Traction scoreGitHub stars can be faked, so popularity alone can be misleading. Traction Score looks for broader signs of recent attention, adoption, and active maintenance.
This skill translates Edward Tufte's principles—data-ink ratio, direct labeling, range-frame axes—into enforceable rules for chart code. Developers working with Recharts, ECharts, Chart.js, matplotlib, or SVG get consistent, accessible visualizations without fighting library defaults. It also catches anti-patterns: legends, 3D effects, dual y-axes, and pie charts that should be bars.
WHO IT'S FOR
Data Journalists and Reporters
creating honest, high-data-ink charts for articles
Data Scientists and Analysts
generating publication-quality plots from Python or R
Frontend Dashboard Developers
implementing clean, accessible charts in React or JS
Technical Writers and Documentation Engineers
producing clear, honest data visualizations for docs
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
Relevant baseline, prior period, target, or peer group
Use the tufte-data-viz skill to review the chart code at <path-to-chart-file>. Replace <path-to-chart-file> with the actual source-file path. Identify the key finding and comparison context the chart should communicate, assess whether its chart type fits the data, and recommend a small set of universal and library-specific improvements. Do not edit the file.
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