Website Font Checker for AI Coding Agents

Check a website's font families, weights, and roles, then give an AI coding agent a practical typography brief for implementation.

website font checker for ai coding agents

A useful website font checker for an AI coding agent should return more than one family name. It should identify each visible family, its role, the settings that affect the layout, and the details that still need verification. The result should be a typography brief the agent can implement and compare against the reference.

Identify type roles first

Classify each observed face by its visible job:

  • Interface or body text for navigation, controls, labels, and paragraphs
  • Display or editorial text for large headings and campaign statements
  • Monospace text for code, metadata, shortcuts, or technical values
  • Supporting faces used in limited parts of the hierarchy

The supplied Linear example lists Inter Variable, Berkeley Mono, and Tiempos Headline. Its notes describe Inter Variable as the primary sans serif, Berkeley Mono as a supporting monospace face, and Tiempos Headline as an occasional editorial display face. Use this as a captured example, not as a claim about every site.

Compare the hierarchy as well as the family names. Check whether heading, interface, and technical text are visibly distinct and whether those choices repeat consistently.

Captured pages

Fonts captured on linear.app

Give the agent implementation details

A practical report should include a family-by-role table, visible variants, representative weights, sizes, line heights, tracking, casing, fallback stacks, and the components where each face appears. A structure like this is useful:

Treat the values as fields to fill from the captured page, not as facts about the source. Confirm whether a variable font uses a weight range, whether italic is a real variant, and whether a font file is actually available. If the file cannot be confirmed, provide a fallback and label the family as unverified. Do not invent a source, license, or delivery promise.

Check the settings that change the look

Two pages can use the same family and still feel unrelated. Record the font size, weight, line height, letter spacing, casing, and text measure for important groups:

  • Navigation and button text
  • Main and supporting headings
  • Body and muted copy
  • Captions, metadata, and labels
  • Code or numeric values
  • Mobile text, if visible

Also check whether a fallback appears during capture. Separate observed behavior from recommendations. An agent can reproduce a tight heading line height while noting that an unconfirmed fallback may wrap differently.

Turn the result into a build brief

Ask for these outputs:

  1. A family-by-role table
  2. A CSS @font-face or import plan only for confirmed files
  3. A fallback stack for every role
  4. Typography tokens for size, weight, line height, and tracking
  5. A component map for headings, buttons, forms, navigation, and code
  6. A desktop and mobile verification checklist

Example tokens can provide a starting shape, but the values should come from the observed page or an explicitly marked recommendation:

Do not call visible usage the site's official brand system without authoritative documentation. A font inspection provides a strong implementation starting point while keeping uncertain files, licensing, and alternatives clearly marked.

Use this in your AI agent

> Check the captured website reference for visible typography. Return a role-based table covering family, variant, weight, size, line height, tracking, and where each face appears. Separate confirmed observations from recommendations, flag font files or licensing details that still need verification, and produce CSS typography tokens plus fallback stacks for review before implementation. > > Install Fudge for your AI agent

What should an AI coding agent receive in a website font report?

Give it four layers of information.

First, provide the family map: exact visible family name and role, such as body, display, mono, navigation, or labels. The Linear example records Inter Variable, Berkeley Mono, and Tiempos Headline as distinct choices instead of flattening them into one answer.

Second, provide visible settings: weight, size, line height, tracking, casing, and approximate text width for representative elements.

Third, state implementation status. Distinguish a confirmed font file from a family name inferred from appearance. Include a fallback stack and note whether variable weight or italic support needs checking.

Fourth, provide a component map for headings, paragraphs, buttons, navigation, metadata, and code. This lets the agent implement a small typography system instead of copying styles element by element.

Ask the agent to compare the result at the same viewport and revise wrapping, density, and hierarchy after the first pass.

How can I check whether the font match is visually accurate after implementation?

Compare the reference and implementation in this order:

  1. Hierarchy: is the main heading clearly stronger than supporting text?
  2. Wrapping: do headings and paragraphs break at similar words and occupy similar widths?
  3. Density: do navigation, forms, and cards feel equally compact or open?
  4. Weight: does regular text look too dark, too light, or artificially bold?
  5. Metrics: check line height, letter spacing, and baseline alignment around icons.
  6. Roles: confirm that mono or display faces appear only in their intended places.
  7. Fallbacks: check whether the fallback causes layout shifts.
  8. Responsive behavior: repeat the comparison at a narrow viewport.

If the result feels wrong, adjust one variable at a time. Start with the confirmed family and weight, then line height, width, size, and tracking. If the source font cannot be confirmed, document the closest visual alternative and label it as an approximation.

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