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ux-researcher-designer — Claude Code skill for UX research

This skill helps turn user data into personas, journey maps, usability test plans, and design recommendations. Use it when research findings need a clear structure.

Install into Claude Code · Plugin marketplace All methods and platforms ↓
/plugin marketplace add alirezarezvani/claude-skills
/plugin install product-skills@claude-code-skills
Open on GitHub

What ux-researcher-designer does

The skill lays out workflows for creating personas, mapping user journeys, planning usability tests, and analyzing interviews, surveys, or observations. Its research synthesis process groups recurring patterns and ties findings to supporting evidence and design recommendations.

Persona generation uses scripts/persona_generator.py. Prepare user data as JSON, then run the script for human-readable output or pass json for machine-readable output. The included guidance also covers test methods, task design, success metrics, and moderator prompts. Python is needed to run the generator.

Install it if you work with user research and want repeatable steps for turning findings into design work. If you only need a one-off interface suggestion and have no research data, much of this workflow may not apply.

What it helps with

  • turn user data into research-backed personas
  • map a user's journey from trigger to outcome
  • write tasks and success metrics for a usability test
  • cluster recurring patterns in interviews and surveys
  • connect research findings to design recommendations

Example prompts for the agent

  • Create personas from this user data in JSON.
  • Map the user journey for completing checkout.
  • Plan a usability test to validate this form.

How to install ux-researcher-designer

1Plugin marketplace

/plugin marketplace add alirezarezvani/claude-skills
/plugin install product-skills@claude-code-skills

This installs the whole product-skills plugin with all its skills

2Skills CLI (Vercel)

npx skills add alirezarezvani/claude-skills --skill ux-researcher-designer -a claude-code

3Manually: put the files in place

~/.claude/skills/ux-researcher-designer/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/ux-researcher-designer/

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Similar

Questions about ux-researcher-designer

How do I install ux-researcher-designer in Claude Code?

Run npx skills add alirezarezvani/claude-skills --skill ux-researcher-designer -a claude-code.

What does ux-researcher-designer do?

It provides workflows for persona generation, journey mapping, usability test planning, and research synthesis into design recommendations.

What do I need to generate personas?

Prepare user data in JSON and run python scripts/persona_generator.py. Pass json to get JSON output; the script requires Python.

Source text

SKILL.md · MIT
---
name: "ux-researcher-designer"
description: UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use when conducting user research, creating personas, mapping user journeys, planning usability tests, or validating designs.
---

# 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](#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)

---

## 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):
   ```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**
   ```bash
   # 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

---

### 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

---

### 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

---

### 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

---

## 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 |

---

## 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 |
|-------------|------------|----------|
| 5-10 users | Low | Exploratory |
| 11-30 users | Medium | Directional |
| 31+ users | High | Production |

### Usability Issue Severity

| 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 |

### Interview Question Types

| 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 |

---

## Knowledge Base

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 |

---

## 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

## Related Skills

- **UI Design System** (`product-team/ui-design-system/`) — Research findings inform design system decisions
- **Product Manager Toolkit** (`product-team/product-manager-toolkit/`) — Customer interview analysis complements persona research

© Alireza Rezvani · MIT · alirezarezvani/claude-skills

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