AI Skill Report Card
Searching iOS Screens
iOS Screen Search & Analysis
Quick Start10 / 15
Search query: "search bar onboarding"
Filter: iOS apps, E-commerce category
Analysis focus: Input field patterns, CTA placement, visual hierarchy
Recommendation▾
The Quick Start needs an actual actionable example, not just a template format - show a real search being performed
Workflow13 / 15
Progress:
- Define search intent (inspiration, competitive analysis, pattern research)
- Craft specific search terms (UI components, flows, industries)
- Apply relevant filters (platform, category, company size)
- Analyze screen patterns and interactions
- Extract actionable design insights
- Document findings with screenshots
Step-by-step Process:
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Identify Search Goal
- UI component research (buttons, forms, navigation)
- User flow analysis (onboarding, checkout, search)
- Competitive benchmarking
- Design trend exploration
-
Construct Search Query
- Use specific UI terms: "tab bar", "modal", "card design"
- Include context: "empty state", "error message", "loading"
- Add industry keywords: "fintech", "social", "ecommerce"
-
Apply Strategic Filters
- Platform: iOS (primary focus)
- Categories: Target relevant verticals
- Companies: Filter by size/type if needed
-
Analyze Results
- Pattern identification across apps
- Interaction design consistency
- Visual design trends
- Information architecture choices
Recommendation▾
Add specific templates or frameworks for documenting findings and organizing research outputs
Examples18 / 20
Example 1: Input: Researching shopping cart abandonment solutions Query: "cart empty state recovery" Output: 15+ screens showing cart recovery patterns, promotional offers, wishlist alternatives
Example 2: Input: Designing payment flow Query: "payment method selection checkout" Output: Payment UI patterns from Uber, Amazon, Shopify showing card layouts, biometric options, error states
Example 3: Input: Improving search experience Query: "search suggestions autocomplete" Output: Search patterns from Spotify, YouTube, eBay demonstrating suggestion types, visual treatments, result previews
Recommendation▾
Include more concrete input/output pairs in examples section - show actual Mobbin search results and what insights were extracted
Best Practices
- Use compound search terms - Combine UI components with context ("button loading state")
- Search by user journey stages - "first time user", "returning customer", "power user"
- Include emotional states - "error frustration", "success celebration", "empty disappointing"
- Filter by app maturity - Established apps vs. new releases show different pattern adoption
- Save promising patterns - Build personal reference library of effective solutions
- Cross-reference multiple apps - Don't rely on single app's approach
Common Pitfalls
- Overly broad searches - "mobile app" returns everything; be specific
- Ignoring context - Beautiful screens may not fit your use case or constraints
- Platform confusion - Mixing iOS and Android patterns creates inconsistent experience
- Trend chasing - Popular ≠ effective for your specific users and goals
- Screenshot collection without analysis - Document why patterns work, not just what they look like
- Overlooking failed patterns - Learn from screens that feel broken or confusing