Ux Researcher Designer logo

Ux Researcher Designer

Community
viralcode
ux-researcher-designer

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

Overview

Publisherviralcode
Repositoryopenwhale
Skill nameux-researcher-designer
Stars
65
Forks
11
Bundled files
6
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by viralcode on GitHub. Read the source before you install it.

Installation

Install the Ux Researcher Designer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/viralcode/openwhale.git /tmp/openwhale
mkdir -p .claude/skills
cp -r /tmp/openwhale/skills/ux-researcher-designer .claude/skills/ux-researcher-designer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ux Researcher Designer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ux Researcher Designer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ux Researcher Designer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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):

    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

    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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ux Researcher Designer AI skill do?

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

Why use Ux Researcher Designer on TypingMind?

Because you install it once and use it with any model. Ux Researcher Designer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ux Researcher Designer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/viralcode/openwhale/tree/main/skills/ux-researcher-designer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ux Researcher Designer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ux Researcher Designer?

As many as you like. As long as a model supports skills, you can use Ux Researcher Designer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ux Researcher Designer AI skill free?

It is published on GitHub by viralcode. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇