D3 Visualization logo

D3 Visualization

OrganizationPopular
benchflow-ai
d3-visualization

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.

Overview

Publisherbenchflow-ai
Repositoryskillsbench
Skill named3-visualization
Stars
1.8K
Forks
371
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by benchflow-ai on GitHub. Read the source before you install it.

Installation

Install the D3 Visualization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/benchflow-ai/skillsbench/tree/main/tasks/data-to-d3/environment/skills/d3-visualization
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git /tmp/skillsbench
mkdir -p .claude/skills
cp -r /tmp/skillsbench/tasks/data-to-d3/environment/skills/d3-visualization .claude/skills/d3-visualization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable D3 Visualization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads D3 Visualization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so D3 Visualization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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: d3@7.9.0).
  • 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:
html
<div id="tooltip" class="tooltip"></div>
  1. Style with CSS using .visible class for show/hide:
css
.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:
javascript
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

javascript
// 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:

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

Conditional interactivity

Sometimes only certain elements should be interactive:

javascript
.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);
})

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the D3 Visualization AI skill do?

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.

Why use D3 Visualization on TypingMind?

Because you install it once and use it with any model. D3 Visualization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install D3 Visualization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/benchflow-ai/skillsbench/tree/main/tasks/data-to-d3/environment/skills/d3-visualization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use D3 Visualization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with D3 Visualization?

As many as you like. As long as a model supports skills, you can use D3 Visualization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the D3 Visualization AI skill free?

Yes. It is published on GitHub by benchflow-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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