Skill: alirezarezvani/ux-researcher-designer (version 2.1.1)

--- name: "ux-researcher-designer" description: 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. --- # 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](#trigger-terms) - [Workflows](#workflows) - [Workflow 1: Generate User Persona](#workflow-1-generate-user-persona) - [Workflow 2: Create Journey Map](#workflow-2-create-journey-map) - [Workflow 3: Plan Usability Test](#workflow-3-plan-usability-test) - [Workflow 4: Synthesize Research](#workflow-4-synthesize-research) - [Tool Reference](#tool-reference) - [Quick Reference Tables](#quick-reference-tables) - [Knowledge Base](#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" --- ## 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** | 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? | 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** | 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 | 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: | 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?" | 2. **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 | 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** | Metric | Target | |--------|--------| | 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** | Cluster | Users | % | Viability | |---------|-------|---|-----------| | Power Users | 18 | 36% | Primary persona | | Business Users | 15 | 30% | Primary persona | | Casual Users | 12 | 24% | 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** | 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? | 6. **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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)