114 件 ・ 関連度順
概要と使いどころ
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
日本語の概要は準備中です。原文の説明を表示しています。
Design and implement parallax scrolling and scrollytelling data visualizations. Use when the user asks for parallax scrolling, scrollytelling, scroll-driven timelines, sticky graphics, Scrollama, ScrollTrigger, ScrollTimeline, view timelines, rich-media timelines, moviescrollers, scroll-scrubbed charts, staged narrative reveals, or interactive visual stories where scrolling changes a data visualization or media scene.
日本語の概要は準備中です。原文の説明を表示しています。
Test data visualizations and dashboards. Use when the user needs chart or diagram test strategy, screenshot or image diff testing, visual regression, mocked or synthetic chart data, component or unit tests, E2E dashboard QA, interactive UML-like diagram verification, scroll-driven story verification, export verification, or guidance on avoiding brittle over-testing.
日本語の概要は準備中です。原文の説明を表示しています。
Build typed data visualizations in TypeScript. Use when the user wants TypeScript visualization code, typed data models, browser visualization components, UML-like diagram models, interactive graph or architecture diagram contracts, scroll-driven scene contracts, library selection guidance, or a maintainable visualization architecture beyond React- or Next-specific concerns.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate data visualizations into React and Next.js applications. Use when the user needs chart components, UML-like or architecture diagram components, React integration patterns, Next.js client or server boundaries, hydration-safe rendering, lazy loading, framework-aware performance, scroll-driven visual stories, or export guidance.
日本語の概要は準備中です。原文の説明を表示しています。
Build custom data visualizations with D3. Use when the user needs SVG or DOM-based charts, rich annotation, domain-native contextual backgrounds, data joins, custom scales or interactions, scroll-driven SVG scene states, or precise control over browser visualization behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Route web data visualization work. Use when the user needs chart choice, visual critique, dashboards, maps or geospatial views, Gantt timelines, UML/software diagrams, scrollytelling, reports or exports, testing, accessibility, browser implementation, or concept-first visual design.
日本語の概要は準備中です。原文の説明を表示しています。
Render data visualizations with Canvas2D. Use when the visualization needs high mark counts, fast redraws, immediate-mode rendering, custom hit testing, or a hybrid Canvas plus SVG or HTML architecture.
日本語の概要は準備中です。原文の説明を表示しています。
Create effective data visualizations using best practices for clarity, accuracy, and visual communication of insights
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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`.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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）。
日本語の概要は準備中です。原文の説明を表示しています。
Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
日本語の概要は準備中です。原文の説明を表示しています。
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
日本語の概要は準備中です。原文の説明を表示しています。
Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。