Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing.

Apache-2.0Auto-check passedFrontend & Design

Install Legibility

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill legibility -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins legibility --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/perception-and-hierarchy-principles/skills/legibility .claude/skills/legibility && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
legibility
GitHub stars
1.2k
Token cost
~3k tokens
SKILL.md length
1,668 words
Files
2 (incl. references)
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing.

  • Picking type for UI elements (buttons
  • SKILL.md covers Why legibility matters, The five inputs to legibility, Diagnosing legibility problems and Sub-skills in this cluster, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Evaluating typography against rendering conditions (small sizes

What it does

Legibility is an agent skill from hashgraph-online/awesome-codex-plugins. Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing. Use when picking type for UI elements (buttons, labels, navigation), evaluating typography against rendering conditions (small sizes, low-contrast displays, distance viewing), choosing display vs. body type, or auditing accessibility. Legibility is the precondition for reading; if a user can't quickly distinguish "I" from "1" or "O" from "0," everything…

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/lineage.md`).

It sits in Frontend & Design, covering Typography and Plain language and style rules. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Picking type for UI elements (buttons
  • Evaluating typography against rendering conditions (small sizes
  • Low-contrast displays
  • Distance viewing)

Example prompts

  • “t quickly distinguish”
  • “from”
  • “/legibility”

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Legibility loads about 3k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 156 tokens; SKILL.md has 1,668 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~156
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 1,668 words, ~3,000 tokens.

Download SKILL.mdSave it as .claude/skills/legibility/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
legibility
description
Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing. Use when picking type for UI elements (buttons, labels, navigation), evaluating typography against rendering conditions (small sizes, low-contrast displays, distance viewing), choosing display vs. body type, or auditing accessibility. Legibility is the precondition for reading; if a user can't quickly distinguish "I" from "1" or "O" from "0," everything downstream from text fails. Distinct from readability, which is about sustained reading of prose.

Legibility

Definition. Legibility is the visual clarity of individual characters and short text — the ease with which a reader can identify each letter, number, or symbol at a glance. It's determined by typeface design, size, weight, color contrast, and the spacing within and between characters. Legibility is the prerequisite for reading; without it, the rest of typography (hierarchy, voice, readability) is pointless because the user can't reliably identify what they're looking at.

The classic test of legibility: can the reader distinguish similar characters quickly under the actual viewing conditions? "I" and "1" and "l" — three different characters that in many typefaces look nearly identical. "O" and "0." "rn" and "m." "5" and "S." "8" and "B." A typeface that makes these distinctions clear is legible; one that doesn't, isn't. The cost of poor legibility is paid every time the user has to look twice.

Legibility is distinct from readability. Readability concerns the ease of reading sustained prose — line length, leading, paragraph rhythm. Legibility concerns the ease of identifying individual characters or short phrases. A poster billboard, a button label, a code listing, a phone number on a contact card — all are legibility problems. A long article is primarily a readability problem (though it requires legibility too).

Why legibility matters

Most UI text is not prose. It's labels, button text, form-field values, error messages, navigation items, search results. These are read in glances, often in motion (scrolling), often in poor conditions (small screens, harsh sunlight, while doing something else). The user has fractions of a second to identify what each chunk of text says.

Poor legibility costs the user every glance. They look, can't immediately resolve what the text says, look again, sometimes lean closer or zoom in. The cost is small per instance and large in aggregate.

In specific high-stakes contexts the cost of misread characters is severe. A pilot misreading "1" as "I" in an altitude callout. A pharmacist misreading a dosage. A doctor misreading patient ID. A user typing a confirmation code wrong because the displayed code can't be distinguished. These are not abstract concerns — they're recurring sources of real-world error.

The five inputs to legibility

Legibility is determined by:

Typeface design. Some typefaces are designed for legibility at small sizes, distance viewing, or character disambiguation. Others are designed for display use, expressive personality, or branded identity, often at the cost of legibility. Roman, sans-serif, slab-serif, and humanist typefaces vary in their legibility characteristics.

Size. Larger text is more legible up to the point where the eye can take it in at a glance. Below ~9px on screens, most typefaces lose distinguishability between characters. Below ~6px, most text becomes unreadable.

Weight. Bold and very thin weights are both less legible at small sizes than regular weight. Hairline weights especially can become illegible at small sizes or low-contrast conditions. Bold weights can fill in detail and lose distinguishability.

Contrast. Color contrast between text and background. Low contrast (gray text on white, dark blue on dark gray) reduces legibility. WCAG guidelines specify minimum contrast ratios (4.5:1 for normal text, 3:1 for large text) that are the floor for legibility.

Spacing (tracking and leading). Too-tight tracking causes characters to merge into ambiguous shapes; too-loose tracking dissolves the word into separate letters. Tight leading (line height) causes ascenders and descenders to overlap.

A typeface chosen for legibility may still be illegible if rendered too small, too thin, in low contrast, or with bad spacing. All five inputs need to be controlled.

Diagnosing legibility problems

Symptoms of legibility problems include:

Users squint or lean closer to read. They can't resolve the text from their natural viewing distance. Either the size is too small, the contrast is too low, or both.

Users frequently mistype confirmation codes, ID numbers, or other character strings. The displayed characters are hard to distinguish. Common culprits: 0/O, 1/I/l, 2/Z, 5/S, 8/B.

Tooltip hover or zoom usage is unexpectedly high. Users reach for tools that help them read because the text is too small.

Reading speed is measurably slow on UI text. A first-time-user study where users take longer to identify menu options or button labels than expected. Legibility cost is measurable.

Accessibility audits flag text contrast. Automated tools (axe, WAVE, Lighthouse) flag instances where text/background contrast is below WCAG thresholds.

Sub-skills in this cluster

  • legibility-typeface-and-size — Choosing typefaces and sizing them appropriately. The most fundamental legibility decisions; most other inputs are secondary to these.
  • legibility-contrast-and-spacing — The conditions under which a legible typeface is rendered: contrast ratio, tracking, leading, color choices.

Worked examples

A login form's legibility

A login form has labels, input fields, helper text, error messages, and a primary button. Each is a legibility decision.

  • Field labels (Email, Password): regular weight, ~14–16px, dark text on light background. Comfortable to read at a glance.
  • Input field text (the user's typed input): regular weight, ~14–16px, dark text. Critical that the user can verify what they've typed.
  • Helper text ("Must include at least one symbol"): smaller (~12px), lighter weight or muted color. Legibility threshold: still readable at small size, but de-emphasized.
  • Error messages ("Password too short"): same size or slightly larger than helper text, in error color (typically red), with sufficient contrast. Critical that the user can read the error.
  • Primary button ("Sign In"): larger (~16–18px), heavier weight, high contrast (white on brand color). Legibility supports recognition at a glance.

Each element's typography is calibrated to its role. Helper text can be smaller because it's secondary; error messages can't be too small because they're critical.

A confirmation code with a problematic typeface

A user receives a 6-character confirmation code: "OL10IB". The displayed code is in a regular sans-serif typeface where O and 0, L and 1, I and 1, all look nearly identical. The user types "0L1OIB," "OL1OIB," or other variants. The code fails. They blame themselves.

The fix: render confirmation codes in a typeface designed for character disambiguation (a slab-serif with strong distinction between 0 and O, like JetBrains Mono or Fira Code), or use only characters that can be unambiguously distinguished (omit 0, O, I, 1, L from the code alphabet).

Show full SKILL.md (657 more words)Show less
Display type vs. body type

A landing page uses a beautiful display typeface — thin, elegant, with high contrast between thick and thin strokes — for both the headline and the body copy. The headline reads beautifully (display type at large sizes is its purpose). The body copy is hard to read: the thin strokes disappear at small sizes, the high contrast makes some letters look like dots and lines rather than letterforms.

The fix: use the display typeface for the headline only. Body copy needs a typeface designed for sustained reading at small sizes — typically a humanist or transitional serif, or a sans-serif designed for legibility (Inter, IBM Plex Sans, Source Sans, etc.).

Low-contrast button text

A button has white text on a light-blue background. The contrast ratio is 2.8:1 — below the WCAG 4.5:1 minimum. Users with low vision can't read the button label; users in bright sunlight can't read it either. The button looks beautiful in the design tool but fails in production.

The fix: darken the button background until contrast is at least 4.5:1, or change the text color (white on a darker blue, dark text on a light blue). The legibility cost of the original choice is too high.

Code listing with the wrong typeface

A documentation site uses a proportional sans-serif for code examples. The proportional spacing makes it hard to align columns; the lack of distinction between l, I, and 1 makes the code ambiguous. Developers struggle to distinguish identifiers and copy code accurately.

The fix: use a monospace typeface for code (every character occupies the same width), ideally one designed for code (programming ligatures, distinguishable 0/O and 1/I/l). Common choices: JetBrains Mono, Fira Code, Cascadia Code, IBM Plex Mono.

Anti-patterns

Display typefaces in body copy. A typeface designed for headlines used at small sizes loses its distinguishing features and becomes hard to read.

Hairline weights at small sizes. Ultra-thin typefaces look elegant at large sizes and disappear at small sizes — especially on lower-DPI displays or in glare conditions.

Light gray text on white. Designers often reach for #999 or #BBB text for "secondary" content. The contrast is below WCAG threshold; users with low vision, older users, and users in bright environments struggle.

Typeface choices for branding that hurt legibility. A custom display typeface used throughout the product because it's "on-brand," even at small UI sizes where it doesn't function. Branding shouldn't override functional legibility.

Inadequate testing on low-DPI or older displays. Designers usually work on high-DPI displays where everything renders crisply. The same typography on lower-DPI displays may render with antialiasing artifacts that hurt legibility.

Rendering on bright/colorful backgrounds without adjustment. Text legibility depends on its background. The same text that's legible on white may not be legible on a photo or gradient. Test against actual backgrounds.

Heuristic checklist

Before shipping any text element, ask: Is the typeface appropriate for the size and use? Display type for headlines, body type for body, monospace for code. Is the size large enough to be read at the intended viewing distance? Test at actual size on actual devices. Is the weight calibrated to the size? Heavier weights for small sizes; lighter for large headlines. Does the contrast meet WCAG? Use a contrast checker; at minimum 4.5:1 for body text. Is the spacing comfortable? Tracking neither tight nor loose; leading at least 1.4× the font size for body text.

  • Readability — the related principle for sustained-reading text (long prose); legibility is the per-character clarity.
  • Color — color choice affects contrast and therefore legibility.
  • Signal-to-Noise Ratio — well-chosen typography is signal; poorly chosen is noise.
  • Accessibility — legibility is the typography dimension of accessibility.
  • Hierarchy — legibility supports hierarchy; you can't have hierarchy if the text is illegible.
  • Form Follows Function — display type follows display function; body type follows body function; mixing them violates the principle.

See also

  • references/lineage.md — origins in typography research, signage standards, and HCI.
  • legibility-typeface-and-size/ — sub-skill on the most fundamental legibility decisions.
  • legibility-contrast-and-spacing/ — sub-skill on rendering conditions.

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/HDeibler/universal-design-principles/plugins/perception-and-hierarchy-principles/skills/legibility of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • references/lineage.md

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Legibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Legibility compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Legibility this skillhashgraph-online/awesome-codex-plugins1.2k—~3kAutomated safety check: PassApache-2.0
Typesetsudomakes/backroad16210 repos~1.4kAutomated safety check: PassMIT
Critique TypographyOwl-Listener/designer-skills2.9k1 repos~824Automated safety check: PassMIT
Form Briefjeremylongshore/tons-of-skills-marketplace2.8k—~3.3kAutomated safety check: NotesMIT
Figure Table QualityMathews-Tom/armory328—~1.7kAutomated safety check: PassMIT
Impeccablebestofjs/bestofjs3.1k27 repos~2.6kAutomated safety check: PassMIT

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Questions about Legibility

What does Legibility do?

Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing. Legibility is an agent skill from hashgraph-online/awesome-codex-plugins. Apply the principle of Legibility — the visual clarity of individual characters and short phrases, determined by typeface, size, weight, contrast, and spacing.

When should I use Legibility?

Legibility fits situations like: picking type for UI elements (buttons; evaluating typography against rendering conditions (small sizes; low-contrast displays; distance viewing).

How do I install Legibility in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill legibility -a claude-code`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/perception-and-hierarchy-principles/skills/legibility in hashgraph-online/awesome-codex-plugins) into .claude/skills/legibility in your project. Claude Code loads it when a task matches its description.

How do I install Legibility in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill legibility -a codex`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/perception-and-hierarchy-principles/skills/legibility in hashgraph-online/awesome-codex-plugins) into .agents/skills/legibility in your project. Codex loads it when a task matches its description.

Can I use Legibility in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill legibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/legibility, .gemini/skills/legibility, .github/skills/legibility and .opencode/skills/legibility in your project.

What does Legibility need to run?

SKILL.md names no scripts, command-line tools or credentials: Legibility is instructions for the agent only.

Does Legibility access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Legibility safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Legibility use?

Legibility is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Legibility use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Legibility?

Skills that share tags, products or a category with Legibility: Typeset (sudomakes/backroad, 162 stars), Critique Typography (Owl-Listener/designer-skills, 2.9k stars), Form Brief (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Figure Table Quality (Mathews-Tom/armory, 328 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Legibility?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.