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UX Researcher & Designer

Free

A comprehensive toolkit for UX research and design.

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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

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1. Install with the skills CLI

npx skills add alirezarezvani/claude-skills/ux-researcher-designer --agent claude-code

2. 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 alirezarezvani

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

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"]
      }
    ]
    
  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

    ComponentWhat to Check
    ArchetypeDoes it match the data patterns?
    DemographicsAre they derived from actual data?
    GoalsAre they specific and actionable?
    FrustrationsDo they include frequency counts?
    Design implicationsCan 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

    ElementDescription
    PersonaWhich user type
    GoalWhat they're trying to achieve
    StartTrigger that begins journey
    EndSuccess criteria
    TimeframeHours/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:

    VagueTestable
    "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

    MethodParticipantsDurationBest For
    Moderated remote5-845-60 minDeep insights
    Unmoderated remote10-2015-20 minQuick validation
    Guerrilla3-55-10 minRapid 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

    MetricTarget
    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

    ClusterUsers%Viability
    Power Users1836%Primary persona
    Business Users1530%Primary persona
    Casual Users1224%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

    FactorScore 1-5
    FrequencyHow often does this occur?
    SeverityHow much does it hurt?
    BreadthHow many users affected?
    SolvabilityCan 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.

ArgumentValuesDefaultDescription
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:

ArchetypeSignalsDesign Focus
power_userDaily use, 10+ featuresEfficiency, customization
casual_userWeekly use, 3-5 featuresSimplicity, guidance
business_userWork context, team useCollaboration, reporting
mobile_firstMobile primaryTouch, offline, speed

Output Components:

ComponentDescription
demographicsAge range, location, occupation, tech level
psychographicsMotivations, values, attitudes, lifestyle
behaviorsUsage patterns, feature preferences
needs_and_goalsPrimary, secondary, functional, emotional
frustrationsPain points with evidence
scenariosContextual usage stories
design_implicationsActionable recommendations
data_pointsSample size, confidence level

Quick Reference Tables

Research Method Selection

Question TypeBest MethodSample Size
"What do users do?"Analytics, observation100+ events
"Why do they do it?"Interviews8-15 users
"How well can they do it?"Usability test5-8 users
"What do they prefer?"Survey, A/B test50+ users
"What do they feel?"Diary study, interviews10-15 users

Persona Confidence Levels

Sample SizeConfidenceUse Case
5-10 usersLowExploratory
11-30 usersMediumDirectional
31+ usersHighProduction

Usability Issue Severity

SeverityDefinitionAction
4 - CriticalPrevents task completionFix immediately
3 - MajorSignificant difficultyFix before release
2 - MinorCauses hesitationFix when possible
1 - CosmeticNoticed but not problematicLow priority

Interview Question Types

TypeExampleUse 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/:

FileContent
persona-methodology.mdValidity criteria, data collection, analysis framework
journey-mapping-guide.mdMapping process, templates, opportunity identification
example-personas.md3 complete persona examples with data
usability-testing-frameworks.mdTest 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

Frequently asked questions about UX Researcher & Designer

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