UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.
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Does it follow best practices?
Impact
90%
1.15xAverage score across 6 eval scenarios
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Generate user personas from research data, create journey maps, plan usability tests, and synthesize research findings into actionable design recommendations.
Use this skill when you need to:
Situation: You have user data (analytics, surveys, interviews) and need to create a research-backed persona.
Steps:
Prepare user data
Required format (JSON):
[
{
"user_id": "user_1",
"age": 32,
"usage_frequency": "daily",
"features_used": ["dashboard", "reports", "export"],
"primary_device": "desktop",
"usage_context": "work",
"tech_proficiency": 7,
"pain_points": ["slow loading", "confusing UI"]
}
]Run persona generator
# Human-readable output
python scripts/persona_generator.py
# JSON output for integration
python scripts/persona_generator.py jsonReview generated components
| Component | What to Check |
|---|---|
| Archetype | Does it match the data patterns? |
| Demographics | Are they derived from actual data? |
| Goals | Are they specific and actionable? |
| Frustrations | Do they include frequency counts? |
| Design implications | Can designers act on these? |
Validate persona
Reference: See references/persona-methodology.md for validity criteria
Situation: You need to visualize the end-to-end user experience for a specific goal.
Steps:
Define scope
| Element | Description |
|---|---|
| Persona | Which user type |
| Goal | What they're trying to achieve |
| Start | Trigger that begins journey |
| End | Success criteria |
| Timeframe | Hours/days/weeks |
Gather journey data
Sources:
Map the stages
Typical B2B SaaS stages:
Awareness → Evaluation → Onboarding → Adoption → AdvocacyFill in layers for each stage
Stage: [Name]
├── Actions: What does user do?
├── Touchpoints: Where do they interact?
├── Emotions: How do they feel? (1-5)
├── Pain Points: What frustrates them?
└── Opportunities: Where can we improve?Identify opportunities
Priority Score = Frequency × Severity × Solvability
Reference: See references/journey-mapping-guide.md for templates
Situation: You need to validate a design with real users.
Steps:
Define research questions
Transform vague goals into testable questions:
| Vague | Testable |
|---|---|
| "Is it easy to use?" | "Can users complete checkout in <3 min?" |
| "Do users like it?" | "Will users choose Design A or B?" |
| "Does it make sense?" | "Can users find settings without hints?" |
Select method
| Method | Participants | Duration | Best For |
|---|---|---|---|
| Moderated remote | 5-8 | 45-60 min | Deep insights |
| Unmoderated remote | 10-20 | 15-20 min | Quick validation |
| Guerrilla | 3-5 | 5-10 min | Rapid feedback |
Design tasks
Good task format:
SCENARIO: "Imagine you're planning a trip to Paris..."
GOAL: "Book a hotel for 3 nights in your budget."
SUCCESS: "You see the confirmation page."Task progression: Warm-up → Core → Secondary → Edge case → Free exploration
Define success metrics
| Metric | Target |
|---|---|
| Completion rate | >80% |
| Time on task | <2× expected |
| Error rate | <15% |
| Satisfaction | >4/5 |
Prepare moderator guide
Reference: See references/usability-testing-frameworks.md for full guide
Situation: You have raw research data (interviews, surveys, observations) and need actionable insights.
Steps:
Code the data
Tag each data point:
[GOAL] - What they want to achieve[PAIN] - What frustrates them[BEHAVIOR] - What they actually do[CONTEXT] - When/where they use product[QUOTE] - Direct user wordsCluster similar patterns
User A: Uses daily, advanced features, shortcuts
User B: Uses daily, complex workflows, automation
User C: Uses weekly, basic needs, occasional
Cluster 1: A, B (Power Users)
Cluster 2: C (Casual User)Calculate segment sizes
| Cluster | Users | % | Viability |
|---|---|---|---|
| Power Users | 18 | 36% | Primary persona |
| Business Users | 15 | 30% | Primary persona |
| Casual Users | 12 | 24% | Secondary persona |
Extract key findings
For each theme:
Prioritize opportunities
| Factor | Score 1-5 |
|---|---|
| Frequency | How often does this occur? |
| Severity | How much does it hurt? |
| Breadth | How many users affected? |
| Solvability | Can we fix this? |
Reference: See references/persona-methodology.md for analysis framework
Generates data-driven personas from user research data.
| Argument | Values | Default | Description |
|---|---|---|---|
| format | (none), json | (none) | Output format |
Sample Output:
============================================================
PERSONA: Alex the Power User
============================================================
📝 A daily user who primarily uses the product for work purposes
Archetype: Power User
Quote: "I need tools that can keep up with my workflow"
👤 Demographics:
• Age Range: 25-34
• Location Type: Urban
• Tech Proficiency: Advanced
🎯 Goals & Needs:
• Complete tasks efficiently
• Automate workflows
• Access advanced features
😤 Frustrations:
• Slow loading times (14/20 users)
• No keyboard shortcuts
• Limited API access
💡 Design Implications:
→ Optimize for speed and efficiency
→ Provide keyboard shortcuts and power features
→ Expose API and automation capabilities
📈 Data: Based on 45 users
Confidence: HighArchetypes Generated:
| Archetype | Signals | Design Focus |
|---|---|---|
| power_user | Daily use, 10+ features | Efficiency, customization |
| casual_user | Weekly use, 3-5 features | Simplicity, guidance |
| business_user | Work context, team use | Collaboration, reporting |
| mobile_first | Mobile primary | Touch, offline, speed |
Output Components:
| Component | Description |
|---|---|
| demographics | Age range, location, occupation, tech level |
| psychographics | Motivations, values, attitudes, lifestyle |
| behaviors | Usage patterns, feature preferences |
| needs_and_goals | Primary, secondary, functional, emotional |
| frustrations | Pain points with evidence |
| scenarios | Contextual usage stories |
| design_implications | Actionable recommendations |
| data_points | Sample size, confidence level |
| Question Type | Best Method | Sample Size |
|---|---|---|
| "What do users do?" | Analytics, observation | 100+ events |
| "Why do they do it?" | Interviews | 8-15 users |
| "How well can they do it?" | Usability test | 5-8 users |
| "What do they prefer?" | Survey, A/B test | 50+ users |
| "What do they feel?" | Diary study, interviews | 10-15 users |
| Sample Size | Confidence | Use Case |
|---|---|---|
| 5-10 users | Low | Exploratory |
| 11-30 users | Medium | Directional |
| 31+ users | High | Production |
| Severity | Definition | Action |
|---|---|---|
| 4 - Critical | Prevents task completion | Fix immediately |
| 3 - Major | Significant difficulty | Fix before release |
| 2 - Minor | Causes hesitation | Fix when possible |
| 1 - Cosmetic | Noticed but not problematic | Low priority |
| Type | Example | Use For |
|---|---|---|
| Context | "Walk me through your typical day" | Understanding environment |
| Behavior | "Show me how you do X" | Observing actual actions |
| Goals | "What are you trying to achieve?" | Uncovering motivations |
| Pain | "What's the hardest part?" | Identifying frustrations |
| Reflection | "What would you change?" | Generating ideas |
Detailed reference guides in references/:
| File | Content |
|---|---|
persona-methodology.md | Validity criteria, data collection, analysis framework |
journey-mapping-guide.md | Mapping process, templates, opportunity identification |
example-personas.md | 3 complete persona examples with data |
usability-testing-frameworks.md | Test planning, task design, analysis |
product-team/ui-design-system/) — Research findings inform design system decisionsproduct-team/product-manager-toolkit/) — Customer interview analysis complements persona research967fe01
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