tomevault-io UX Researcher & Designer
Comprehensive toolkit for user-centered research and experience design with data-driven approaches.
Overview
This skill provides professional UX research and design capabilities including persona generation from real user data, customer journey mapping, usability testing frameworks, and research synthesis methods.
When to Use This Skill
- Creating data-driven user personas
- Analyzing user behavior patterns and insights
- Mapping customer journeys and touchpoints
- Conducting usability research and validation
- Synthesizing research data into actionable insights
- Generating design implications from user data
Core Capabilities
1. Data-Driven Persona Generation
Create research-backed personas from quantitative and qualitative user data:
- Analyze user behavior patterns from analytics
- Identify persona archetypes automatically
- Extract psychographics (motivations, values, attitudes)
- Generate realistic usage scenarios
- Provide confidence scoring based on sample size
- Derive design implications from persona data
2. Persona Archetypes
Built-in archetype templates for common user types:
- Power User: Tech-savvy, frequent user, efficiency-focused
- Casual User: Occasional user, values simplicity and ease-of-use
- Business User: ROI-focused, team collaboration oriented
- Mobile First: Primarily mobile usage, on-the-go access
3. Customer Journey Mapping
Map complete user journeys with:
- Touchpoint identification
- Pain point analysis
- Opportunity discovery
- Emotion mapping across journey stages
4. Usability Testing Frameworks
Structured approaches for:
- Test plan creation
- Success metrics definition
- User task scenarios
- Observation frameworks
5. Research Synthesis
Transform raw research into insights:
- Pattern identification across users
- Theme extraction from interviews
- Quantitative and qualitative data integration
- Actionable design recommendations
Available Tools
Python Script: persona_generator.py
Location: scripts/persona_generator.py
Purpose: Generate comprehensive, data-driven user personas from user data and optional interview insights.
Usage:
# JSON output
python scripts/persona_generator.py json
# Human-readable output
python scripts/persona_generator.py
Features:
- Analyzes usage frequency, feature usage, device patterns
- Identifies persona archetype based on behavior patterns
- Aggregates demographics (age, location, tech proficiency)
- Extracts psychographics (motivations, values, lifestyle)
- Generates usage scenarios with context and pain points
- Calculates confidence level based on sample size
- Provides design implications for each persona
Input Data Structure:
user_data = [
{
'user_id': 'user_1',
'age': 28,
'usage_frequency': 'daily', # daily, weekly, monthly
'features_used': ['dashboard', 'reports', 'settings'],
'primary_device': 'desktop', # desktop, mobile, tablet
'usage_context': 'work', # work, personal
'tech_proficiency': 7, # 1-10 scale
'pain_points': ['slow loading', 'confusing UI']
}
# ... more users
]
Output Includes:
- Persona name and archetype
- Demographics and psychographics
- Goals, needs, and frustrations
- Behavior patterns and feature preferences
- Usage scenarios with pain points
- Design implications
- Data confidence metrics
TypeScript Script: persona-generator.ts
Location: scripts/persona-generator.ts
Purpose: Same functionality as Python version, integrated with Node.js/TypeScript ecosystem.
Usage:
# JSON output
ts-node scripts/persona-generator.ts --format=json
# Pretty output
ts-node scripts/persona-generator.ts
TypeScript Advantages:
- Type-safe data structures
- Better IDE integration
- Easy integration with existing Node.js projects
- Modern async/await patterns
Design Implications Framework
Each generated persona includes specific design implications based on their characteristics:
For High-Frequency Users:
- Optimize for speed and efficiency
- Provide keyboard shortcuts and power features
- Minimize friction in common workflows
For Casual Users:
- Focus on discoverability and clear guidance
- Simplify onboarding experience
- Reduce cognitive load
For Mobile Users:
- Mobile-first responsive design
- Touch-optimized interactions
- Offline capability considerations
For Business Users:
- Professional visual design
- Enterprise features (SSO, audit logs, team management)
- Integration with business tools
Best Practices
Data Collection
- Minimum Sample Size: 20+ users for Medium confidence, 50+ for High confidence
- Mix Methods: Combine quantitative analytics with qualitative interviews
- Regular Updates: Refresh personas quarterly or after major product changes
- Validation: Test personas against real user behavior continuously
Persona Usage
- Share Widely: Make personas accessible to entire product team
- Reference Often: Use in feature discussions and design reviews
- Update Regularly: Keep personas current as user base evolves
- Measure Impact: Track how persona-driven decisions affect metrics
Research Synthesis
- Document Everything: Keep detailed notes from all research activities
- Look for Patterns: Identify recurring themes across multiple users
- Prioritize Insights: Focus on actionable findings with high impact
- Validate Assumptions: Test hypotheses with additional research
Context Levels
Level 1 - Minimal (Always Loaded)
- Core UX research principles
- Persona archetype definitions
- Basic usage patterns
Level 2 - Detailed (Load on Request)
- Complete persona generation methodology
- Journey mapping frameworks
- Usability testing templates
- Research synthesis techniques
Level 3 - Full (Scripts and Examples)
- Working Python and TypeScript scripts
- Sample data structures
- Complete persona examples
- Integration code samples
Integration with Other Skills
Works well with:
- ui-design-system: Use personas to inform design token decisions
- frontend-specialist: Apply persona insights to component design
- testing-fundamentals: Create test scenarios based on persona behaviors
- accessibility-specialist: Ensure designs work for all persona types
References
UX Research Methods:
- Nielsen Norman Group UX Research Guidelines
- IDEO Human-Centered Design Toolkit
- Google Design Sprint Methodology
Persona Creation:
- Alan Cooper's "The Inmates Are Running the Asylum"
- Data-Driven Personas (Jansen et al.)
- Jobs To Be Done Framework
Journey Mapping:
- Adaptive Path's Guide to Experience Mapping
- Service Design Toolkit
Version History
v1.0.0 - Initial release
- Data-driven persona generation
- Python and TypeScript implementations
- 4 persona archetype templates
- Design implications framework
- Confidence scoring system
Maintained by: Primadata Enhanced Toolkit Source: Based on claude-skills repository by Alireza Rezvani Last Updated: November 2025
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