Visual Consistency Validation Skill
SkillMediaUse when validating design system token compliance, detecting visual regressions, or analyzing responsive design consistency across breakpoints and modes
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Visual Consistency Validation Skill skill
What this skill tells your AI
The instructions your AI receives, as published by tan-yong-sheng/ai-vision-mcp in plugins/design-eval/skills/visual-consistency-validation/SKILL.md and read by ahel’s review.
Systematic approach to validating design system compliance across all breakpoints, modes, and interactive states.
Core Principle
Every violation must be: (1) mapped to a specific token, (2) quantified, (3) severity-categorized, and (4) provided with remediation. No rationalization allowed.
When to Use
- Validating component compliance against design system tokens
- Detecting visual regressions between baseline and updated designs
- Analyzing responsive design consistency (mobile, tablet, desktop)
- Validating dark mode and alternative state compliance
The Three Disciplines
1. Systematic Token Mapping
Every violation follows this template:
Property: [CSS property]
Current: [actual value]
Token: [token name from design system]
Expected: [token value]
Difference: [quantified: pixels, %, hex delta]
2. Comprehensive Coverage
Validate everything:
- All color properties (text, background, border, shadow, icon)
- All spacing (padding, margin, gap, position)
- All typography (size, weight, line-height, letter-spacing)
- All shapes (radius, borders, shadows)
- All animations (duration, easing)
- All breakpoints (mobile, tablet, desktop)
- All modes (light, dark, high-contrast)
- All interactive states (default, hover, focus, active, disabled)
3. Severity Categorization
| Level | When |
|---|---|
| CRITICAL | Accessibility/layout failures (WCAG contrast, broken states, layout shifts) |
| HIGH | Token violations with user impact (colors, spacing, typography) |
| MEDIUM | Minor deviations (< 10% difference) |
| LOW | Very minor (< 5% difference) |
Rule: All WCAG failures → CRITICAL (regardless of context).
Red Flags - STOP and Reanalyze
| Red Flag | What to Do |
|---|---|
| "Close enough" | Measure it. Report every difference. |
| "Desktop is primary" | Analyze all breakpoints equally. |
| "Light mode is primary" | Analyze all modes equally. |
| "Minor refinement" | Categorize by severity and report. |
| "Typical for design systems" | Escalate accessibility failures to CRITICAL. |
| "Not evaluating hover states" | Validate all interactive states. |
| "Acceptable variance" | Quantify and report every deviation. |
Validation Checklist
Before completing analysis:
- All visible properties analyzed
- All violations mapped to specific tokens
- All differences quantified (pixels, percentages, hex deltas)
- All breakpoints analyzed (mobile, tablet, desktop)
- All modes analyzed (light, dark, high-contrast)
- All interactive states validated
- Severity categorized for each violation
- Accessibility violations escalated to CRITICAL
- Complete remediation (CSS + testing approach) for each violation
- No violations rationalized away
Reference Materials
Detailed implementation guidance:
- Execution Guide — How visual-consistency-tester implements this skill using Playwright and design tokens
- Implementation Patterns — Code patterns for token mapping, violation detection, severity calculation, remediation generation
Signals
- GitHub stars
- 78
- Forks
- 16
- Last commit
- Apr 2026
Advanced
- Catalog kind
- skill
- Gateway key
visual-consistency-validation- Source
- github.com/tan-yong-sheng/ai-vision-mcp