user-testing

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": [...] },
    "neutral": { "name": "...", "pronouns": "they/them", "traits": [...] }
  },
  "traits": {
    "readingSpeed": "slow|medium|fast",
    "clickConfidence": "hesitant|normal|decisive",
    "errorRecovery": "confused|methodical|adaptive",
    "scrollBehavior": "careful|normal|impatient",
    "formFilling": "cautious|normal|rushed"
  },
  "timing": {
    "baseReadingWPM": 100-400,
    "clickDelay": 300-3000,
    "hoverBeforeClick": 200-1500,
    "confusionPause": 5000-30000,
    "successPause": 300-2000,
    "pageLoadTolerance": 2000-10000
  },
  "narrationStyle": {
    "vocabulary": "simple|moderate|technical|casual",
    "frustrationThreshold": 1-10,
    "verbosity": "quiet|normal|chatty",
    "expressions": ["...", "..."]
  },
  "frustrationTriggers": ["...", "..."],
  "positiveReactions": ["...", "..."],
  "typicalBehaviors": ["...", "..."]
}

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:

1. Initial Pause: Extended delay (5-30 seconds based on patience)
2. Visual Scanning: Look around the page for clues
3. Verbal Expression: Narrate confusion in character
4. Recovery Attempt: Try alternative approaches
5. 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

1. Test Multiple Personas: Different users find different issues
2. Include Edge Cases: Tech-averse users reveal clarity issues
3. Document Everything: Screenshots capture evidence
4. Compare Results: Cross-persona patterns reveal universal issues

For Actionable Results

1. Prioritize by Severity: Critical > Major > Minor
2. Note Affected Personas: Some issues only affect certain users
3. Provide Recommendations: Don't just report problems
4. Include Evidence: Screenshots make issues undeniable

Integration with Chrome

This skill requires claude --chrome for browser automation:

# Start Claude with Chrome integration
claude --chrome

# Then run user tests
/user-test --url https://your-app.com --persona genz-digital-native

The skill uses Playwright MCP tools:

• browser_navigate - Page navigation
• browser_snapshot - Accessibility tree analysis
• browser_click - Element interaction
• browser_type - Text input
• browser_take_screenshot - Evidence capture