Agent skill

Tufte Data Viz

by caylent in caylent/tufte-data-viz

A skill your agent uses when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization.

MITAuto-check passedData & Analytics

Install Tufte Data Viz

skills CLI
$ npx skills add caylent/tufte-data-viz --skill tufte-data-viz -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install caylent/tufte-data-viz tufte-data-viz --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tufte-data-viz
GitHub stars
223
Token cost
~3.5k tokens
SKILL.md length
1,757 words
Files
34
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization.

  • Works in 12 steps: Identify the message → Apply universal rules → Apply library-specific config → …
  • Any data visualization
  • SKILL.md covers Workflow, Universal rules and Library quick reference
  • Runs Python, JavaScript and TypeScript scripts from its folder

What it does

Tufte Data Viz is an agent skill from caylent/tufte-data-viz. Use when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization. Applies to Recharts, ECharts, Chart.js, matplotlib, Plotly, seaborn, D3.js, and SVG. Enforces Tufte principles (data-ink ratio, direct labeling, range-frame axes) plus modern screen-first standards (accessibility, responsive, dark mode).

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 36 other files (for example `README.md`, `_docs/generate_showcase.py` and `examples/chartjs-tufte-plugin.js`).

It sits in Data & Analytics, covering Data visualization and Accessibility. It works with Chart.js, Matplotlib, Plotly and D3.js. The repository describes itself as: Agent skill: Edward Tufte's data visualization principles for clean, honest, high-data-ink-ratio charts. Recharts, ECharts, Chart.js, matplotlib, Plotly, D3/SVG. The licence is MIT.

When your agent uses it

  • Any data visualization
  • Tasks that involve Data visualization
  • Tasks that involve Accessibility

Example prompts

  • “/tufte-data-viz”

Requirements

  • Python 3
  • Node.js
  • Pre-approved tools (allowed-tools): Read, Glob, Grep

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Identify the message
  2. Apply universal rules
  3. Apply library-specific config
  4. Validate
  5. Remove top and right borders
  6. Direct labels, not legends
  7. No gridlines by default
  8. Range-frame axes
  9. No 3D effects
  10. No pie charts unless explicitly requested
  11. Aspect ratio ~1.5:1
  12. Gray first, highlight selectively

What it can do on your machine

Read from SKILL.md and the folder at commit ae7ca0d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python, JavaScript and TypeScript, from the files we listed), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tufte Data Viz loads about 3.5k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 1,757 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from caylent/tufte-data-viz at commit ae7ca0d, republished under its MIT licence (© caylent). 1,757 words, ~3,472 tokens.

Download SKILL.mdSave it as .claude/skills/tufte-data-viz/SKILL.md (or your agent's skills folder). This skill also uses 33 other files; get the full folder from GitHub.
name
tufte-data-viz
description
Use when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization. Applies to Recharts, ECharts, Chart.js, matplotlib, Plotly, seaborn, D3.js, and SVG. Enforces Tufte principles (data-ink ratio, direct labeling, range-frame axes) plus modern screen-first standards (accessibility, responsive, dark mode).
allowed-tools
Read, Glob, Grep

Tufte Data Visualization

Apply Edward Tufte's principles whenever generating or reviewing code that renders data visually. This skill covers chart generation, not slide/presentation design.

Workflow

Follow these steps in order when creating any chart:

Step 1: Identify the message

Before writing code, determine:

  1. The key finding or trend the chart must make visible.
  2. The comparison context — a baseline, prior period, target, or peer group. A number without context is meaningless.
  3. The chart type that best fits the data structure (see Chart type guidance below).
Step 2: Apply universal rules

Review the rules below. Every rule is a default — deviate only when the user explicitly requests otherwise.

Step 3: Apply library-specific config

Use the Library quick reference table to find the essential overrides for the target library. For complete code examples and helper functions, read ONE rule file from rules/ matching the library.

Step 4: Validate

Run through the validation checklist at the bottom of this file before presenting the chart.


Universal rules

Rules 1–14 cover static principles; 15–19 extend them for screens; 20–22 address content and formatting.

1. Remove top and right borders

No chart should have top or right axis lines, borders, or spines. The bottom and left axes are sufficient. Top and right lines are pure chartjunk.

2. Direct labels, not legends

Label each data series directly — at the endpoint of a line, on or beside a bar, next to a cluster. Remove the <Legend> component entirely. If there is only one series, the chart title provides that context; no label is needed.

3. No gridlines by default

The default is zero gridlines. For static charts where users need to read precise values, add horizontal-only gridlines at very low opacity (0.08–0.12). For interactive charts, prefer a contextual crosshair on hover instead (see rule 15). Never add vertical gridlines.

4. Range-frame axes

Axis lines should span only the range of the data, not from zero to some arbitrary maximum. The axis starts at (or near) the minimum data value and ends at the maximum.

5. No 3D effects

No perspective, no depth, no shadows on chart elements. Two-dimensional data gets two-dimensional representation.

6. No pie charts unless explicitly requested

Default to a horizontal bar chart sorted by value. If the user explicitly asks for a pie chart: maximum 4 slices, 2D only, start at 12 o'clock, direct percentage labels on each slice.

7. Aspect ratio ~1.5:1

Charts should be approximately 50% wider than tall. Standard sizes: 600x400, 750x500, 900x600. Exception: sparklines and small multiples may be more compact.

8. Gray first, highlight selectively

The default data series color is medium gray (#666). Use a single accent color to highlight the most important series or data point. Never use more than 4 distinct colors. Choose the right palette type: categorical (4-color muted) for unordered groups, sequential (single-hue ramp) for ordered magnitude, diverging (two-hue from center) for deviation from a midpoint. See rules/typography-and-color.md for hex values.

9. Off-white background

Light mode: #fffff8. Dark mode: #151515. Never use pure white (#ffffff) or pure black (#000000).

10. Serif fonts for data

Use serif fonts for data labels, annotations, and chart titles: "ET Book", "Palatino Linotype", Palatino, "Book Antiqua", Georgia, serif. Sans-serif (system-ui, sans-serif) is acceptable only for small axis tick labels (11-12px).

11. No dual y-axes

Two y-axes on one chart create false implied correlations. Use small multiples instead — two charts stacked vertically with shared x-axis.

12. Annotate the notable

If the data contains a peak, trough, inflection point, or event boundary, add a text annotation pointing to it directly on the chart. Place annotations in the nearest clear space — offset from the data point with a short leader line if needed. When multiple annotations compete for space, keep only the most important; move others to a footnote or tooltip.

13. Show comparison context

Include at least one reference element: a reference line (average, target, prior period), a shaded band, or a second series. A chart showing one line with no context fails the "Compared to what?" test.

14. Minimal tooltips

Tooltips should be plain text with the data value and label. No colored background, no border, no arrow pointer, no shadow.

15. Progressive disclosure over static density

Default to the Tufte-clean overview — high data-ink, minimal chrome. Layer details through hover, tap, and click (values, annotations, comparisons). Don't frontload everything onto a single static view. A contextual crosshair on hover replaces permanent gridlines.

16. Accessible by default

3:1 contrast ratio minimum for chart elements against their background; 4.5:1 for text in charts. Never use color as the sole differentiator — pair with shape, pattern, or direct label. Provide a text alternative for every chart (aria-label with key finding, or companion data table). Interactive charts must be keyboard-navigable.

17. Responsive, not just resized

Charts must have a responsive strategy — fluid (percentage width + viewBox), adaptive (breakpoint-based layout changes), or hybrid. At narrow viewports, change chart type or layout (horizontal bars for categories, reduced tick density, abbreviated labels), don't just shrink.

18. Animate to explain, not to decorate

Transitions for data changes (sorting, filtering, time progression) are good — they help the viewer track transformations. Gratuitous entrance animations, bouncing, and decorative motion are chartjunk. Duration: 200–500ms, ease-out. Always respect prefers-reduced-motion.

19. Dark mode as first-class citizen

Design both light and dark palettes intentionally. Never invert colors. Reduce saturation in dark mode (bright colors "vibrate" on dark backgrounds). Respect prefers-color-scheme. Use semantic color tokens (--tufte-bg, --tufte-text, --tufte-series-default) so charts adapt automatically.

20. Titles assert findings

The chart title states the key insight, not the axis description. "Revenue Surged 23% in Q3" not "Revenue by Quarter, 2024". The subtitle can provide context ("vs. prior year, USD millions"). If the data has no clear finding, the chart may not be needed (see rule 22).

21. Format numbers for humans

Abbreviate large numbers: $1.2M not $1,200,000. Use thousand separators for mid-range numbers (12,450 not 12450). Match decimal precision to significance (don't show $4.2391M when $4.2M suffices). Right-align numbers in tables. Use consistent units and state them once (in the axis label or title), not on every data point.

22. Don't chart what a sentence can say

If the data is 1–2 numbers, write a sentence with inline context ("Revenue was $4.2M, up 23% from Q2"). If the data is a simple ranking of 3–5 items, consider a table. Charts earn their space by revealing patterns, trends, or distributions that text and tables cannot. A chart of two bars is almost always worse than a sentence.


Show full SKILL.md (681 more words)Show less

Library quick reference

The universal rules above are sufficient for most charts. For complete code examples and library-specific helpers, read the appropriate rule file from the rules/ directory in this skill's folder. Only read ONE rule file per task.

LibraryRule file to readEssential config (apply even without reading the file)
Rechartsrules/recharts.md<CartesianGrid stroke="none" />, remove <Legend />, <YAxis axisLine={false} tickLine={false} />, <Line dot={false} strokeWidth={1.5} />
EChartsrules/echarts.mdsplitLine: { show: false }, legend: { show: false }, grid: { show: false }, use endLabel on series
Chart.jsrules/chartjs.mdgrid: { display: false }, border: { display: false }, plugins.legend.display: false, use chartjs-plugin-datalabels
matplotlibrules/matplotlib.mdspines['top'].set_visible(False), spines['right'].set_visible(False), spines['bottom'].set_bounds(min, max), font.family: serif
Plotlyrules/plotly.mdshowgrid=False, showlegend=False, plot_bgcolor='#fffff8', zeroline=False
D3/SVG/HTMLrules/svg-html.md.domain { display: none }, no <rect> backgrounds, stroke-opacity: 0.1 for any gridlines

Chart type guidance

TypeKey settings
Line1.5–2px stroke, dot={false} unless <7 points (then r=2), direct label at rightmost point
BarPrefer horizontal for categories, sort by value descending, direct value labels, #7a7a7a default fill
ScatterGray dots #999 r=3, highlight key cluster/outlier with accent, regression line if meaningful (dashed, thin)
Time seriesLabel events on chart ("Recession", "Launch"), range-frame x-axis, YoY via opacity (current solid, prior 30%)
Small multiplesSame scale ALL panels, shared axis labels (x on bottom row, y on left column), no panel borders
Sparklines~80x20px, no axes/labels/gridlines, min/max dots r=1.5, embed inline in text or table cells
Data tablesNo zebra striping, whitespace + thin rules every 3–5 rows, right-align numbers, font-feature-settings: 'onum' 1
SlopegraphBefore/after categories, label both endpoints (value + name), gray default + highlight key slopes
AreaPrefer lines. If area: fillOpacity 0.03–0.08, no gradient, direct labels at endpoints
Stacked barAvoid — use small multiples instead. If forced: sort by total, direct labels per segment, max 4 segments
HeatmapSequential or diverging palette only, value labels in cells, companion data table for accessibility

For small multiples, sparklines, and slopegraph implementation patterns, see rules/small-multiples-sparklines.md.


Color quick reference

TokenLightDark
Background#fffff8#151515
Text#111#ddd
Text secondary#666#999
Axis/rule#ccc#444
Grid (if used)#eee (8-12% opacity)#333
Default series#666#999
Highlight#e41a1c#fc8d62

Categorical (max 4): #4e79a7 steel blue · #f28e2b tangerine · #e15759 coral · #76b7b2 sage

Font stacks in rule 10. For full palettes (sequential, diverging), font loading, and old-style figures, see rules/typography-and-color.md.


Anti-pattern detection

When reviewing existing chart code, check for: legends (→ direct labels), pie charts (→ horizontal bars), 3D effects (→ flat 2D), dual y-axes (→ small multiples), heavy gridlines (→ remove or 0.1 opacity), rainbow palettes (→ gray + accent), gauge widgets (→ number + sparkline), gradient fills (→ solid color), rotated labels (→ flip axes or abbreviate), pure white/black backgrounds (→ #fffff8/#151515), hover-only information (→ tap/focus fallback), missing text alternatives (→ aria-label), color-only encoding (→ add shape/pattern).

For the full table with per-library detection patterns and one-liner fixes, see rules/anti-patterns.md.


Validation checklist

Before presenting any chart, verify:

  • No top or right borders/spines
  • No Legend component — series labeled directly on the chart
  • Gridlines removed or horizontal-only at opacity <= 0.12
  • Aspect ratio approximately 1.5:1
  • Background is #fffff8 (light) or #151515 (dark), not pure white/black
  • Serif font for data labels and titles
  • Default series color is gray (#666); color used only for emphasis
  • No 3D effects, no pie chart (unless explicitly requested)
  • Axis lines span only the data range (range-frame)
  • Notable data features annotated directly on chart
  • Comparison context present (reference line, band, or second series)
  • Tooltips are plain text with no decorative styling
  • Interactive elements have tap/click/focus alternatives (no hover-only)
  • Contrast ratios meet 3:1 (elements) / 4.5:1 (text) minimums
  • Chart has a text alternative (aria-label, description, or data table)
  • Animations respect prefers-reduced-motion
  • Charts render usably at 320px and 1440px+ widths
  • Title states the finding, not the axis description
  • Numbers are formatted for readability (abbreviations, separators, consistent precision)
  • A chart is warranted — the data couldn't be communicated as a sentence or table

Additional resources

Library rules (read ONE per task): rules/recharts.md, rules/echarts.md, rules/chartjs.md, rules/matplotlib.md, rules/plotly.md, rules/svg-html.md — complete code examples, helpers, and theme registrations.

Cross-cutting (read when specifically needed):

  • rules/interactive-and-accessible.md — progressive disclosure, WCAG, responsive, animation, dark mode
  • rules/typography-and-color.md — font loading, full palette tables, old-style figures
  • rules/anti-patterns.md — per-library detection heuristics and fixes
  • rules/small-multiples-sparklines.md — layout patterns for small multiples, sparklines, slopegraphs

Working examples in examples/ — one per library, plus an inline SVG sparkline.

© caylent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 33 other files in the repository root of caylent/tufte-data-viz.

  • SKILL.md
  • LICENSE
  • README.md
  • _docs/before-after-animated.gif
  • _docs/before-after.png
  • _docs/generate_showcase.py
  • _docs/small-multiples.png
  • _docs/tufte-accessible-scatter.png
  • _docs/tufte-bar-chart.png
  • _docs/tufte-dark-mode.png
  • _docs/tufte-data-table.png
  • _docs/tufte-light-dark.png
  • _docs/tufte-line-chart.png
  • _docs/tufte-slopegraph.png
  • _docs/tufte-sparklines.png
  • docs/index.html
  • examples/chartjs-tufte-plugin.js
  • examples/echarts-tufte-theme.ts
  • … and 16 more

Open the folder on GitHubat commit ae7ca0d

Compare with similar skills

Tufte Data Viz next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Scientific Visualizationmims-harvard/OptimusKG14719 repos~6.3kAutomated safety check: PassMIT
SeabornzLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
Ieee Figure TableCloudWave818/ieee-skills359—~1kAutomated safety check: PassMIT
CJK Font Setup for Plotsxjtulyc/MedgeClaw6171 repos~1.3kAutomated safety check: PassNone

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Questions about Tufte Data Viz

What does Tufte Data Viz do?

A skill your agent uses when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization. Tufte Data Viz is an agent skill from caylent/tufte-data-viz. Use when creating, reviewing, or styling charts, graphs, dashboards, sparklines, or any data visualization.

When should I use Tufte Data Viz?

Tufte Data Viz fits situations like: any data visualization; tasks that involve Data visualization; tasks that involve Accessibility.

How do I install Tufte Data Viz in Claude Code?

Run `npx skills add caylent/tufte-data-viz --skill tufte-data-viz -a claude-code`. Or copy the skill folder (the caylent/tufte-data-viz repository) into .claude/skills/tufte-data-viz in your project. Claude Code loads it when a task matches its description.

How do I install Tufte Data Viz in Codex?

Run `npx skills add caylent/tufte-data-viz --skill tufte-data-viz -a codex`. Or copy the skill folder (the caylent/tufte-data-viz repository) into .agents/skills/tufte-data-viz in your project. Codex loads it when a task matches its description.

Can I use Tufte Data Viz in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add caylent/tufte-data-viz --skill tufte-data-viz -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tufte-data-viz, .gemini/skills/tufte-data-viz, .github/skills/tufte-data-viz and .opencode/skills/tufte-data-viz in your project.

What does Tufte Data Viz need to run?

Going by SKILL.md and its folder, Tufte Data Viz needs Python, JavaScript and TypeScript for the scripts in its folder. Our summary lists: Python 3; Node.js. Its frontmatter pre-approves these tools: Read, Glob, Grep.

Does Tufte Data Viz access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tufte Data Viz safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tufte Data Viz use?

Tufte Data Viz is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tufte Data Viz use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tufte Data Viz?

Skills that share tags, products or a category with Tufte Data Viz: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Scientific Visualization (mims-harvard/OptimusKG, 147 stars), Seaborn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Ieee Figure Table (CloudWave818/ieee-skills, 359 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tufte Data Viz?

caylent (a GitHub organization) maintains it in caylent/tufte-data-viz, which has 223 GitHub stars. The repository was last updated on February 19, 2026.

Source: caylent/tufte-data-viz on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.