D3

d3js-visualization

A workflow for building standalone D3.js visualizations from local data files.

Install

mkdir -p .claude/skills/d3js-visualization && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1884" && unzip -o skill.zip -d .claude/skills/d3js-visualization && rm skill.zip

Installs to .claude/skills/d3js-visualization

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Build deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.
274 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Generate standalone HTML/SVG visualizations
  • Apply deterministic sorting to data
  • Create interactive tooltips and click handlers
  • Pin D3.js version for reproducibility

How it works

The skill binds structured data to SVG elements using D3.js while enforcing strict determinism rules to ensure stable, reproducible output.

Inputs & outputs

You give it
Structured data and chart requirements
You get back
Standalone HTML/SVG visualization files

When to use d3js-visualization

  • Creating static charts from JSON data
  • Generating SVG plots for reports
  • Visualizing local data sets without external libraries

About this skill

D3.js Visualization Skill

Use this skill to turn structured data (CSV/TSV/JSON) into clean, reproducible visualizations using D3.js. The goal is to produce stable outputs that can be verified by diffing files or hashing.

When to use

Activate this skill when the user asks for any of the following:

  • “Make a chart/plot/graph/visualization”
  • bar/line/scatter/area/histogram/box/violin/heatmap
  • timelines, small multiples, faceting
  • axis ticks, scales, legends, tooltips
  • data-driven SVG output for a report or web page
  • converting data to a static SVG or HTML visualization

If the user only needs a quick table or summary, don’t use D3—use a spreadsheet or plain markdown instead.


Inputs you should expect

  • One or more local data files: *.csv, *.tsv, *.json
  • A chart intent:
    • chart type (or you infer the best type)
    • x/y fields and aggregation rules
    • sorting/filtering rules
    • dimensions (width/height) and margins
    • color rules (categorical / sequential)
    • any labeling requirements (title, axis labels, units)
  • Output constraints:
    • “static only”, “no animation”, “must be deterministic”, “offline”, etc.

If details are missing, make reasonable defaults and document them in comments near the top of the output file.


Outputs you should produce

Prefer producing all of the following when feasible:

  1. dist/chart.html — standalone HTML that renders the visualization
  2. dist/chart.svg — exported SVG (stable and diff-friendly)
  3. (Optional) dist/chart.png — if the task explicitly needs a raster image

Always keep outputs in a predictable folder (default: dist/), unless the task specifies paths.


Determinism rules (non-negotiable)

To keep results stable across runs and machines:

Data determinism

  • Sort input rows deterministically before binding to marks (e.g., by x then by category).
  • Use stable grouping order (explicit Array.from(grouped.keys()).sort()).
  • Avoid locale-dependent formatting unless fixed (use d3.format, d3.timeFormat with explicit formats).

Rendering determinism

  • No randomness: do not use Math.random() or d3-random.
  • No transitions/animations by default (transitions can introduce timing variance).
  • Fixed width, height, margin, viewBox.
  • Use explicit tick counts only when needed; otherwise rely on D3 defaults but keep domains fixed.
  • Avoid layout algorithms with non-deterministic iteration unless you control seeds/iterations (e.g., force simulation). If a force layout is required:
    • fix the tick count,
    • fix initial positions deterministically (e.g., sorted nodes placed on a grid),
    • run exactly N ticks and stop.

Offline + dependency determinism

  • Do not load D3 from a CDN.
  • Pin D3 to a specific version (default: [email protected]).
  • Prefer vendoring a minified D3 bundle (e.g., vendor/d3.v7.9.0.min.js) or bundling with a lockfile.

File determinism

  • Stable SVG output:
    • Avoid auto-generated IDs that may change.
    • If you must use IDs (clipPath, gradients), derive them from stable strings (e.g., "clip-plot").
  • Use LF line endings.
  • Keep numeric precision consistent (e.g., round to 2–4 decimals if needed).

Recommended project layout

If the task doesn't specify an existing structure, use:

dist/
  chart.html        # standalone HTML with inline or linked JS/CSS
  chart.svg         # exported SVG (optional but nice)
  chart.png         # rasterized (optional)
vendor/
  d3.v7.9.0.min.js  # pinned D3 library

Interactive features (tooltips, click handlers, hover effects)

When the task requires interactivity (e.g., tooltips on hover, click to highlight):

Tooltip pattern (recommended)

  1. Create a tooltip element in HTML:
<div id="tooltip" class="tooltip"></div>
  1. Style with CSS using .visible class for show/hide:
.tooltip {
    position: absolute;
    padding: 10px;
    background: rgba(0, 0, 0, 0.8);
    color: white;
    border-radius: 4px;
    pointer-events: none;  /* Prevent mouse interference */
    opacity: 0;
    transition: opacity 0.2s;
    z-index: 1000;
}

.tooltip.visible {
    opacity: 1;  /* Show when .visible class is added */
}
  1. Add event handlers to SVG elements:
svg.selectAll('circle')
    .on('mouseover', function(event, d) {
        d3.select('#tooltip')
            .classed('visible', true)  // Add .visible class
            .html(`<strong>${d.name}</strong><br/>${d.value}`)
            .style('left', (event.pageX + 10) + 'px')
            .style('top', (event.pageY - 10) + 'px');
    })
    .on('mouseout', function() {
        d3.select('#tooltip').classed('visible', false);  // Remove .visible class
    });

Key points:

  • Use opacity: 0 by default (not display: none) for smooth transitions
  • Use .classed('visible', true/false) to toggle visibility
  • pointer-events: none prevents tooltip from blocking mouse events
  • Position tooltip relative to mouse with event.pageX/pageY

Click handlers for selection/highlighting

// Add 'selected' class on click
svg.selectAll('.bar')
    .on('click', function(event, d) {
        // Remove previous selection
        d3.selectAll('.bar').classed('selected', false);
        // Add to clicked element
        d3.select(this).classed('selected', true);
    });

CSS for highlighting:

.bar.selected {
    stroke: #000;
    stroke-width: 3px;
}

Conditional interactivity

Sometimes only certain elements should be interactive:

.on('mouseover', function(event, d) {
    // Example: Don't show tooltip for certain categories
    if (d.category === 'excluded') {
        return;  // Exit early, no tooltip
    }
    // Show tooltip for others
    showTooltip(event, d);
})

When not to use it

  • When a simple table or markdown summary suffices
  • When external network dependencies are required

Prerequisites

Structured data files (CSV, TSV, JSON)

Limitations

  • No support for non-deterministic animations
  • Requires manual handling of complex layout algorithms

How it compares

It produces standalone, deterministic files without external network calls, unlike standard web-based charting libraries.

Compared to similar skills

d3js-visualization side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
d3js-visualization (this skill)72moReviewIntermediate
d3-visualization74moNo flagsAdvanced
antv-l721moNo flagsIntermediate
vchart-development-assistant14moReviewIntermediate

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Example prompts that trigger this skill in your AI assistant.

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