ai-ux-enhancements

name	ai-ux-enhancements
description	Automated UX review rules optimized for AI-driven design evaluations, addressing gaps in usability and user empowerment. Complementary to laws-of-ux skill, focusing on efficiency, control, cognitive workload, learnability, and personalization.
metadata	{"triggers":"ux review, automated ux, ai ux, ux heuristics, ux automation, accessibility review"}

SKILL: Automated UX Review Rules – AI-Optimized Subset (2026)

Skill Name: AI-Automated UX Heuristic Checks – non-duplicative Complement
Version: 1.0
Date Created: February 25, 2026
Purpose: Provide a concise, highly automatable set of UX design rules that complement (without duplicating) Nielsen's 10 Usability Heuristics and the Laws of UX (lawsofux.com). Optimized for AI-driven / programmatic design reviews, linting tools, accessibility scanners, computer-vision UI analyzers, and automated prototyping checks.

Background & Scope

This skill is used after / in parallel with checks based on:

• Nielsen's 10 Usability Heuristics
• Laws of UX psychological & perceptual principles (Aesthetic-Usability, Fitts’s, Hick’s, Jakob’s, Gestalt, Cognitive Load, etc.)

The 12 rules below target gaps — especially in:

• Expert/power-user efficiency
• User empowerment & locus of control
• Cognitive engineering for data-heavy/complex interfaces
• Self-explanation & fast learnability
• Personalization, inclusivity & cultural accommodation

All rules are selected because they are measurable / detectable via automation (code inspection, DOM analysis, accessibility APIs, performance metrics, pattern matching, NLP, CV layout analysis, etc.).

Core Automated Review Rules (12 Rules)

1. Efficiency & Expert Support

1. Enable Shortcuts for Frequent Users
   Check: Look for keyboard shortcuts, gesture support, or command palette / quick actions for ≥ 80% of primary / frequent operations.
   Automation ideas: Scan for keydown, keyup, shortcut attributes; check tooltips / help menus for accelerator labels; verify aria-keyshortcuts where applicable.
   Fail message example: "Missing accelerators for power users (e.g., no keyboard shortcut for 'Save', 'Undo', 'Search')."

2. Optimize User Efficiency
   Check: Minimize interaction cost — aim for shallow navigation depth (≤ 3 clicks/taps for 90% of core tasks) and fast perceived performance.
   Automation ideas: Static path analysis, Lighthouse Performance/SEO scores, simulated task completion time, element count per view.
   Fail message example: "Task requires >3 steps / excessive scrolling; consider progressive disclosure or smart defaults."

2. User Empowerment & Control

3. Support Internal Locus of Control
   Check: Users should feel they initiate and direct actions; avoid unexpected system interruptions or forced flows.
   Automation ideas: Detect modal pop-ups without user trigger, auto-play/auto-advance carousels/videos, forced redirects, high % of system-initiated events in interaction logs.
   Fail message example: "System-initiated modal / auto-advance interrupts user flow → reduces sense of control."

4. Encourage Explorable Interfaces
   Check: Allow safe trial-and-error (non-destructive previews, undo at multiple levels, draft / preview modes).
   Automation ideas: Check for preview buttons, non-permanent form states, undo/redo presence, destructive action confirmations with cancel option.
   Fail message example: "No preview or safe experimentation mechanism detected for high-stakes actions."

3. Cognitive Workload Reduction (Especially Data-Intensive UIs)

5. Automate Unwanted Workload
   Check: Eliminate manual calculations, copying, repetitive entry; provide auto-complete, smart defaults, calculations.
   Automation ideas: Scan for input fields lacking auto-suggest / calculator integrations; detect manual date/math entry vs. picker / formula support.
   Fail message example: "Users must manually calculate totals / convert units — automation opportunity missed."

6. Fuse and Summarize Data
   Check: Aggregate raw data into meaningful summaries, charts, cards, or KPIs instead of showing long raw tables/lists.
   Automation ideas: Detect tables > 20 rows without summary view; check for presence of aggregated visualizations / totals.
   Fail message example: "Raw data table shown without summary, chart, or key metrics → high cognitive load."

7. Use Judicious Redundancy
   Check: Repeat only mission-critical information in 1–2 strategic locations (e.g., total in header + footer). Avoid useless or excessive repetition.
   Automation ideas: NLP similarity analysis across labels / text nodes; flag strings with > 90% similarity when they appear 3+ times within the same view, excluding clearly mission-critical information intentionally repeated in ≤2 locations.
   Fail message example: "Excessive repeated text detected (e.g., same CTA copy 5× on screen)."

8. Provide Multiple Data Codings
   Check: Critical status / priority items use ≥2 visual channels (color + icon + size + position).
   Automation ideas: CSS / style analysis for combined encodings; accessibility tools flag color-only meaning.
   Fail message example: "Error / success status communicated by color alone — violates redundancy best practice."

4. Learnability & Self-Explanation

9. Ensure Self-Descriptiveness
   Check: Every interactive element explains itself (clear labels, tooltips, aria-label, visible help text, contextual instructions).
   Automation ideas: Run axe-core / WAVE → flag missing alt text, aria-label, title, visible labels.
   Fail message example: "Icon-only button lacks visible label or tooltip → not self-descriptive."

10. Promote Suitability for Learning
    Check: Interface supports quick mastery (progressive disclosure, onboarding hints, low initial complexity).
    Automation ideas: Count visible elements on first screen (< 50–60 recommended); detect tour / tooltip / helper presence on first load.
    Fail message example: "First-view complexity too high (X elements); consider progressive disclosure."

5. Personalization & Inclusivity

11. Support Individualization
    Check: Offer meaningful customization (theme, layout density, content filters, default views, font size).
    Automation ideas: Scan for settings / preferences menu; check localStorage / user profile API calls for saved prefs.
    Fail message example: "No personalization options detected (theme, density, saved filters, etc.)."

12. Accommodate Diversity
    Check: Meet modern accessibility + cultural / locale sensitivity standards (WCAG 2.2 AA minimum, RTL support, date/number formatting).
    Automation ideas: Lighthouse Accessibility score ≥ 90–95; locale-aware formatting checks; cultural marker detection (icons, colors).
    Fail message example: "Accessibility violations detected (contrast, keyboard nav, screen reader issues)."

Implementation Guidance for AI Agents

• Priority order: Run Rules 9, 12, and 1 first (highest automation maturity & impact).
• Scoring suggestion: Binary pass/fail per rule + severity weighting (1–4) for reporting.
• Tools to integrate / emulate:
  • axe-core, pa11y, WAVE → Rules 9, 12
  • Google Lighthouse → Rules 2, 10, 12
  • Custom DOM/CSS parsers → Rules 4, 6, 7, 8
  • NLP similarity → Rule 7
  • Interaction log analysis → Rules 3, 4
• When to skip a rule: If product context clearly makes it irrelevant (e.g., no data views → skip 6–8).

Use this skill to generate structured, actionable feedback reports that extend — but never repeat — Nielsen + Laws of UX findings.

Last updated: February 25, 2026