Claude Code Skills for Academic Research

Reusable Claude Code skills for paper review, code review, and computational reproducibility audits

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Tufte Visualization

Ideate and critique data visualisations using Edward Tufte’s principles, drawn from The Visual Display of Quantitative Information (1983), Envisioning Information (1990), Visual Explanations (1997), and Beautiful Evidence (2006). Use it when designing a new figure, critiquing an existing one, checking a chart for graphical integrity, or choosing between visualisation approaches.

Most useful on paper figures before submission, on referee reports where the exhibits are the weak point, and on slide decks.

Usage

/tufte-viz

Point it at a figure, at the script that generates one, or at a description of the data you want to show.

Install

This skill has two reference files alongside the main one, so it needs a references/ subfolder:

~/.claude/skills/tufte-viz/
├── SKILL.md
└── references/
    ├── tufte-principles.md
    └── analytical-design.md

Everything below the checklist on this page is the contents of those two reference files.

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 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

Both reference files are reproduced in full at the bottom of this page:

Quick checklist:

Reference: core principles

Contents of references/tufte-principles.md.

1. Graphical Excellence

Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.

Core qualities:

Questions to ask:

2. Graphical Integrity

Graphics must tell the truth about the data.

The Lie Factor:

Lie Factor = Size of effect shown in graphic / Size of effect in data

Six principles of graphical integrity:

  1. Representation of numbers should be directly proportional to quantities represented
  2. Clear, detailed, thorough labeling defeats distortion
  3. Show data variation, not design variation
  4. In time-series displays, standardize money (deflate) and use consistent baselines
  5. Dimensions of graphics should not exceed dimensions of data
  6. Graphics must not quote data out of context

Common violations:

3. Data-Ink Ratio

The data-ink ratio is the proportion of a graphic’s ink devoted to the non-redundant display of data-information.

Data-Ink Ratio = Data-ink / Total ink used in graphic

Maximize the data-ink ratio within reason:

  1. Erase non-data-ink (decoration, heavy grids, boxes)
  2. Erase redundant data-ink (3D when 2D suffices)
  3. Revise and edit

Non-data-ink to eliminate:

The eraser test: If you can erase something without losing data information, erase it.

4. Chartjunk

Chartjunk is the interior decoration of graphics that does not convey information.

Three categories of chartjunk:

A. Unintentional optical art (moiré vibration)

B. The Grid

C. The Duck (self-promoting graphics)

Chartjunk indicators:

5. Small Multiples

Small multiples are series of graphics showing the same combination of variables, indexed by changes in another variable.

Characteristics:

When to use:

Design guidelines:

6. Data Density & Information Resolution

Data density = numbers plotted per unit area

High data density is a sign of graphical quality. Maps and time-series can achieve thousands of numbers per square inch.

Shrink principle: Graphics can often be reduced significantly while maintaining readability and gaining impact. Consider:

Resolution thinking:

7. Multifunctioning Graphical Elements

Every graphical element should serve multiple purposes when possible.

Data measures that can serve as:

Examples:

8. Aesthetics and Technique

Balance complexity and simplicity:

Visual hierarchy:

Color use:

Typography:

Quick Reference: The Tufte Test

For any visualization, ask:

  1. Data-Ink: Can I erase any element without losing data? (Erase it)
  2. Integrity: Does the visual effect match the data effect? (Lie Factor ≈ 1)
  3. Chartjunk: Does any element exist for decoration only? (Remove it)
  4. Excellence: Does it reveal the data at multiple levels? (Broad + detailed)
  5. Comparison: Can the viewer easily compare data elements? (Enable it)
  6. Density: Could this show more data in the same space? (Condense)
  7. Context: Is all necessary context provided? (Labels, sources, scales)

Reference: analytical design, sparklines and layering

Contents of references/analytical-design.md.

Extends tufte-principles.md with material from Envisioning Information (1990), Visual Explanations (1997), and Beautiful Evidence (2006).

1. The Six Principles of Analytical Design

From Beautiful Evidence. The most actionable framework Tufte produced — applies to any analytical presentation, not just charts.

  1. Show comparisons, contrasts, differences The fundamental analytical act. Every display should answer “compared to what?”

  2. Show causality, mechanism, structure, explanation Move beyond description. What’s the why behind the pattern?

  3. Show multivariate data — more than 1 or 2 variables Real problems are multivariate. Reducing to a single variable hides interactions.

  4. Completely integrate words, numbers, images, diagrams Don’t segregate by mode. Labels next to the data they describe; equations next to the curves they generate.

  5. Thoroughly describe the evidence Provenance, authorship, scales, sources, measurements. Documentation enables trust.

  6. Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content. No amount of design fixes weak evidence. Content is paramount.

Use in critique: walk through all six. The lowest-scoring principle is usually the biggest improvement opportunity.

2. Sparklines

Word-sized, data-intense graphics. Tufte’s signature Beautiful Evidence invention.

Defining properties:

Design rules:

When to use:

When not to use:

3. Layering and Separation

From Envisioning Information. The most useful concept for dense displays.

The principle: Visually distinct elements can coexist in the same space if they’re layered — separated by value, weight, hue, or transparency rather than spatial isolation.

Techniques:

Test: squint at the graphic. The most important data should remain visible; chartjunk should disappear first.

4. Micro/Macro Design

Distinct from raw data density. A micro/macro graphic reveals different stories at different viewing distances.

Canonical examples:

Design implication: don’t choose between overview and detail — show both simultaneously by layering.

5. Escaping Flatland

The 2D page/screen is inherently flat; good information design adds dimensions without 3D gimmicks.

Dimensions you can add on flat media:

Anti-pattern: 3D bar charts, pie charts with depth, isometric projections that distort proportions. These add visual dimension without adding information dimension — pure chartjunk.

6. Range-Frame and Dot-Dash Plot

Tufte’s signature reinventions of standard chart elements. Direct applications of data-ink maximization.

Range-frame:

Dot-dash plot:

Pattern: every standard chart element (axis, tick, gridline) can be redesigned to carry data.

7. Confections, Parallelism, Narrative

From Visual Explanations.

Confections: assemblages of disparate visual elements (images, maps, text, diagrams) into a single explanatory composition. Examples: Minard’s Napoleon march, Snow’s cholera map, exploded technical illustrations. They work when each element serves the argument.

Parallelism: repetition of visual structure to enable comparison — small multiples are one form, but parallelism extends to side-by-side maps, before/after states, repeated annotation styles.

Narrative graphics of space and time: combine spatial and temporal dimensions in one frame. Minard’s Napoleon graphic encodes troop size, geography, direction, temperature, and time simultaneously.

8. Cause and Effect

From Visual Explanations. Causality is hard to visualize because it requires showing both the variables and the mechanism linking them.

Techniques:

Worked example: Challenger O-ring decision. The available data, plotted against temperature, showed catastrophic risk — but the engineers presented it in a way that hid the causal relationship. Tufte’s redesign makes the causality unavoidable.

Quick Reference: Extended Tufte Test

After applying the standard 7-question test in tufte-principles.md, add:

  1. Comparison: Does the graphic answer “compared to what?”
  2. Causality: Is the mechanism or explanation visible, not just the pattern?
  3. Multivariate: Are interactions among variables shown, or has the problem been over-reduced?
  4. Integration: Are words, numbers, and images interleaved — or segregated?
  5. Documentation: Can a stranger evaluate the evidence (sources, scales, authorship)?
  6. Layering: Do important elements dominate; do secondary elements recede?
  7. Micro/macro: Does the display reward both a glance and a close read?