UX Researcher & Designer Skill

UX Researcher & Designer

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

Trigger Terms
Workflows
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"

Clarify First

Before generating the research artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:

[ ] Which deliverable — persona, journey map, usability test plan, or research synthesis (sets which workflow and template applies)
[ ] Available data and volume — analytics/interviews/surveys and how many users (drives persona confidence and proto- vs data-driven persona)
[ ] The user goal and scope — the persona, the goal being mapped, and start/end (drives journey-map stages and research questions)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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.md for validity criteria

Proto-Persona Canvas (Lightweight Alternative)

When you lack research data but need a hypothesis-driven persona to align the team, use a proto-persona canvas. Proto-personas are assumption tools -- not validated truth -- meant to be tested and refined.

Use when: Starting a new initiative with no research budget, aligning a cross-functional team quickly, or creating a testable hypothesis about your user.

Proto-Persona Canvas Template:

### [Alliterative Name] (e.g., "Careful Carlos")

**Bio & Demographics:**
- Age, geography, social status, career stage
- Online presence, leisure activities, partner status

**Quotes** (what they say, feel, think):
- "[Direct quote capturing their perspective]"
- "[Quote revealing frustration or aspiration]"

**Pains:**
- [Pain related to the problem space]
- [Pain related to current workarounds]

**What They're Trying to Accomplish:**
- [Observable behavior 1]
- [Observable behavior 2]

**Goals** (wants, needs, dreams):
- [Short-term goal]
- [Long-term aspiration]

**Attitudes & Influences:**
- Decision Making Authority: [Can they buy/adopt your solution?]
- Decision Influencers: [Who influences their decisions?]
- Beliefs & Attitudes: [What beliefs impact their choices?]

**Assumptions to Validate:**
- [Top assumption that must be true for this persona to be viable]
- [Second assumption]
- [Third assumption]

Next steps after proto-persona:

Generate interview questions to validate assumptions (Recommended)
Generate an anti-persona to define scope boundaries
Convert into a one-page stakeholder brief

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?

Map three experience paths (not just the happy path)

| Stage | Happy Path | Fail Path | Difficult Path | |---|---|---|---| | Awareness | Finds product via search | Never discovers product | Finds competitor first | | Consideration | Clear value proposition | Confused by pricing | Needs manager approval | | Decision | Easy signup flow | Form errors, abandons | Legal review delays | | Delivery & Use | Smooth onboarding | Can't import data | Workaround needed | | Loyalty | Becomes advocate | Churns silently | Stays but complains |
- Happy Path: Everything works as designed.
- Fail Path: User cannot complete their goal and drops off.
- Difficult Path: User completes the goal but with friction, workarounds, or frustration.
Add KPIs and ownership per stage

| Stage | Leading KPI | Lagging KPI | Team Owner | |---|---|---|---| | Awareness | Site visits, ad impressions | Brand recall | Marketing | | Consideration | Demo requests, pricing page views | MQL conversion | Marketing/Sales | | Decision | Trial starts, contract sent | Close rate | Sales | | Use | Feature adoption, DAU | Retention rate | Product | | Loyalty | NPS, referral count | LTV, expansion revenue | Customer Success |
Identify top friction points and interventions

For each friction point, document:

| Friction Point | Why It Matters | Intervention | Expected Impact | Effort | Confidence | |---|---|---|---|---|---| | [Description] | [User/business impact] | [Proposed fix] | High/Med/Low | S/M/L | High/Med/Low |

Priority Score = Frequency x Severity x Solvability
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:

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.md for 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.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

Tool Reference

persona_generator.py

Generates data-driven personas from user research data, classifying users into archetypes with demographics, psychographics, behaviors, goals, frustrations, and design implications.

| Argument | Type | Default | Description | |----------|------|---------|-------------| | format | positional | (none) | Add json for JSON output; omit for human-readable |

Archetypes supported: power_user, casual_user, business_user, mobile_first

Output components: name, archetype, tagline, quote, demographics, psychographics, behaviors, needs_and_goals, frustrations, scenarios, data_points, design_implications

python scripts/persona_generator.py           # Human-readable formatted output
python scripts/persona_generator.py json      # JSON for programmatic use

Data input format (customize in script):

[{
  "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"]
}]

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | Persona confidence level is "Low" | Fewer than 20 users in sample data | Collect more data points; combine quantitative analytics with qualitative interviews | | All users classified as same archetype | Insufficient variation in input data | Ensure data includes diverse usage frequencies, devices, and contexts | | Frustrations are generic (fallback defaults) | Not enough pain_points in user data | Enrich user data with pain_points from interviews and support tickets | | Design implications too vague | Patterns don't strongly differentiate | Add more behavioral signals (features_used, session duration, task completion) | | Journey map has flat emotion curve | All stages scored similarly | Re-evaluate with actual user data; conduct contextual interviews per stage | | Usability test sample too small | Fewer than 5 participants | 5 participants find ~85% of usability issues; recruit to minimum 5 | | Research synthesis has no clear patterns | Data not coded consistently | Use consistent tagging scheme (GOAL, PAIN, BEHAVIOR, CONTEXT, QUOTE) |

Success Criteria

| Criterion | Target | How to Measure | |-----------|--------|----------------| | Persona validity | Validated by 3+ real users ("sounds like me") | Post-creation validation interviews | | Persona coverage | All key segments represented | Count of personas vs identified user segments | | Data confidence level | "High" (31+ users) | persona_generator data_points.confidence_level | | Research cadence | 5-8 interviews per segment per quarter | Count of completed research sessions | | Insight-to-action rate | >70% of findings result in design changes | Track findings through to implementation | | Usability issue resolution | All critical/major issues fixed before release | Issue severity tracking | | Journey map freshness | Updated at least quarterly | Last-updated date on each journey map |

Scope & Limitations

In scope:

Data-driven persona generation from user research
Archetype classification (power, casual, business, mobile-first)
User journey mapping frameworks
Usability test planning and scoring
Research synthesis and coding methodology
Interview question frameworks
Empathy map and opportunity identification

Out of scope:

Automated user interview recording/transcription
Real-time analytics integration (use analytics platforms)
Quantitative survey design and distribution (use Typeform/SurveyMonkey)
Eye tracking or biometric data analysis
AI-powered sentiment analysis (tool uses heuristic classification)
Persona illustration or visual asset generation
Accessibility auditing (see product-designer or design-system-lead skills)

Integration Points

| Tool / Platform | Integration Method | Use Case | |-----------------|-------------------|----------| | Dovetail / Condens | Export research data, import persona JSON | Centralize research insights | | Figma / Miro | Paste persona output as design artifact | Reference personas during design work | | Notion / Confluence | Human-readable output | Document and share personas with team | | product-manager-toolkit | Persona pain points inform RICE scoring | Connect user needs to feature prioritization | | agile-product-owner | Persona data informs user story personas | Write stories grounded in research | | product-designer | Persona feeds into journey mapping and usability test recruitment | End-to-end design research workflow |

Agent Skills: UX Researcher & Designer

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