| name | user-testing |
| description | Comprehensive persona-based user testing skill for web applications. Simulates how real users from different demographics interact with interfaces, including realistic timing, behavioral patterns, and frustration triggers.
Use when:
- Testing user interfaces before release
- Validating UX flows from diverse perspectives
- Conducting accessibility reviews
- Optimizing onboarding or checkout experiences
- Getting feedback on form design
|
User Testing Skill
Overview
This skill enables persona-based user testing that simulates how real users interact with web applications. It combines behavioral psychology, UX research methodologies, and browser automation to provide authentic testing experiences.
When to Use This Skill
- Pre-release testing: Test new features before they ship
- UX validation: Verify that flows work for different user types
- Accessibility review: Check clarity and usability across skill levels
- Form optimization: Test form completion rates and friction points
- Onboarding analysis: Evaluate first-time user experience
- Checkout testing: Identify cart abandonment risks
Persona System
Available Personas
| ID | Generation | Tech Comfort | Patience | Key Trait |
|---|
boomer-tech-averse | Boomer | 2/10 | 7/10 | Needs explicit guidance |
boomer-tech-friendly | Boomer | 6/10 | 8/10 | Prefers familiar patterns |
millennial-tech-skeptic | Millennial | 7/10 | 5/10 | Skeptical of manipulation |
genz-digital-native | Gen Z | 9/10 | 3/10 | Expects instant results |
genalpha-tablet-kid | Gen Alpha | 8/10 | 2/10 | Touch-first, visual |
Gender Variants
Each persona has three gender variants with different names and subtle trait adjustments:
- Male: Slightly different communication style
- Female: Slightly different priorities and observations
- Neutral: Balanced default variant
Persona Structure
Personas are defined in JSON files with this schema:
{
"id": "persona-id",
"name": "Display Name",
"generation": "boomer|millennial|genz|genalpha",
"techComfort": 1-10,
"patience": 1-10,
"genderVariants": {
"male": { "name": "...", "pronouns": "he/him", "traits": [...] },
"female": { "name": "...", "pronouns": "she/her", "traits": [...] }
...
Timing System
Reading Time Calculation
base_time = (word_count / persona.timing.baseReadingWPM) * 60 * 1000 // ms
adjusted_time = base_time * complexity_modifier
final_time = adjusted_time * random(0.8, 1.2) // Add variance
Complexity Modifiers:
- Simple text: 1.0x
- Technical jargon: 1.5x (varies by persona)
- Legal/privacy text: 2.0x for skeptics, 0.5x for impatient users
Interaction Delays
| Action | Tech-Averse | Tech-Friendly | Tech-Skeptic | Digital Native | Tablet Kid |
|---|
| Click | 2000-3000ms | 800-1200ms | 600-1000ms | 200-400ms | 300-600ms |
| Form field | 1500ms | 800ms | 600ms | 200ms | 400ms |
| Decision | 5000ms | 2000ms | 3000ms | 500ms | 1000ms |
| Confusion | 20000ms | 10000ms | 8000ms | 5000ms | 3000ms |
Confusion Simulation
When a persona encounters confusing UI:
- Initial Pause: Extended delay (5-30 seconds based on patience)
- Visual Scanning: Look around the page for clues
- Verbal Expression: Narrate confusion in character
- Recovery Attempt: Try alternative approaches
- Potential Abandonment: If frustration threshold exceeded
Report Format
Real-Time Narration
During testing, output continuous first-person narration:
[10:32:15] [Gen Z - Jayden] [NAVIGATE]
"Okay let's see what this is about..."
[10:32:18] [Gen Z - Jayden] [SCAN]
"Lot of text... skip... skip... where's the sign up button?"
[10:32:20] [Gen Z - Jayden] [CLICK]
"There it is. Let's go."
[10:32:25] [Gen Z - Jayden] [FRUSTRATION]
"Bruh, why do they need my phone number?? This is sus."
Summary Report Structure
# User Testing Report
## Session Overview
- **Persona**: [Name] ([ID])
- **Gender Variant**: [Male/Female/Neutral]
- **URL Tested**: [URL]
- **Tasks Attempted**: [List]
- **Overall Success**: [X/Y tasks completed]
## Executive Summary
[1-2 paragraph overview]
## Task Analysis
### Task 1: [Description]
- **Status**: Completed / Partial / Failed
- **Time**: [Duration]
- **Confusion Points**:
- [Timestamp]: [What caused confusion]
- **Screenshots**: [Links]
## Usability Issues
### Critical (Task Blockers)
1. **[Issue]**
- Personas affected: [List]
- Evidence: [Screenshot/description]
- Recommendation: [Fix]
### Major (High Frustration)
...
### Minor (Suboptimal)
...
## Persona-Specific Insights
[Observations unique to this persona's worldview]
## Prioritized Recommendations
| Priority | Issue | Recommendation | Affected Personas |
|----------|-------|----------------|-------------------|
| P0 | ... | ... | All |
| P1 | ... | ... | Tech-averse |
Usage Examples
Basic Test
/user-test --url https://example.com --persona genz-digital-native
With Specific Tasks
/user-test --url https://shop.example.com --persona boomer-tech-averse --tasks "find product, add to cart, checkout"
With Gender Variant
/user-test --url https://app.example.com --persona millennial-tech-skeptic --gender f
Verbose Mode
/user-test --url https://example.com --persona genalpha-tablet-kid --verbose
Best Practices
For Accurate Testing
- Test Multiple Personas: Different users find different issues
- Include Edge Cases: Tech-averse users reveal clarity issues
- Document Everything: Screenshots capture evidence
- Compare Results: Cross-persona patterns reveal universal issues
For Actionable Results
- Prioritize by Severity: Critical > Major > Minor
- Note Affected Personas: Some issues only affect certain users
- Provide Recommendations: Don't just report problems
- Include Evidence: Screenshots make issues undeniable
Integration with Chrome
This skill requires claude --chrome for browser automation:
claude --chrome
/user-test --url https://your-app.com --persona genz-digital-native
The skill uses Playwright MCP tools:
browser_navigate - Page navigationbrowser_snapshot - Accessibility tree analysisbrowser_click - Element interactionbrowser_type - Text inputbrowser_take_screenshot - Evidence capture
