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From tufte-vdqi
Routes data-visualization requests to the right Tufte skill: assess a graphic, produce a new chart, or fix a cluttered one. Invoke when unsure which Tufte skill to use.
npx claudepluginhub gnurio/tufte-vdqi-plugin --plugin tufte-vdqiHow this skill is triggered — by the user, by Claude, or both
Slash command
/tufte-vdqi:orchestrate-tufte-vdqiThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are the router. Read the request, decide the intent, and invoke the right
Creates and critiques data visualizations using Edward Tufte's principles: high data-ink ratio, direct labels, range frames, and small multiples. Covers Recharts, Plotly, matplotlib, Chart.js, ECharts, D3, SVG, and HTML.
Scores a data graphic against Tufte's nine criteria, computes lie factor, identifies chartjunk species, and returns prioritized fixes with specific remedies and exemplars.
Guides chart selection for data relationships and generates Python visualization code with matplotlib, seaborn using professional styles, palettes, and accessibility principles.
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You are the router. Read the request, decide the intent, and invoke the right skill. You are doing this by understanding, not by matching keywords — the previous version was a brittle keyword function and it is gone.
assess-graphical-excellence — evaluate an existing graphic against the nine
criteria, name the chartjunk species present (moiré / dreaded grid / duck /
decoration), compute the lie factor and compare it to VDQI's catalogue
(NYT MPG 14.8, TIME barrel 59.4, etc.), check whether the data deserves a
different genre (table for ≤20 numbers, small multiples for many series,
range frame instead of bordered scatter), and emit prioritised fixes tagged
with remedy (B1–B7), genre to switch to (C1–C10), anti-pattern resemblance,
and exemplar to emulate. The default when intent is unclear.render-tufte-chart — produce an actual chart file. Ships per-genre scripts:
render_line_svg.py (time-series C10), small_multiples.py (C5),
quartile_plot.py (C1), range_frame.py (C2, optional C3 dot-dash
marginals), plus wrap_html.py for a tufte-css HTML page. The only skill
that outputs a chart.references/tufte-principles.md (mirrored into both skills) — the canonical
Tufte knowledge, source-grounded to VDQI with page citations: Part A nine
criteria with numeric anchors, Part B seven remedies, Part C ten chart
genres with construction recipes, Part D chartjunk taxonomy, Part E
named-failure catalogue (13 dissected real-world graphics with metrics),
Part F named-exemplar catalogue (14 praised graphics with copyable moves),
Part G compact quantitative defaults.assess-graphical-excellence.render-tufte-chart. If the data is
currency across multiple years, deflate it first (remedy B7) before rendering.assess-graphical-excellence to diagnose and list remedies, then
render-tufte-chart to rebuild honoring them. The assessment's remedy tags
(B1–B7) are the instructions render follows.assess-graphical-excellence.Earlier this toolkit had ten skills. Benchmarking showed most encoded a single Tufte idea the model already applies, so separate routing targets only added latency and risk. Assessment and rendering are the two actions that genuinely benefit from a skill. The depth that makes the skills non-generic lives in the principles reference — specifically Parts C–F, the source-grounded genre playbook, chartjunk taxonomy, named-failure library, and named-exemplar library quoted from VDQI by page. Without those, the skills regress to "Claude paraphrasing Tufte from memory"; with them, the model has Tufte's specific vocabulary and worked numbers to reach for.