
UX Researcher & Designer
FreeA comprehensive toolkit for UX research and design.
Free · Opens the source repo
What UX Researcher & Designer does
The UX Researcher & Designer skill provides a structured approach for UX professionals to enhance their research and design processes. It includes tools for generating data-driven user personas, creating detailed journey maps, planning effective usability tests, and synthesizing research findings into actionable insights. This skill is particularly useful for Senior UX Designers and Researchers who need to validate their designs and understand user needs through empirical data.
With this skill, you can generate user personas from various research data sources such as analytics, surveys, and interviews. The persona generator takes user data in JSON format and produces human-readable outputs that help in understanding user demographics, goals, and pain points. This ensures that your design decisions are grounded in real user data rather than assumptions.
The journey mapping functionality allows you to visualize the entire user experience from start to finish. By defining the scope and gathering relevant data, you can identify user actions, emotions, and pain points at each stage of their journey. This helps in pinpointing areas for improvement and enhancing the overall user experience.
Additionally, the skill provides a framework for planning usability tests, enabling you to transform vague design goals into testable research questions. It outlines methods for conducting tests, designing tasks, and defining success metrics, ensuring that you gather meaningful feedback from users. Finally, the research synthesis feature aids in coding and clustering raw data, allowing you to derive actionable insights from user research effectively.
When to use it
Use this skill when you need to conduct user research, create personas, or validate designs based on user feedback.
When not to use it
This skill may not be suitable for quick design iterations without user data or in scenarios where user research is not feasible.
What you can build with it
Creating User Personas
Use the persona generator to create detailed user personas from your research data, ensuring your designs are user-centered.
Mapping User Journeys
Visualize the user experience by creating journey maps that highlight user actions, emotions, and pain points.
Planning Usability Tests
Define and execute usability tests to validate design decisions and gather user feedback effectively.
How to install UX Researcher & Designer
View source1. Install with the skills CLI
npx skills add alirezarezvani/claude-skills/ux-researcher-designer --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by alirezarezvaniUX 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
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:
-
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 json -
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? -
Validate persona
- Show to 3-5 real users: "Does this sound like you?"
- Cross-check with support tickets
- Verify against analytics data
-
Reference: See
references/persona-methodology.mdfor validity criteria
Workflow 2: Create Journey Map
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:
- User interviews (ask "walk me through...")
- Session recordings
- Analytics (funnel, drop-offs)
- Support tickets
-
Map the stages
Typical B2B SaaS stages:
Awareness → Evaluation → Onboarding → Adoption → Advocacy -
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? -
Identify opportunities
Priority Score = Frequency × Severity × Solvability
-
Reference: See
references/journey-mapping-guide.mdfor templates
Workflow 3: Plan Usability Test
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
- Think-aloud instructions
- Non-leading prompts
- Post-task questions
-
Reference: See
references/usability-testing-frameworks.mdfor full guide
Workflow 4: Synthesize Research
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 words
-
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) -
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:
- Finding statement
- Supporting evidence (quotes, data)
- Frequency (X/Y participants)
- Business impact
- Recommendation
-
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.mdfor 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
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