---

name: enhance-readability description: > Audit and fix how easily content is understood: line length (CPL), reading level, grouping, deadspace, icons or tables that cut verbosity. Use when "hard to read", "too dense", "reading level", "cognitive load", or "turn this prose into a table". license: MIT

Surface router: /uiux. You are here: enhance-readability. Native iOS/Android (SwiftUI / Compose, no web layer) is out of scope — use Apple HIG / Material directly.

enhance-readability — Make content understood

Degree of freedom: MIXED. Text-metric measurement [LOW freedom — run exactly]; grouping, iconography, and tone [HIGH freedom].

Audit-and-fix. The goal is comprehension: the least mental effort for the reader to understand the content. enhance-web-ui makes it look good; audit-responsive makes it fit the screen; this makes it understood.

The backbone is Cognitive Load Theory (working memory is small — cut extraneous load) and Gestalt (the brain auto-groups visual elements — design with that). A consequence of CLT / information overload: showing more information can raise extraneous load and lower understanding. Replacing a dense paragraph with one table or diagram is a comprehension win only when the visual reduces total load versus the text it replaces — never decoration.

How to reason

1. Observe — what makes this effortful? (dense text / no hierarchy / wall of prose / no whitespace / wrong register)
2. Diagnose — which of the six channels, and which Gestalt / CLT principle
3. Classify load — extraneous (bad presentation — always cut) vs intrinsic (inherent complexity — restructure / chunk) vs germane (productive — keep)
4. Lever — cheapest channel that removes that load
5. Severity — how much it blocks understanding on the primary path

Worked example

Observe: a pricing section is 3 dense paragraphs comparing 4 plans, 105 CPL, one grey block. Diagnose: dim 4 (comparison is a table), dim 3 (no proximity grouping), dim 1 (CPL 105 > 80), dim 5 (no whitespace). Classify: extraneous (prose form) + intrinsic (4-way comparison → chunk into a table). Lever: generate a 4-column comparison table; cap surrounding text at 66ch; icon per tier. Handoff: table collapse on mobile → audit-responsive; table tokens → audit-uiux-design-system.

Self-critique before reporting

• Load reduced — each change names extraneous vs intrinsic; "looks cleaner" is not the test
• Right owner — breakpoints → audit-responsive; personality → enhance-web-ui; WCAG floor → audit-accessibility; voice authenticity → plan-antislop; tokens → audit-uiux-design-system
• Language-correct — CJK judged at 40 CPL (WCAG 2.2 SC 1.4.8)
• Visuals earn their place — generated graphic reduces load vs the text it replaces
• Voice intact — tone lowers effort without flattening the author

The six channels (one goal: lower cognitive load)

1. Text — legibility and reading level (measurable).
2. Separation — Gestalt figure-ground + similarity (weight / color / rule).
3. Layout — proximity + chunking (~4 working-memory units) + hierarchy.
4. Iconography & graphics — icons paired with text; generate a visual only if load drops.
5. Deadspace — negative space that directs focus. "Is space aiding understanding," not "fix the 1440px grid."
6. Tone & accent — register/rhythm for the audience. Authenticity / AI-tells → plan-antislop.

Each is sized per form factor as comprehension judgment, not breakpoint CSS.

Boundary — what this skill does NOT own

• Breakpoint layout / matcher / CSS grid / "desktop = stretched phone" → audit-responsive
• Visual personality, brand, aesthetic composition → enhance-web-ui
• WCAG pass/fail contrast/size floor → audit-accessibility (this skill treats the floor as the minimum)
• Voice authenticity / anti-slop → plan-antislop
• Design-token / system coherence → audit-uiux-design-system

Note and hand off. Do not duplicate.

Text thresholds (Phase 1) [LOW freedom — run exactly]

Sentence limits come from ASD-STE100: 20 words in a procedure, 25 in descriptive text, one instruction per sentence. Full rules and the Vale recipe: ../docs-writer/references/plain-language-ste.md.

House targets, measured not eyeballed. CPL/CJK and line-height come from WCAG 2.2 SC 1.4.8 Visual Presentation (AAA mechanism criterion; we use the numbers as authoring targets):

• CPL / measure: 50–75, ~66 sweet spot; hard limit 80 Latin, 40 CJK (日本語).
• Line-height: 1.5–1.8 body. Font: body ≥16px (16–18 optimal). Paragraphs: 2–4 lines.
• Contrast: WCAG AA floor is the minimum, not the goal — flag pale secondary text.
• Alignment: left-align running text (do not justify). Opposite of audit-responsive's left-stacked-layout anti-pattern.
• Reading level: Flesch-Kincaid ≤8 for general UI (plain-language house target, not WCAG); sentences <~20 words; front-load; chunk.

Phase 0 — Scope the content surfaces [HIGH freedom]

Identify where understanding is the job: article/doc bodies, comparisons, dashboards, onboarding, help/FAQ, dense forms, data-heavy views, empty/error copy. Note language(s) (CJK vs Latin) and the primary comprehension goal ("decide between plans", "learn the concept", "complete the task").

Phase 1 — Measure text legibility [LOW freedom — run exactly]

Per surface, measure rendered values: CPL at real width, line-height, body/caption px, paragraph length, contrast, alignment, reading level. Record value-vs-threshold.

Phase 2 — Visual channels (2, 3, 5) against Gestalt [HIGH freedom]

• Separation (2): distinct types look distinct, or one undifferentiated stream?
• Grouping (3): related close, unrelated apart? Chunked to working-memory size? Clear first-look target?
• Deadspace (5): whitespace separates and focuses, or everything is crammed and left-stacked? (Use of space for comprehension — not the breakpoint grid.)

Phase 3 — Iconography & graphics (4) [HIGH freedom]

Where prose carries a load a visual would carry better, propose (and generate if the environment supports it) a comparison table, flow/step diagram, scan-anchor icons, or a chart. Test: does it reduce total load vs the text it replaces? If not, do not add it. Existing icons: paired with text and consistent, or noise? (NN/g: almost no icon is universal without a label.)

Phase 4 — Tone (6), then fix in the system [HIGH freedom]

Register right for audience and goal (short + direct for tasks; warmer for onboarding). Preserve author voice. Fixes use the repo's design tokens and type scale — never one-off values; systemic gaps → audit-uiux-design-system. Generated visuals follow the design system. Keep diffs to comprehension.

Definition of Done

• Surfaces scoped with per-surface comprehension goal; language(s) noted
• Text measured value-vs-threshold (CPL incl. CJK, line-height, px, paragraph, contrast, align, reading level)
• Separation, grouping, and deadspace assessed against Gestalt / CLT
• Iconography/graphics assessed; visuals generated only if load drops
• Tone assessed; author voice preserved
• Findings classified by channel + load type + severity
• Fixes use tokens/scale; systemic gaps flagged
• Boundary respected (responsive / web-ui / a11y / antislop / design-system)
• Self-critique applied; every change names the load it removes

Output format

1. Text measurement table — surface | CPL | line-height | px | paragraph | contrast | align | reading level | vs threshold
2. Channel findings — surface | channel (1–6) | Gestalt/CLT principle | load type | severity | fix
3. Proposed/generated visuals — load-reduction rationale
4. Fixes applied — token CSS + regrouping + visuals + copy/tone, before/after
5. Handoffs — breakpoints / personality / WCAG / voice / tokens

Related

• enhance-web-ui — looks good (composition / personality)
• audit-responsive — container reflow per breakpoint
• audit-accessibility — WCAG pass/fail floor
• plan-antislop — voice authenticity / AI tells
• audit-uiux-design-system — tokens / component SSOT
• enhance-web-ux — flows / IA / information density of the task, not reading measure