| name | ui-critique |
| description | Structured UI review — visual hierarchy, consistency, accessibility, and actionable improvement feedback. Use when working with ui critique. |
| domain | content |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | content-creation |
| tags | ["content-creation","critique","digital-content","media"] |
| version | 1.0.0 |
Overview
Structured framework for reviewing UI designs. Evaluates visual hierarchy, consistency, accessibility, spacing, typography, color usage, and provides actionable improvement recommendations.
Capabilities
- Analyze visual hierarchy and information architecture
- Check consistency across components and pages
- Evaluate accessibility (contrast, focus states, semantic HTML)
- Review responsive behavior across breakpoints
- Provide specific, actionable improvement recommendations
When to Use
Trigger phrases:
-
"ui critique"
-
"Structured UI review — visual hierarchy, consistency, accessibility, and actiona"
-
Before launching a new UI or redesign
-
Onboarding a new team member to design standards
-
User complaints about usability or confusion
-
Pre-launch design review checklist
When NOT to Use
- Task is about content strategy, not creation (use strategy skills)
- Task is about content distribution (use distribution skills)
- You need to analyze content performance (use analytics skills)
- Task is about content moderation (use moderation tools)
- You don't have content guidelines
- Task requires domain expertise (consult experts)
Pseudo Code
The ui-critique workflow follows a standard pipeline pattern.
Core flow:
# ui-critique primary flow
input = prepare(raw_data)
result = process(input, config={accessibility, actionable, consistency, critique, feedback})
validate(result)
deliver(result)
Error handling:
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Core Workflow
# ui-critique primary flow
input = prepare(raw_data)
result = process(input, config={accessibility, actionable, consistency, critique, feedback})
validate(result)
deliver(result)
Error Handling
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Critique Framework
def critique_ui(screenshot, url):
checks = {
"visual_hierarchy": check_hierarchy(screenshot),
"consistency": check_spacing_and_colors(screenshot),
"contrast": check_color_contrast(screenshot),
"responsive": check_mobile_view(url),
"loading": check_performance(url),
"accessibility": check_aria_and_semantics(url)
}
return generate_report(checks)
Common Patterns
- 3-second test: Can user identify the primary action in 3 seconds?
- Squint test: Blur the UI — does the hierarchy still work?
- Contrast check: All text meets WCAG AA (4.5:1 for normal text)
- Spacing audit: Consistent spacing between similar elements
How to Use
- Define content goal (traffic, engagement, conversion, brand awareness)
- Research target audience pain points and search intent
- Generate content using appropriate AI tools
- Edit and humanize output for authenticity
- Optimize for target platform (SEO, hashtags, format)
- Schedule and distribute across channels
- Measure performance and iterate
Red Flags
- AI-generated content sounds robotic: Always run through humanizer before publishing
- Engagement dropping week-over-week: Content fatigue or algorithm change — vary formats
- Duplicate content across platforms: Adapt content per platform, don't just cross-post
- No content calendar: Sporadic posting kills audience retention
- Ignoring analytics: Content without measurement is just publishing, not marketing
Verification
Process
- Analyze the task requirements
- Apply domain expertise
- Verify output quality
Anti-Rationalization Table
| Rationalization | Reality |
|---|
| "Good enough content works" | Quality content drives engagement. Mediocre content gets ignored. |
| "I will optimize later" | SEO and distribution need optimization from the start. |
| "Templates are good enough" | Templates are a starting point. Custom content outperforms generic. |
