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tufte-viz

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Use when the user is designing, critiquing, or choosing data visualizations: charts, graphs, dashboards with visual encodings, graphical integrity, misleading axes, lie factor, chartjunk, data-ink ratio, sparklines, small multiples, dense displays, or high-density analytical graphics. Do not load for generic product dashboard UX, report writing, BI pipeline work, SQL metric debugging, or visual design that is not about data representation.

One new superpower.

Run this command in your project to choose an agent and install the skill.

npx skillycli add https://github.com/bryanzk/MyCodexEnv --skill tufte-vizView original source

BEFORE YOU INSTALL

Understand the trade-offs.

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The full skill.

Original instructions from the publisher’s SKILL.md

# Tufte Visualization Ideation

Apply Edward Tufte's principles to design clear, honest, high-density data visualizations.

## Workflow

### For new visualizations:

1. **Clarify the data story**
   - What comparisons matter?
   - What's the key insight to communicate?
   - Who's the audience?

2. **Select approach** using Tufte principles:
   - High comparison need → Small multiples
   - Dense data → Consider data tables, sparklines
   - Time-series → Line charts with minimal grid
   - Part-to-whole → Avoid pie charts; prefer bar/table

3. **Design with data-ink in mind**
   - Start minimal, add only what's necessary
   - Every element must earn its ink
   - Default to grayscale; use color purposefully

4. **Apply the eraser test before shipping**
   - For every element (label, tick, gridline, border, annotation): can it be erased without losing information that's not already conveyed elsewhere?
   - Watch for duplicate encodings: numeric labels next to a value already marked by a tick; legends duplicating direct labels; per-panel scale annotations duplicating a shared-scale caption.
   - If two elements compete for the same job, keep the visual one and drop the textual one (or vice versa) — not both.

5. **Apply the Tufte test** (see references/tufte-principles.md)

### For critiquing visualizations:

1. **Check graphical integrity**
   - Calculate lie factor if proportions seem off
   - Verify baselines and scales
   - Look for 3D distortion

2. **Identify chartjunk**
   - Decorative elements
   - Heavy grids
   - Unnecessary 3D effects
   - Moiré patterns

3. **Evaluate data-ink ratio**
   - What can be erased?
   - What's redundant?

4. **Suggest improvements** with specific before/after recommendations

## Key Principles Reference

- `references/tufte-principles.md` — core principles from *Visual Display of Quantitative Information*: lie factor, data-ink, chartjunk, small multiples, integrity.
- `references/analytical-design.md` — extensions from *Envisioning Information*, *Visual Explanations*, and *Beautiful Evidence*: the 6 principles of analytical design, sparklines, layering & separation, micro/macro, range-frames, causality, confections. Load when designing dashboards, dense displays, sparklines, or explanatory graphics.

**Quick checklist:**
- [ ] Lie Factor ≈ 1.0 (no visual distortion)
- [ ] Maximum data-ink ratio
- [ ] Zero chartjunk
- [ ] Clear labeling
- [ ] Answers "compared to what?"
- [ ] Shows causality or mechanism where relevant
- [ ] Multivariate (not over-reduced)
- [ ] Words, numbers, images integrated — not segregated
- [ ] Reveals multiple levels of detail (micro + macro)
- [ ] Layering: primary data dominates, secondary recedes
- [ ] Appropriate data density