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Dataviz

Community
open-fox
dataviz

Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium (HTML/React artifact, inline SVG, matplotlib/plotly/d3/Recharts, PNG). Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / KPI row, or laying out a dashboard. Teaches a design-system-agnostic method - form heuristic, color formula with a runnable validator, mark specs, interaction rules. Default palette in references/palette.md.

Overview

Publisheropen-fox
Repositoryagents
Skill namedataviz
Stars
55
Forks
19
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

    Published by open-fox on GitHub. Read the source before you install it.

Installation

Install the Dataviz 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/open-fox/agents/tree/main/skills/dataviz
  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/open-fox/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/dataviz .claude/skills/dataviz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dataviz 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 Dataviz 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 Dataviz 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.

Data Visualization

A chart is read by people and executed by you. This skill turns "make it look good" into a procedure with checks, so the result is right by construction rather than by taste.

The method here is design-system-agnostic. Nothing in the procedure, the form heuristic, the six checks, or the mark specs is specific to one product. A design system supplies a small set of parameters (its ramps, a categorical order, a diverging pair, a status palette, a texture, its surfaces, its filter components); the method consumes them unchanged. A validated default palette is the reference instance, fully specified in references/palette.md. To target your brand, read that file's structure and substitute its values - touch nothing else.

The single most important habit: the color part is computable, so compute it. Never eyeball whether a palette is colorblind-safe - run scripts/validate_palette.js.

The procedure - do these in order

Color comes LAST. Most bad charts pick colors first.

  1. Pick the form. What is the data's job - magnitude, identity, polarity, a single headline, change-over-time? The job picks the chart type, and sometimes the answer is not a chart (a stat tile or hero number). -> references/choosing-a-form.md
  2. Assign color by the job it does. Categorical (identity), sequential (magnitude), diverging (polarity), or status (state) - each has one rule. Assign categorical hues in fixed order, never cycled. -> references/color-formula.md
  3. VALIDATE the palette - run the script, don't reason about Delta E. node scripts/validate_palette.js "<hex,hex,...>" --mode light (relative to this skill's base directory - or load it as <script type="module"> in the chart's own page, where it reads data-palette off <body> and logs a console.table report). It returns pass/fail on the lightness band, chroma floor, adjacent-pair CVD separation, the normal-vision floor, and contrast. Fix anything that FAILs before continuing. Re-run for --mode dark with that mode's surface.
  4. Apply mark specs & spacers. Thin marks, 4px rounded data-ends anchored to the baseline, 2px lines, >=8px markers, a 2px surface gap between fills (stacked segments and adjacent bars alike) and a 2px surface ring on overlapping marks, selective direct labels. -> references/marks-and-anatomy.md
  5. Add the hover layer - by default. An HTML/SVG chart is interactive; ship a crosshair+tooltip on line/area and a per-mark hover tooltip on bar/dot/cell. The only form that skips it is a bare stat tile with no plot. Hit targets bigger than the mark; filters in one row above the charts. -> references/interaction.md
  6. Final accessibility pass. For >= 2 series a legend is always present and <= 4 are also direct-labeled (a single series needs no legend box - the title names it), so identity is never color-alone; a table view exists; dark mode is selected - its own steps from the same ramps, validated against the dark surface, not an automatic flip; texture is available for the CVD/print/forced-colors case.
  7. Render it and look at it. The validator checks color, not layout - open or screenshot the output and eyeball it for label collisions, geometry, and overflow before calling it done.

Then check the result against references/anti-patterns.md - it is the catalog of what goes wrong. If your chart matches an entry, it's wrong.

Non-negotiables (true in every design system)

  • Assign categorical hues in fixed order, never cycled. A 9th series is never a generated hue - it folds into "Other," small multiples, or composite encoding.
  • One axis. Never a dual-axis chart (two y-scales). Two measures of different scale -> two charts, small multiples, or indexed to a common base. (This is the #1 chart mistake - see anti-patterns.)
  • Color follows the entity, never its rank. A filter that changes the series count must not repaint the survivors.
  • Sequential = one hue, light->dark. Diverging = two hues + a neutral gray midpoint. Never a rainbow; never a hue at the diverging midpoint.
  • Run the validator before shipping any categorical palette. CVD Delta E >= 8 is the target (OKLab x100); 6-8 is a floor that is legal ONLY with secondary encoding. A normal-vision floor below 15 is a hard FAIL - full-color readers can't tell the pair apart; re-step it on the adjacent pairlist (secondary encoding does not excuse this one); under --pairs all cut series or facet instead - see check 4. A contrast WARN obligates visible labels or a table view - it is not dismissable.
  • Thin marks; a legend always present for >= 2 series (none for one), with selective direct labels (never a number on every point); recessive grid/axes.
  • Text wears text tokens, never the series color - values, labels, and legends stay in primary/secondary/muted ink; a colored mark beside them carries identity.
  • Status colors are reserved (good/warning/serious/critical) and never reused for "series 4"; they ship with an icon + label, never color alone.

Plugging in a design system

The method is invariant; only these parameters change per system. The reference instance - every value filled in - is references/palette.md.

ParameterWhat the system provides
Rampsthe hue scales (named steps) the palette draws from
Categorical themethe fixed hue order (a named theme); default + alternates
Sequential huethe default single hue for magnitude
Diverging pairtwo warm/cool poles + a neutral midpoint
Status palettegood / warning / serious / critical - steps distinct from categorical
Texture fillone directional hand-drawn fill, used at 45° / 135°
Surfaceslight & dark chart-surface colors (the validator needs these)
Filter controlsdate-range & dimension controls (behavioral spec in interaction.md)

To onboard a new system: fill those rows, feed its ramps to the validator, and let it snap each slot to the nearest passing step. Structure and rules stay as written.

Reference files

FileWhat it answers
references/choosing-a-form.mdWhich chart type / is it even a chart?
references/color-formula.mdThe four jobs, the six checks, snap-to-passing
references/marks-and-anatomy.mdMark specs, spacers, labels, figures, hero number
references/interaction.mdTooltips & hover, filters & time ranges
references/components.mdThe pieces a chart is made of - build each in plain HTML
references/anti-patterns.mdWhat goes wrong - check every chart against this
references/palette.mdThe reference palette instance - every parameter, filled in; swap for your brand's
scripts/validate_palette.jsRunnable six-checks validator (run it; don't eyeball)

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 Dataviz AI skill do?

Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium (HTML/React artifact, inline SVG, matplotlib/plotly/d3/Recharts, PNG). Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / KPI row, or laying out a dashboard. Teaches a design-system-agnostic method - form heuristic, color formula with a runnable validator, mark specs, interaction rules. Default palette in references/palette.md.

Why use Dataviz on TypingMind?

Because you install it once and use it with any model. Dataviz 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 Dataviz in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/open-fox/agents/tree/main/skills/dataviz. 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 Dataviz?

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 Dataviz?

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

Is the Dataviz AI skill free?

It is published on GitHub by open-fox. Check the repository for licensing terms. 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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