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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 130% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 89% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 77% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 76% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 69% | 0% |
Generate user personas from research data, create journey maps, plan usability tests, and synthesize research findings into actionable design recommendations.
Use this skill when you need to:
Situation: You have user data (analytics, surveys, interviews) and need to create a research-backed persona.
Steps:
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"] } ]
bash # Human-readable output python scripts/persona_generator.py
# JSON output for integration python scripts/persona_generator.py json
| 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? |
references/persona-methodology.md for validity criteriaSituation: You need to visualize the end-to-end user experience for a specific goal.
Steps:
| 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 |
Sources:
Typical B2B SaaS stages: Awareness → Evaluation → Onboarding → Adoption → Advocacy
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?
Priority Score = Frequency × Severity × Solvability
references/journey-mapping-guide.md for templatesSituation: You need to validate a design with real users.
Steps:
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?" |
| 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 |
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
| Metric | Target | |--------|--------| | Completion rate | >80% | | Time on task | <2× expected | | Error rate | <15% | | Satisfaction | >4/5 |
references/usability-testing-frameworks.md for full guideSituation: You have raw research data (interviews, surveys, observations) and need actionable insights.
Steps:
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 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)
| Cluster | Users | % | Viability | |---------|-------|---|-----------| | Power Users | 18 | 36% | Primary persona | | Business Users | 15 | 30% | Primary persona | | Casual Users | 12 | 24% | Secondary persona |
For each theme:
| 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? |
references/persona-methodology.md for analysis frameworkGenerates 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: HighArchetypes 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 |
| 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 |
| Sample Size | Confidence | Use Case | |-------------|------------|----------| | 5-10 users | Low | Exploratory | | 11-30 users | Medium | Directional | | 31+ users | High | Production |
| 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 |
| 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 |
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 |
product-team/ui-design-system/) — Research findings inform design system decisionsproduct-team/product-manager-toolkit/) — Customer interview analysis complements persona researchOther measured skills in the registry, with their headline benchmark lift.