Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
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.
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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:
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
Reference: See references/persona-methodology.md for validity criteria
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:
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?
Identify opportunities
Priority Score = Frequency × Severity × Solvability
Reference: See references/journey-mapping-guide.md for templates
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
Reference: See references/usability-testing-frameworks.md for full guide
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 wordsCluster 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:
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
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 |
| 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 researchまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
日本語の概要は準備中です。原文の説明を表示しています。
Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Conduct a comprehensive accessibility audit against WCAG guidelines with severity ratings and remediation steps.
日本語の概要は準備中です。原文の説明を表示しています。
Create accessibility testing plans covering assistive technologies and WCAG criteria.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see ads. For landing page copy, see copywriting.
日本語の概要は準備中です。原文の説明を表示しています。