Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

Apache-2.0Auto-check passedData & Analytics

Install Data Artist

skills CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -a claude-code

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

GitHub CLI
$ gh skill install foryourhealth111-pixel/Vibe-Skills data-artist --agent claude-code

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

Manual copy
$ git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/bundled/skills/data-artist .claude/skills/data-artist && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

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

Facts

Skill name
data-artist
GitHub stars
3.6k
Token cost
~1.8k tokens
SKILL.md length
541 words
Files
1
Skills in repo
81
Repo updated
First seen
Licence
Apache-2.0

At a glance

Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

  • Works in 5 steps: Mathematical Foundations… → Color Design (@geepers_datavis_color) → Narrative Design (@geepers_datavis_story) → …
  • Tasks that involve Data visualization
  • SKILL.md covers The "Data is Beautiful"…, Visualization Domains, Execution Strategy and Output Format, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Data Artist is an agent skill from foryourhealth111-pixel/Vibe-Skills. Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data visualization. The repository describes itself as: Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Data visualization

Example prompts

  • “Data is Beautiful”
  • “/data-artist”

Workflow steps

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

  1. Mathematical Foundations (@geepers_datavis_math)
  2. Color Design (@geepers_datavis_color)
  3. Narrative Design (@geepers_datavis_story)
  4. Technical Implementation (@geepers_datavis_viz)
  5. Data Integrity (@geepers_datavis_data)

What it can do on your machine

Read from SKILL.md and the folder at commit ddcaa2a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are css).

    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

Data Artist loads about 1.8k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 541 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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 foryourhealth111-pixel/Vibe-Skills at commit ddcaa2a, republished under its Apache-2.0 licence (© foryourhealth111-pixel). 541 words, ~1,819 tokens.

Download SKILL.mdSave it as .claude/skills/data-artist/SKILL.md (or your agent's skills folder).
name
data-artist
description
Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.
version
1.0.0

Data Artist

You are creating a work of data art. This skill brings together mathematical elegance, emotional resonance, narrative design, and technical excellence to transform raw data into something beautiful that tells a story and moves the viewer.

The "Data is Beautiful" Philosophy

Core Principles
  1. Life is Beautiful - Data visualization should reveal the wonder in information
  2. Mathematical Elegance - Perceptually accurate encodings, thoughtful scales
  3. Emotional Resonance - Create moments of awe, reflection, insight
  4. Swiss Minimalism - Clean geometry, purposeful color, no chartjunk
  5. Narrative Journey - Guide the viewer through a story
What Makes Data Beautiful
  • Clarity - The data speaks clearly without distortion
  • Proportion - Visual weight matches data importance
  • Rhythm - Patterns emerge naturally from the encoding
  • Surprise - Reveals insights not obvious in raw numbers
  • Humanity - Connects data to human experience

Visualization Domains

1. Mathematical Foundations (@geepers_datavis_math)

Scale Selection:

  • Linear for comparison
  • Log for orders of magnitude
  • Sqrt for area perception
  • Time scales for temporal data

Visual Encoding:

  • Position (most accurate)
  • Length/height (good)
  • Angle/slope (moderate)
  • Area (requires sqrt scaling)
  • Color intensity (least precise)

Perceptual Accuracy:

  • Ensure encodings don't mislead
  • Account for human perception biases
  • Use perceptually uniform color scales
2. Color Design (@geepers_datavis_color)

Palette Types:

  • Sequential: Low → High (single hue)
  • Diverging: Negative ↔ Neutral ↔ Positive
  • Categorical: Distinct groups (max 7-9)

Color Principles:

  • Perceptual uniformity (Lab/HCL color space)
  • Colorblind accessibility (avoid red-green only)
  • Emotional resonance (warm/cool, muted/vibrant)
  • Cultural considerations

Signature Palettes:

css
/* Elegant Sequential */
--seq-1: #F7FBFF;
--seq-2: #DEEBF7;
--seq-3: #9ECAE1;
--seq-4: #4292C6;
--seq-5: #084594;

/* Thoughtful Diverging */
--div-neg: #B2182B;
--div-neutral: #F7F7F7;
--div-pos: #2166AC;

/* Accessible Categorical */
--cat-1: #1B9E77;
--cat-2: #D95F02;
--cat-3: #7570B3;
--cat-4: #E7298A;
--cat-5: #66A61E;
3. Narrative Design (@geepers_datavis_story)

Story Arc:

  1. Hook - What draws the viewer in?
  2. Context - Why does this matter?
  3. Journey - Guide through the data
  4. Insight - The "aha" moment
  5. Reflection - What does it mean?

Emotional Calibration:

  • What emotion should viewers feel?
  • How do we honor the subject matter?
  • Where are moments of wonder/pause/reflection?

Metaphor Selection:

  • Timelines → Rivers, journeys
  • Networks → Galaxies, ecosystems
  • Proportions → Physical objects, scale comparisons
  • Change → Growth, transformation
4. Technical Implementation (@geepers_datavis_viz)

Tools:

  • D3.js for custom visualizations
  • Chart.js for standard charts
  • SVG for crisp, scalable graphics
  • Canvas for high-performance rendering

Interaction Patterns:

  • Hover for details
  • Click for drill-down
  • Drag for exploration
  • Scroll for revelation

Responsive Design:

  • Mobile-first
  • Touch-friendly interactions
  • Graceful degradation
Show full SKILL.md (199 more words)Show less
5. Data Integrity (@geepers_datavis_data)

Source Verification:

  • Cite authoritative sources
  • Document methodology
  • Note limitations/caveats

Data Pipeline:

  • Clean, validated data
  • Reproducible transformations
  • Cached appropriately

Execution Strategy

For a new visualization, launch in PARALLEL:

1. @geepers_datavis_story - Define narrative arc and emotional journey
2. @geepers_datavis_math - Design encodings and scales
3. @geepers_datavis_color - Develop color palette
4. @geepers_datavis_data - Validate and prepare data

Then:

5. @geepers_datavis_viz - Technical implementation

Output Format

🎨 DATA ARTIST BRIEF

Visualization: {title}
Data Source: {source}
Story: {one-line narrative}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
           NARRATIVE DESIGN
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Central Question: {what we're answering}

Emotional Journey:
Entry → Curiosity
Middle → {surprise/concern/wonder}
Exit → {reflection/action/understanding}

Metaphor: {chosen metaphor and rationale}

Key Insight: {the "aha" moment}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
       MATHEMATICAL APPROACH
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Visualization Type: {bar/line/scatter/custom}

Encodings:
- X-axis: {variable} → {encoding}
- Y-axis: {variable} → {encoding}
- Color: {variable} → {encoding}
- Size: {variable} → {encoding}

Scale Choices:
- {scale type with rationale}

Perceptual Considerations:
- {any adjustments needed}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
          COLOR PALETTE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Palette Type: {sequential/diverging/categorical}

Colors:
🔵 Primary: #2563EB - {meaning}
⚪ Neutral: #F8FAFC - {purpose}
🔴 Accent: #DC2626 - {usage}

Accessibility:
✓ Colorblind safe (simulated)
✓ Contrast ratio > 4.5:1

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
         IMPLEMENTATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Technology: {D3.js/Chart.js/SVG}

Key Components:
1. {component} - {purpose}
2. {component} - {purpose}

Interactions:
- Hover: {behavior}
- Click: {behavior}

Animation:
- Entry: {animation description}
- Update: {transition behavior}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
          BEAUTY SCORE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Mathematical Elegance: ★★★★☆
Color Harmony: ★★★★★
Narrative Clarity: ★★★☆☆
Technical Polish: ★★★★☆
Emotional Impact: ★★★★☆

Overall: "Data is Beautiful" certified ✨

Visualization Types & When to Use

TypeBest ForAvoid When
Bar ChartComparing categoriesToo many categories (>12)
Line ChartTrends over timeDiscrete, unordered data
Scatter PlotRelationshipsOverplotting (use density)
Pie ChartPart-of-whole (few)>5 segments
TreemapHierarchical proportionsDeep hierarchies
Force NetworkRelationships>100 nodes without clustering
ChoroplethGeographic patternsUnequal area regions
TimelineTemporal eventsToo many overlapping events

Anti-Patterns to Avoid

  • ❌ Chartjunk (unnecessary decoration)
  • ❌ 3D effects that distort perception
  • ❌ Truncated axes that exaggerate
  • ❌ Rainbow color scales (not perceptually uniform)
  • ❌ Dual Y-axes (confusing comparisons)
  • ❌ Pie charts for comparison
  • ❌ Too much data (know when to aggregate)

Inspiration Sources

  • r/dataisbeautiful - Community examples
  • Information is Beautiful - David McCandless
  • Flowing Data - Nathan Yau
  • NYT Graphics - Journalism excellence
  • Observable - D3 community

Key Principles

  1. Data first - Let the data guide design decisions
  2. Less is more - Remove until it breaks
  3. Perception matters - Account for how humans see
  4. Tell a story - Every visualization has a narrative
  5. Respect the subject - Honor what the data represents

© foryourhealth111-pixel, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in bundled/skills/data-artist of foryourhealth111-pixel/Vibe-Skills.

Open the folder on GitHubat commit ddcaa2a

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Questions about Data Artist

What does Data Artist do?

Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic. Data Artist is an agent skill from foryourhealth111-pixel/Vibe-Skills. Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

When should I use Data Artist?

Data Artist fits situations like: tasks that involve Data visualization.

How do I install Data Artist in Claude Code?

Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -a claude-code`. Or copy the skill folder (bundled/skills/data-artist in foryourhealth111-pixel/Vibe-Skills) into .claude/skills/data-artist in your project. Claude Code loads it when a task matches its description.

How do I install Data Artist in Codex?

Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -a codex`. Or copy the skill folder (bundled/skills/data-artist in foryourhealth111-pixel/Vibe-Skills) into .agents/skills/data-artist in your project. Codex loads it when a task matches its description.

Can I use Data Artist 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 foryourhealth111-pixel/Vibe-Skills --skill data-artist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-artist, .gemini/skills/data-artist, .github/skills/data-artist and .opencode/skills/data-artist in your project.

What does Data Artist need to run?

SKILL.md names no scripts, command-line tools or credentials: Data Artist is instructions for the agent only.

Does Data Artist 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 Data Artist 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 Data Artist use?

Data Artist is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Artist use?

About 1.8k tokens (SKILL.md is roughly 7.3k 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 Data Artist?

Skills that share tags, products or a category with Data Artist: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Chart Visualization (bytedance/deer-flow, 84k stars), Scientific Visualization (mims-harvard/OptimusKG, 147 stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Artist?

foryourhealth111-pixel (a GitHub user) maintains it in foryourhealth111-pixel/Vibe-Skills, which has 3,627 GitHub stars. The repository holds 81 skills in this directory. The repository was last updated on August 31, 2026.

Source: foryourhealth111-pixel/Vibe-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.