ux-researcher-designer – Design & UI skill for AI agents

UX Researcher & Designer

Generate user personas from research data, create journey maps, plan usability tests, and synthesize research findings into actionable design recommendations.

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Table of Contents

• [Trigger Terms](#trigger-terms)
• [Workflows](#workflows)
• [Workflow 1: Generate User Persona](#workflow-1-generate-user-persona)
• [Workflow 2: Create Journey Map](#workflow-2-create-journey-map)
• [Workflow 3: Plan Usability Test](#workflow-3-plan-usability-test)
• [Workflow 4: Synthesize Research](#workflow-4-synthesize-research)
• [Tool Reference](#tool-reference)
• [Quick Reference Tables](#quick-reference-tables)
• [Knowledge Base](#knowledge-base)

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Trigger Terms

Use this skill when you need to:

• "create user persona"
• "generate persona from data"
• "build customer journey map"
• "map user journey"
• "plan usability test"
• "design usability study"
• "analyze user research"
• "synthesize interview findings"
• "identify user pain points"
• "define user archetypes"
• "calculate research sample size"
• "create empathy map"
• "identify user needs"

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Workflows

Workflow 1: Generate User Persona

Situation: You have user data (analytics, surveys, interviews) and need to create a research-backed persona.

Steps:

1. 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"]
     }
   ]
   

2. Run persona generator

   # Human-readable output
   python scripts/persona_generator.py

   # JSON output for integration
   python scripts/persona_generator.py json
   

3. Review 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? |

4. Validate persona

• Show to 3-5 real users: "Does this sound like you?"
• Cross-check with support tickets
• Verify against analytics data

5. Reference: See references/persona-methodology.md for validity criteria

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Workflow 2: Create Journey Map

Situation: You need to visualize the end-to-end user experience for a specific goal.

Steps:

1. 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 |

2. Gather journey data

Sources:

• User interviews (ask "walk me through...")
• Session recordings
• Analytics (funnel, drop-offs)
• Support tickets

3. Map the stages

Typical B2B SaaS stages:

   Awareness → Evaluation → Onboarding → Adoption → Advocacy
   

4. Fill 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?
   

5. Identify opportunities

Priority Score = Frequency × Severity × Solvability

6. Reference: See references/journey-mapping-guide.md for templates

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Workflow 3: Plan Usability Test

Situation: You need to validate a design with real users.

Steps:

1. 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?" |

2. 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 |

3. 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

4. Define success metrics

| Metric | Target |

|--------|--------|

| Completion rate | >80% |

| Time on task | <2× expected |

| Error rate | <15% |

| Satisfaction | >4/5 |

5. Prepare moderator guide

• Think-aloud instructions
• Non-leading prompts
• Post-task questions

6. Reference: See references/usability-testing-frameworks.md for full guide

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Workflow 4: Synthesize Research

Situation: You have raw research data (interviews, surveys, observations) and need actionable insights.

Steps:

1. 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 words

2. Cluster 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)
   

3. Calculate segment sizes

| Cluster | Users | % | Viability |

|---------|-------|---|-----------|

| Power Users | 18 | 36% | Primary persona |

| Business Users | 15 | 30% | Primary persona |

| Casual Users | 12 | 24% | Secondary persona |

4. Extract key findings

For each theme:

• Finding statement
• Supporting evidence (quotes, data)
• Frequency (X/Y participants)
• Business impact
• Recommendation

5. 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? |

6. Reference: See references/persona-methodology.md for analysis framework

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Tool Reference

persona_generator.py

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: High

Archetypes 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 |

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Quick Reference Tables

Research Method Selection

| 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 |

Persona Confidence Levels

| Sample Size | Confidence | Use Case

… (full content is in the download package)