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Chartwright: Turning Tufte's Book Into an AI Skill

Source: GitHub — gnurio/tufte-vdqi-plugin
Concept: Encoding a full book's methodology into composable AI skills

TL;DR

The tufte-vdqi-plugin (called "Chartwright") demonstrates a powerful pattern: distilling Edward Tufte's The Visual Display of Quantitative Information into 10 composable AI skills that encode process rather than content. Each skill (like calculate-lie-factor, erase-non-data-ink, standardize-monetary-units) corresponds to a Tufte principle expressed as a repeatable workflow. An orchestrator skill routes chart challenges to the correct sub-skills — turning a book's accumulated wisdom into executable AI procedures.

The Core Idea

The key insight is that skill files encode process, not content — they function like method calls in programming, where markdown is the language and human judgment is the runtime. The same /investigate skill applied to entirely different domains produces radically different but equally valid outputs, because the skill describes how to think, not what to think.

How It Implements Tufte's Book

The plugin breaks Tufte's principles into actionable steps:

  1. orchestrate-tufte-vdqi — Routes chart challenges to the correct sub-skill
  2. assess-graphical-excellence — Scores a graphic against Tufte's nine criteria
  3. calculate-lie-factor — Measures ratio of visual distortion to actual data change
  4. erase-non-data-ink — Strips decoration, borders, and fills that carry no information
  5. erase-redundant-data-ink — Removes repeated encodings of the same datum
  6. standardize-monetary-units — Converts nominal dollars to inflation-adjusted values
  7. generate-range-frames — Replaces rectangular plot frames with data-spanning range-frames
  8. integrate-text-and-graphic — Places labels and captions directly in the plotting field
  9. construct-small-multiples — Composes paneled graphics sharing a design
  10. render-tufte-chart — Renders data as complete Tufte-styled HTML with inline CSS

The Deeper Pattern

This plugin is a case study in a broader movement: taking authoritative reference works and encoding them as AI-accessible procedural knowledge. Instead of having an AI read a book and try to recall it, the book's methodology is decomposed into deterministic, testable components that produce consistent, high-quality outputs. The skill format itself becomes the distribution mechanism for expert knowledge — accessible to any AI agent that loads it.