=== CORE IDENTITY ===

name: ux-researcher-designer title: UX Researcher Designer Skill Package 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. domain: product subdomain: ux-design

=== WEBSITE DISPLAY ===

difficulty: intermediate time-saved: "TODO: Quantify time savings" frequency: "TODO: Estimate usage frequency" use-cases:

• Primary workflow for Ux Researcher Designer
• Analysis and recommendations for ux researcher designer tasks
• Best practices implementation for ux researcher designer
• Integration with related skills and workflows

=== RELATIONSHIPS ===

related-agents: [] related-skills: [] related-commands: [] orchestrated-by: []

=== TECHNICAL ===

dependencies: scripts: [] references: [] assets: [] compatibility: python-version: 3.8+ platforms: [macos, linux, windows] tech-stack:

• Python 3.8+
• CLI
• JSON processing
• User data analysis
• JSON export

=== EXAMPLES ===

examples:

title: Example Usage
input: "TODO: Add example input for ux-researcher-designer"
output: "TODO: Add expected output"

=== ANALYTICS ===

stats: downloads: 0 stars: 0 rating: 0.0 reviews: 0

=== VERSIONING ===

version: v1.0.0 author: Claude Skills Team contributors: [] created: 2025-10-19 updated: 2025-11-08 license: MIT

=== DISCOVERABILITY ===

tags: [data, design, designer, product, researcher, testing] featured: false verified: true

UX Researcher & Designer

Overview

This skill provides [TODO: Add 2-3 sentence overview].

Core Value: [TODO: Add value proposition with metrics]

Target Audience: [TODO: Define target users]

Use Cases: [TODO: List 3-5 primary use cases]

Core Capabilities

• [Capability 1] - [Description]
• [Capability 2] - [Description]
• [Capability 3] - [Description]
• [Capability 4] - [Description]

Key Workflows

Workflow 1: [Workflow Name]

Time: [Duration estimate]

Steps:

1. [Step 1]
2. [Step 2]
3. [Step 3]

Expected Output: [What success looks like]

Workflow 2: [Workflow Name]

Time: [Duration estimate]

Steps:

1. [Step 1]
2. [Step 2]
3. [Step 3]

Expected Output: [What success looks like]

Comprehensive toolkit for user-centered research and experience design. This skill provides Python tools for persona generation, research frameworks for validation, and battle-tested templates for interviews and journey mapping.

What This Skill Provides:

• Data-driven persona generator from user research
• User research methodologies (interviews, usability testing)
• Journey mapping and Jobs-to-be-Done frameworks
• Design validation methods (prototypes, A/B tests)
• Accessibility compliance frameworks (WCAG 2.1)

Best For:

• Conducting user research and synthesis
• Creating research-backed personas
• Journey mapping and empathy building
• Usability testing and validation
• Ensuring accessible design

Quick Start

Generate Personas

# Interactive mode
python scripts/persona_generator.py

# From user data
python scripts/persona_generator.py --data user_research.json

# Filter by segment
python scripts/persona_generator.py --data user_data.json --segment "premium"

Persona Components

Demographics: Age, role, company, technical proficiency Goals: Primary objectives and motivations Pain Points: Frustrations and challenges Behaviors: Usage patterns and preferences JTBD: Jobs-to-be-done framework

See frameworks.md for complete persona development framework.

Core Workflows

1. User Research Process

Steps:

1. Define research questions
2. Recruit participants (5-8 per cohort)
3. Conduct interviews (30-45 min each)
4. Synthesize findings
5. Generate personas: python scripts/persona_generator.py --data research.json
6. Validate with stakeholders

Research Methods:

• Qualitative: Interviews, usability testing, field studies
• Quantitative: Surveys, analytics, A/B tests
• Mixed: Combine both for comprehensive insights

Interview Structure:

• Introduction (5 min)
• Background (5 min)
• Problem exploration (20 min)
• Solution validation (10 min)
• Wrap-up (5 min)

Detailed Methods: See frameworks.md for qualitative and quantitative research frameworks.

Templates: See templates.md for interview scripts and usability test plans.

2. Persona Creation Process

Steps:

1. Collect user data (interviews, surveys, analytics)
2. Format as JSON input
3. Generate personas: python scripts/persona_generator.py --data user_research.json
4. Segment by user type (enterprise, SMB, individual)
5. Validate with real users
6. Update quarterly with new data

Persona Components:

• Demographics and psychographics
• Goals and motivations
• Pain points and frustrations
• Behavior patterns
• Jobs-to-be-done
• Representative quotes

Confidence Scoring:

• High: Based on 15+ interviews
• Medium: Based on 8-14 interviews
• Low: Based on <8 interviews

Detailed Framework: See frameworks.md for persona development and Jobs-to-be-Done framework.

Templates: See templates.md for persona template and journey map format.

3. Design Validation Process

Methods:

• Prototype Testing: Low/mid/high-fidelity testing
• Usability Testing: Task-based scenarios with 5-8 users
• A/B Testing: Quantitative validation of design decisions
• Design Critiques: Structured feedback sessions

Usability Test Structure:

1. Plan (research questions, success metrics)
2. Recruit (5-8 participants per round)
3. Execute (45-50 min sessions)
4. Analyze (severity rating, prioritization)
5. Iterate (implement fixes, retest)

Severity Rating:

• Critical: Prevents task completion
• High: Causes significant frustration
• Medium: Minor inconvenience
• Low: Cosmetic issue

Detailed Frameworks: See frameworks.md for usability testing and validation methods.

Templates: See templates.md for usability test plan template.

Python Tools

persona_generator.py

Data-driven persona generation from user research.

Key Features:

• Demographic and psychographic profiling
• Goals and pain points extraction
• Behavior pattern identification
• Jobs-to-be-done analysis
• Confidence scoring based on sample size
• Multiple output formats (text, JSON, CSV)

Usage:

# Interactive persona creation
python3 scripts/persona_generator.py

# From user research JSON
python3 scripts/persona_generator.py --data user_research.json

# Filter by segment
python3 scripts/persona_generator.py --data user_data.json --segment "enterprise"

# JSON output
python3 scripts/persona_generator.py --data user_research.json --output json

# Save to file
python3 scripts/persona_generator.py --data user_research.json -o json -f personas.json

# Verbose mode
python3 scripts/persona_generator.py --data user_research.json -v

Generated Persona Includes:

• Name and archetype
• Demographics (age, role, company, industry)
• Goals (primary objectives)
• Pain points (frustrations)
• Behaviors (usage patterns)
• Jobs-to-be-done (JTBD framework)
• Representative quote
• Confidence level (based on sample size)

Input Format:

• JSON file with user research data
• Demographics, behaviors, goals, pain points, quotes
• Multiple users per segment

Complete Documentation: See tools.md for full usage guide, input formats, and integration patterns.

Reference Documentation

Frameworks (frameworks.md)

Comprehensive research and design frameworks:

• User Research Methods: Qualitative and quantitative approaches
• Persona Development: JTBD, persona components, validation criteria
• Journey Mapping: Customer journey stages, map components, insights
• Usability Testing: Test planning, execution, severity rating
• Accessibility Framework: WCAG 2.1 principles, compliance checklist
• Design Validation: Prototype testing, A/B testing, design critiques

Templates (templates.md)

Ready-to-use templates:

• User Interview Script: Complete interview guide with questions
• Persona Template: Comprehensive persona format
• Journey Map Template: Multi-stage journey mapping format
• Usability Test Plan: Complete test plan with scenarios

Tools (tools.md)

Python tool documentation:

• persona_generator.py: Complete usage guide
• Command-Line Options: All flags and parameters
• Input Format: User research JSON structure
• Generated Output: Persona format examples
• Integration Patterns: Figma, documentation, research synthesis
• Best Practices: DO/DON'T guidelines

Integration Points

This toolkit integrates with:

• Design Tools: Figma, Sketch, Miro (personas and journey maps)
• Research Tools: Dovetail, UserVoice, Maze, Optimal Workshop
• Analytics: Amplitude, Mixpanel, Hotjar, FullStory
• Testing: UserTesting.com, Lookback, UserZoom
• Documentation: Confluence, Notion, Airtable

See tools.md for detailed integration workflows.

Quick Commands

# Interactive persona creation
python scripts/persona_generator.py

# From user research data
python scripts/persona_generator.py --data user_research.json

# By segment
python scripts/persona_generator.py --data user_data.json --segment "enterprise"
python scripts/persona_generator.py --data user_data.json --segment "smb"

# Export formats
python scripts/persona_generator.py --data research.json -o json -f personas.json
python scripts/persona_generator.py --data research.json -o csv -f personas.csv

# Verbose output
python scripts/persona_generator.py --data research.json -v