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
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')."
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
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."
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)
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."
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."
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)."
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
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."
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
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.)."
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
1---2name: ai-ux-enhancements3description: 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.4---56# SKILL: Automated UX Review Rules – AI-Optimized Subset (2026)78**Skill Name:** AI-Automated UX Heuristic Checks – non-duplicative Complement 9**Version:** 1.0 10**Date Created:** February 25, 2026 11**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.1213## Background & Scope1415This skill is used **after / in parallel with** checks based on:16- Nielsen's 10 Usability Heuristics17- Laws of UX psychological & perceptual principles (Aesthetic-Usability, Fitts’s, Hick’s, Jakob’s, Gestalt, Cognitive Load, etc.)1819The 12 rules below target **gaps** — especially in:20- Expert/power-user efficiency21- User empowerment & locus of control22- Cognitive engineering for data-heavy/complex interfaces23- Self-explanation & fast learnability24- Personalization, inclusivity & cultural accommodation2526All 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.).2728## Core Automated Review Rules (12 Rules)2930### 1. Efficiency & Expert Support31321. **Enable Shortcuts for Frequent Users** 33 **Check:** Look for keyboard shortcuts, gesture support, or command palette / quick actions for ≥ 80% of primary / frequent operations. 34 **Automation ideas:** Scan for `keydown`, `keyup`, `shortcut` attributes; check tooltips / help menus for accelerator labels; verify aria-keyshortcuts where applicable. 35 **Fail message example:** "Missing accelerators for power users (e.g., no keyboard shortcut for 'Save', 'Undo', 'Search')."36372. **Optimize User Efficiency** 38 **Check:** Minimize interaction cost — aim for shallow navigation depth (≤ 3 clicks/taps for 90% of core tasks) and fast perceived performance. 39 **Automation ideas:** Static path analysis, Lighthouse Performance/SEO scores, simulated task completion time, element count per view. 40 **Fail message example:** "Task requires >3 steps / excessive scrolling; consider progressive disclosure or smart defaults."4142### 2. User Empowerment & Control43443. **Support Internal Locus of Control** 45 **Check:** Users should feel they initiate and direct actions; avoid unexpected system interruptions or forced flows. 46 **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. 47 **Fail message example:** "System-initiated modal / auto-advance interrupts user flow → reduces sense of control."48494. **Encourage Explorable Interfaces** 50 **Check:** Allow safe trial-and-error (non-destructive previews, undo at multiple levels, draft / preview modes). 51 **Automation ideas:** Check for preview buttons, non-permanent form states, undo/redo presence, destructive action confirmations with cancel option. 52 **Fail message example:** "No preview or safe experimentation mechanism detected for high-stakes actions."5354### 3. Cognitive Workload Reduction (Especially Data-Intensive UIs)55565. **Automate Unwanted Workload** 57 **Check:** Eliminate manual calculations, copying, repetitive entry; provide auto-complete, smart defaults, calculations. 58 **Automation ideas:** Scan for input fields lacking auto-suggest / calculator integrations; detect manual date/math entry vs. picker / formula support. 59 **Fail message example:** "Users must manually calculate totals / convert units — automation opportunity missed."60616. **Fuse and Summarize Data** 62 **Check:** Aggregate raw data into meaningful summaries, charts, cards, or KPIs instead of showing long raw tables/lists. 63 **Automation ideas:** Detect tables > 20 rows without summary view; check for presence of aggregated visualizations / totals. 64 **Fail message example:** "Raw data table shown without summary, chart, or key metrics → high cognitive load."65667. **Use Judicious Redundancy** 67 **Check:** Repeat only mission-critical information in 1–2 strategic locations (e.g., total in header + footer). Avoid useless or excessive repetition. 68 **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. 69 **Fail message example:** "Excessive repeated text detected (e.g., same CTA copy 5× on screen)."70718. **Provide Multiple Data Codings** 72 **Check:** Critical status / priority items use ≥2 visual channels (color + icon + size + position). 73 **Automation ideas:** CSS / style analysis for combined encodings; accessibility tools flag color-only meaning. 74 **Fail message example:** "Error / success status communicated by color alone — violates redundancy best practice."7576### 4. Learnability & Self-Explanation77789. **Ensure Self-Descriptiveness** 79 **Check:** Every interactive element explains itself (clear labels, tooltips, aria-label, visible help text, contextual instructions). 80 **Automation ideas:** Run axe-core / WAVE → flag missing alt text, aria-label, title, visible labels. 81 **Fail message example:** "Icon-only button lacks visible label or tooltip → not self-descriptive."828310. **Promote Suitability for Learning** 84 **Check:** Interface supports quick mastery (progressive disclosure, onboarding hints, low initial complexity). 85 **Automation ideas:** Count visible elements on first screen (< 50–60 recommended); detect tour / tooltip / helper presence on first load. 86 **Fail message example:** "First-view complexity too high (X elements); consider progressive disclosure."8788### 5. Personalization & Inclusivity899011. **Support Individualization** 91 **Check:** Offer meaningful customization (theme, layout density, content filters, default views, font size). 92 **Automation ideas:** Scan for settings / preferences menu; check localStorage / user profile API calls for saved prefs. 93 **Fail message example:** "No personalization options detected (theme, density, saved filters, etc.)."949512. **Accommodate Diversity** 96 **Check:** Meet modern accessibility + cultural / locale sensitivity standards (WCAG 2.2 AA minimum, RTL support, date/number formatting). 97 **Automation ideas:** Lighthouse Accessibility score ≥ 90–95; locale-aware formatting checks; cultural marker detection (icons, colors). 98 **Fail message example:** "Accessibility violations detected (contrast, keyboard nav, screen reader issues)."99100## Implementation Guidance for AI Agents101102- **Priority order:** Run Rules 9, 12, and 1 first (highest automation maturity & impact).103- **Scoring suggestion:** Binary pass/fail per rule + severity weighting (1–4) for reporting.104- **Tools to integrate / emulate:**105 - axe-core, pa11y, WAVE → Rules 9, 12106 - Google Lighthouse → Rules 2, 10, 12107 - Custom DOM/CSS parsers → Rules 4, 6, 7, 8108 - NLP similarity → Rule 7109 - Interaction log analysis → Rules 3, 4110- **When to skip a rule:** If product context clearly makes it irrelevant (e.g., no data views → skip 6–8).111112Use this skill to generate structured, actionable feedback reports that extend — but never repeat — Nielsen + Laws of UX findings.113114Last updated: February 25, 2026
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