run in the browser, separate rendering from application state, and address loading performance, mobile layouts, accessibility, and shareable views.
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Design tests for charts, dashboards, and interactive diagrams. Combine data checks, component tests, screenshot comparisons, and end-to-end workflows to catch incorrect values, broken interactions, and visual changes while keeping tests stable.
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Routes web visualization work to suitable specialist skills, from chart selection and critique to maps, dashboards, and software diagrams. Helps plan readable views, mobile interactions, accessible controls, shareable state, exports, and testing around the question the data should answer.
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Build dense, frequently updated charts with Canvas2D, including zooming, clicking, and dragging. Guides rendering performance, mobile interactions, and the use of SVG or HTML for labels, controls, and accessibility.
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Design live monitoring dashboards with clear priorities, linked charts, and predictable update behavior. Covers mobile interactions, connection failures, and rendering performance so users can scan current conditions, spot anomalies, and explore details as data changes.
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Build custom browser charts with D3, including precise labels, annotations, zooming, and range selection. Create SVG graphics that adapt to mobile screens, use meaningful contextual backgrounds, and support export when needed.
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Create clear, accurate charts for dashboards, data exploration, and reports. Choose chart types that fit the message, refine labels and scales, use accessible colors, and add context and annotations to help readers understand the findings.
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Use this skill whenever the user asks for a chart, graph, plot, or visualization of data — 'chart this by month', 'show me a breakdown by category', 'visualize the trend'. Covers picking the right chart type for the question being asked, labeling/readability rules, and which plotting library to reach for. Pair with data-analysis for the underlying data prep.
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Create analytical charts and plots from existing data. Use for exploratory or reporting visuals such as bars, lines, scatters, and dashboards; not for publication-grade scientific figures or AI-generated schematics.
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Build a product colour system — tonal scales, semantic roles, and contrast compliance. Use when defining or rebuilding colour from scratch. For dark-mode adaptation use `dark-mode-design`; for chart palettes use `data-visualization`; for multi-brand token architecture use `theming-system` (design-systems).
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Select chart types and design data encodings — marks, axes, labels, and accessible chart styling. Use when presenting data graphically. Owns chart selection and encoding only; the categorical colour ramp itself belongs to `color-system`.
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Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data-visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.
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数据可视化的判断层 SSOT——画任何图表、dashboard、看板之前先加载它，不是画完之后。管"该不该这么画"：这张图想让读者得出什么结论、用均值还是中位数、能不能聚合、堆叠还是折线、能不能上双轴、配色与颜色数量、同一实体跨图是否同色同位、表格该留几列。介质无关：HTML 报告页、React/Vue dashboard、PPT 原生图表、matplotlib/plotly/ECharts/D3、渲染成 PNG 的图，一律适用。Use when 动手画图之前，或评审既有图表（"这几张图有什么问题"、"图例为什么不一致"）。Not for 报告页的排版/组件/交互（用 report-with-html:report-with-html）或 PPT 原生图表的实现与渲染陷阱（用 deck-creator:deck-creator）。
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Produces the reader-facing HTML artifact — report, dashboard, architecture view, data browser, or review workbench — evidence-backed, browser-verified. Use when the deliverable IS a visual HTML report, or the user asks for an HTML/web presentation. Load data-visualization-discipline for chart judgment. Not for chat status or Markdown prose unless asked to become a report; not for PowerPoint (deck-creator Route C).
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Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
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Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
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Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.
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Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.
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