ux-researcher-designer（用户体验研究员设计师）

UX Researcher & Designer

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

Table of Contents

• Trigger Terms
• Workflows

- Workflow 1: Generate User Persona - Workflow 2: Create Journey Map - Workflow 3: Plan Usability Test - Workflow 4: Synthesize Research

• Tool Reference
• Quick Reference Tables
• Knowledge Base

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"

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"]
     }
   ]

1. Run persona generator

   # Human-readable output
   python scripts/persona_generator.py

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

1. Review generated components

1. Validate persona

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

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

Workflow 2: Create Journey Map

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

Steps:

1. Define scope

1. Gather journey data

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

1. Map the stages

Typical B2B SaaS stages:

   Awareness → Evaluation → Onboarding → Adoption → Advocacy

1. 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?

1. Identify opportunities

Priority Score = Frequency × Severity × Solvability

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

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:

1. Select method

1. 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

1. Define success metrics

1. Prepare moderator guide

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

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

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

1. 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)

1. Calculate segment sizes

1. Extract key findings

For each theme: - Finding statement - Supporting evidence (quotes, data) - Frequency (X/Y participants) - Business impact - Recommendation

1. Prioritize opportunities

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

Tool Reference

persona_generator.py

Generates data-driven personas from user research data.

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:

Output Components:

Quick Reference Tables

Research Method Selection

Persona Confidence Levels

Usability Issue Severity

Interview Question Types

Knowledge Base

Detailed reference guides in references/:

Validation Checklist

Persona Quality

• [ ] Based on 20+ users (minimum)
• [ ] At least 2 data sources (quant + qual)
• [ ] Specific, actionable goals
• [ ] Frustrations include frequency counts
• [ ] Design implications are specific
• [ ] Confidence level stated

Journey Map Quality

• [ ] Scope clearly defined (persona, goal, timeframe)
• [ ] Based on real user data, not assumptions
• [ ] All layers filled (actions, touchpoints, emotions)
• [ ] Pain points identified per stage
• [ ] Opportunities prioritized

Usability Test Quality

• [ ] Research questions are testable
• [ ] Tasks are realistic scenarios, not instructions
• [ ] 5+ participants per design
• [ ] Success metrics defined
• [ ] Findings include severity ratings

Research Synthesis Quality

• [ ] Data coded consistently
• [ ] Patterns based on 3+ data points
• [ ] Findings include evidence
• [ ] Recommendations are actionable
• [ ] Priorities justified