Enforce Tufte-inspired design principles for accessible web data visualizations.
npx skills add https://github.com/KyaniteLabs/tastecheck --skill data-viz-kyanitelabs
Please help me install this Agent Skill. Skill: data-viz Source: https://github.com/KyaniteLabs/tastecheck/tree/main/skills/data-viz Command: npx skills add https://github.com/KyaniteLabs/tastecheck --skill data-viz-kyanitelabs
This skill prevents the creation of misleading, cluttered, or inaccessible data visualizations by enforcing Tufte-inspired design principles and web-native accessibility standards.
Use the data-viz skill to generate a range-frame line chart and accessible table for the provided quarterly revenue dataset.
High-intent search queries and answers about installing and using this skill.
Lie-factor minimization in data visualization ensures graphical representations accurately reflect quantitative changes without distortion. It enforces honest encoding through techniques like zero-baseline bars and 1-D length mapping, preventing charts from visually exaggerating or misrepresenting the underlying data.
Making web charts accessible for screen readers requires pairing every visualization with a text-based takeaway and a screen-reader-friendly data table. This approach ensures accessibility parity by providing structured, navigable data alternatives to visual graphical encoding.
Applying Tufte design principles to a responsive dashboard chart involves maximizing data-ink ratio and enforcing graphical excellence. The process generates honest, responsive visualizations using design-system token integration, replacing cluttered defaults with clear, quantitative length mappings.
Yes, this approach works for converting misleading 3D pie charts into honest visualizations by replacing them with contrast-safe, responsive alternatives like range-frame line charts. It enforces strict requirements for zero-baseline encoding and 1-D position mapping.
You should not use standard charting libraries for quantitative data display when strict graphical excellence and accessibility parity are required. Default libraries often produce cluttered output lacking zero-baseline enforcement, lie-factor verification, and screen-reader-compatible data tables.
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