What problem does it solve? UI text that users cannot quickly read — ambiguous characters like I/1/l, low-contrast labels, hairline weights at small sizes — causes misread codes, failed forms, and accessibility failures. This Skill provides a systematic framework for evaluating and fixing the legibility of individual characters and short text in interfaces. ## Core Features & Use Cases - Five-input legibility model: Evaluates typeface design, size, weight, contrast, and spacing together, since a legible typeface can still fail under poor rendering conditions. - Diagnostic symptoms: Identifies legibility problems through observable signals like squinting, mistyped confirmation codes, excessive zoom usage, and WCAG contrast audit failures. - Worked patterns: Covers login forms, confirmation codes, display vs. body type pairing, button contrast, and code listings with concrete fixes. - Use Case: A user repeatedly mistypes a 6-character confirmation code because the typeface renders O and 0 identically. The Skill recommends switching to a disambiguation-focused typeface like JetBrains Mono or removing ambiguous characters from the code alphabet. ## Quick Start Audit the typography of my login form and confirmation code display for legibility problems and recommend fixes.