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Guide for using Observable Plot, a JavaScript library for exploratory data visualization built on D3. Use this skill when creating charts, exploring data, or building visualizations with marks, scales, and transforms.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/maragudk/fabrik --skill observable-plot命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Guide for using git with specific preferences -- backticks around code identifiers in commit messages, asking about GitHub issues to reference before committing. Use this whenever you commit, write a commit message, or work with pull requests -- not just when explicitly asked to "commit". These conventions aren't in your default knowledge and you'll get them wrong without consulting this skill.
Guide for making code reviews. Use this when asked to make code reviews, or ask to use it before committing changes.
Comprehensively reviews SwiftUI code for best practices on modern APIs, maintainability, and performance. Use when reading, writing, or reviewing SwiftUI projects.
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| name | observable-plot |
| description | Guide for using Observable Plot, a JavaScript library for exploratory data visualization built on D3. Use this skill when creating charts, exploring data, or building visualizations with marks, scales, and transforms. |
| license | MIT |
Observable Plot is a JavaScript library for exploratory data visualization. It's built on D3 and provides a concise, declarative API for creating charts.
npm install @observablehq/plot
Or via CDN:
<script type="module">
import * as Plot from "https://cdn.jsdelivr.net/npm/@observablehq/plot@0.6/+esm";
</script>
The main function that renders a visualization. Returns an SVG or HTML figure element.
Plot.plot({
marks: [
Plot.dot(data, {x: "weight", y: "height"})
]
})
| Option | Description | Default |
|---|---|---|
width | Outer width in pixels | 640 |
height | Outer height in pixels | auto |
margin | All margins | varies |
marginTop/Right/Bottom/Left | Individual margins | varies |
marks | Array of marks to render | required |
color | Color scale options | auto |
x, y | Position scale options | auto |
title, subtitle | Chart titles | none |
caption | Figure caption | none |
Plot expects tabular data as arrays of objects:
const data = [
{date: new Date("2024-01-01"), value: 100, category: "A"},
{date: new Date("2024-01-02"), value: 120, category: "B"}
];
Map data columns to visual properties:
Plot.dot(data, {
x: "date", // column name
y: "value", // column name
fill: "category", // color by category
r: 5 // constant radius
})
Plot.lineY(data, {x: "date", y: "value"}).plot()
Plot.barY(data, {x: "category", y: "value"}).plot()
Plot.dot(data, {x: "weight", y: "height", fill: "species"}).plot()
Plot.rectY(data, Plot.binX({y: "count"}, {x: "value"})).plot()
Plot.areaY(data, {x: "date", y: "value", fill: "steelblue"}).plot()
This skill includes detailed documentation for each feature:
marks/area.md - Area charts and stacked areasmarks/bar.md - Bar charts (horizontal and vertical)marks/dot.md - Scatter plots and bubble chartsmarks/line.md - Line chartsmarks/rect.md - Rectangles, histograms, heatmapsmarks/text.md - Text labels and annotationsmarks/rule.md - Reference linesmarks/cell.md - Heatmaps with ordinal dimensionsmarks/tip.md - Interactive tooltipsmarks/axis.md - Custom axesmarks/geo.md - Geographic/map visualizationsmarks/link.md - Connections between pointsmarks/arrow.md - Directed arrowsmarks/vector.md - Vector fieldsmarks/tick.md - Tick marksmarks/box.md - Box plotsmarks/frame.md - Frame decorationmarks/image.md - Image glyphsmarks/contour.md - Contour plotsmarks/density.md - Density estimationmarks/raster.md - Raster/heatmap imagesmarks/hexgrid.md - Hexagonal gridsmarks/waffle.md - Waffle chartsmarks/delaunay.md - Voronoi and Delaunaymarks/bollinger.md - Bollinger bandsmarks/difference.md - Difference chartsmarks/tree.md - Hierarchical treesmarks/auto.md - Automatic mark selectionmarks/linear-regression.md - Trend linesmarks/crosshair.md - Interactive crosshairsmarks/grid.md - Grid linestransforms/bin.md - Binning for histogramstransforms/group.md - Grouping categorical datatransforms/stack.md - Stacking for area/bar chartstransforms/dodge.md - Beeswarm plotstransforms/hexbin.md - Hexagonal binningtransforms/window.md - Moving averagestransforms/select.md - Selecting specific pointstransforms/normalize.md - Normalizing valuesfeatures/scales.md - Scale configurationfeatures/facets.md - Small multiplesfeatures/projections.md - Map projectionsfeatures/legends.md - Legend configurationfeatures/interactions.md - Interactive featuresfeatures/curves.md - Line interpolationfeatures/markers.md - Line markersfeatures/shorthand.md - Concise syntaxfeatures/intervals.md - Time intervalsPlot.plot({
y: {grid: true},
marks: [Plot.line(data, {x: "date", y: "value"})]
})
Plot.plot({
color: {legend: true},
marks: [Plot.dot(data, {x: "x", y: "y", fill: "category"})]
})
Plot.plot({
facet: {data, x: "region"},
marks: [Plot.dot(data, {x: "income", y: "lifeExpectancy"})]
})
Plot.plot({
marks: [
Plot.dot(data, {x: "x", y: "y", tip: true})
]
})
Plot.plot({
marks: [
Plot.areaY(data, {x: "date", y: "value", fill: "category"})
]
})
import * as Plot from "@observablehq/plot";
import { useRef, useEffect } from "react";
function Chart({ data }) {
const ref = useRef();
useEffect(() => {
const plot = Plot.plot({
marks: [Plot.dot(data, {x: "x", y: "y"})]
});
ref.current.append(plot);
return () => plot.remove();
}, [data]);
return <div ref={ref} />;
}
const plot = Plot.plot({
marks: [Plot.dot(data, {x: "x", y: "y"})]
});
document.body.append(plot);