Data Viz logo

Data Viz

Organization
KyaniteLabs
data-viz

Honest, Tufte-informed web data visualization. Use for charts, dashboards, metrics, KPI tiles, sparklines, chart accessibility, chartjunk removal, genre choice, lie-factor checks, direct labels, data tables, and themed SVG/HTML.

Overview

PublisherKyaniteLabs
Repositorytastecheck
Skill namedata-viz
Stars
10
Forks
7
Bundled files
5
LicenseMIT
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.

  • 5 bundled files

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

  • Open source

    Published by KyaniteLabs on GitHub. Read the source before you install it.

Installation

Install the Data Viz 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/KyaniteLabs/tastecheck/tree/main/skills/data-viz
  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/KyaniteLabs/tastecheck.git /tmp/tastecheck
mkdir -p .claude/skills
cp -r /tmp/tastecheck/skills/data-viz .claude/skills/data-viz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Viz 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 Data Viz 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 Data Viz 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 Viz (honest, web-native, Tufte)

Make web-native charts with honest marks: tokenized, responsive, accessible. Reuse type, spacing, surface, color tokens; one accent adds craft but never competes with data. Use Tufte tooling when present.

Decision order

  1. State comparison question, source/grain, missingness, and uncertainty.
  2. Prefer a table for small exact lookup; chart shape, change, or comparison.
  3. Encode honestly (zero-baseline bars; 1-D length/position; lie factor ≈1), direct-label, and remove non-data ink.
  4. Provide tokenized, responsive, contrast-safe chart/table parity; keep data keyboard reachable.

Data quality

Before genre, record question, unit, time grain, source, and absent status. A missing month can mean zero, not observed, suppressed, not applicable, or not yet reported; these states cannot share a mark, line, or cell treatment.

Name the uncertainty band (e.g. confidence interval, forecast range, or measurement error), level if known, and shared method. Missing values, units, provenance, or interval meaning means stop at a representation spec/data-request checklist. Never invent a trend, interpolate a gap, or manufacture an empty chart.

For sparse monthly series, prefer an observed-month table or interval strip. If a line helps, break it at missing observations, plot estimated uncertainty only, label the gap, and never imply continuity with a continuous area.

Genre selection

Data shapeUseNot
Small; exact lookuptable (or supertable)slow-lookup chart
1 series/timeline / sparkline3D, decorative area
Few categories/one valuezero bar, direct-labeledpie (hard angles)
Part-to-whole, 2–4bar/stacked; number + barpie/donut (>3 slices)
Many series/facetssmall multiples (shared scale)spaghetti
Group distributionquartile/box or stripbar-of-averages
Two variablesscatter with range framebordered box + gridlines
Table-row trendinline sparklinesseparate chart page
Geographic/multivariatemap / Minard-style layeredrainbow choropleth

Default-challenge rule: justify an unprompted pie, 3-color bar, or spaghetti line, or use a stronger Tufte move (table, small multiples, sparkline, range frame).

Refuse misleading requests

Refuse a dramatic 3-D chart because depth, perspective, and occlusion encode absent values. Name the distortion; offer the smallest honest alternative: direct-labeled interval strip, observed-month table, shared-scale small multiple, or zero-baseline bar.

Non-negotiables

  • Table for lookup; chart for shape. A 12-month trend may deserve a line; a 40-cell audit may belong in a sortable table.
  • Bars start at zero when length encodes magnitude. For floating ranges or deltas, use explicit interval/difference encoding, not a truncated bar.
  • Use position or length for comparison. Never exaggerate area or volume. Redundant color-plus-shape, label, or pattern aids identification/accessibility; channels must agree.
  • Lie factor ≈ 1.0 (visual-change% ÷ data-change%, acceptable 0.95–1.05); check dramatic proportions.
  • Direct labels, not a legend where feasible; label line end/bar.
  • Kill chartjunk: no heavy gridlines, non-data fills/gradients, drop shadows, decorative imagery, or non-data borders.
  • Limit hues to distinguishable colors; color is never the only channel. Use position, direct label, or pattern too (color-blind + WCAG 1.4.1). Series colors come from color-system tokens and must hit ≥3:1 vs background and adjacent series.
  • Works in dark mode (re-check axes/text contrast).
  • Responsive — SVG viewBox/fluid width; no fixed px overflow at 320px.
  • Every chart has a text equivalent — takeaway caption plus accessible data table (visually-hidden or toggle); non-sighted users get the numbers.

Quick-start

Use assets/chart-starter.html; keep range-frame axis, direct end labels, caption, and parity.

References

  • references/tufte-and-genres.md — data-ink, lie factor, genres.
  • references/web-and-a11y.md — tokens, SVG, parity.
  • references/decision-records.md — novel-case ADR rules.

Evidence

Chart/table expose the same value, unit, uncertainty, missingness, source, and filter. Caption what can/cannot be concluded; record quality, genre choice/refusal, encoding, parity, and rendered evidence.

Check

  1. Table/genre fits the comparison, not a default?
  2. Zero bars, 1-D encoding, lie factor ≈1, direct labels/range frame, no chartjunk?
  3. Tokenized ≤5-hue non-color encoding, ≥3:1 contrast, dark mode, 320px behavior?
  4. Caption/table retain source, unit, uncertainty, filters, missingness; gaps imply no continuity.

How to deliver

State genre/rationale, material lie factor, parity, caption, contrast, and theming evidence. Use specialist Tufte tooling only when available.

Provenance

This is an independent web-and-accessibility synthesis of Tufte principles; use specialist assessment/rendering tools when available.

Contract (generated)

Canonical detail: contract.json.

  • Route: Data needs a chart, table, uncertainty treatment, or accessible comparison.; avoid: There is no data subject or the request is only a decorative illustration.
  • Exclude: Do not choose a chart before identifying the comparison task. (+1 in contract.json)
  • Stop / handoff: Stop when the data provenance or comparison question is missing. (+1 in contract.json); receives [color-system, design-system-interview, improve-existing-website, tasteroll] -> sends [a11y-pass, empty-states, tastecheck-pass]
  • Output: honest chart/table recommendation and implementation plan
  • Evidence: table_with_evidence with status, reason, remediation, evidence, provenance.

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 Data Viz AI skill do?

Honest, Tufte-informed web data visualization. Use for charts, dashboards, metrics, KPI tiles, sparklines, chart accessibility, chartjunk removal, genre choice, lie-factor checks, direct labels, data tables, and themed SVG/HTML.

Why use Data Viz on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/KyaniteLabs/tastecheck/tree/main/skills/data-viz. 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 Data Viz?

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 Data Viz?

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

Is the Data Viz AI skill free?

Yes. It is published on GitHub by KyaniteLabs under the MIT 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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