SKILL.md
User Research Analysis
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
Overview
Effective research analysis transforms raw data into actionable insights that guide product development and design.
When to Use
- Synthesis of user interviews and surveys
- Identifying patterns and themes
- Validating design assumptions
- Prioritizing user needs
- Communicating insights to stakeholders
- Informing design decisions
Quick Start
Minimal working example:
# Analyze qualitative and quantitative data
class ResearchAnalysis:
def synthesize_interviews(self, interviews):
"""Extract themes and insights from interviews"""
return {
'interviews_analyzed': len(interviews),
'methodology': 'Thematic coding and affinity mapping',
'themes': self.identify_themes(interviews),
'quotes': self.extract_key_quotes(interviews),
'pain_points': self.identify_pain_points(interviews),
'opportunities': self.identify_opportunities(interviews)
}
def identify_themes(self, interviews):
"""Find recurring patterns across interviews"""
themes = {}
theme_frequency = {}
for interview in interviews:
for statement in interview['statements']:
theme = self.categorize_statement(statement)
theme_frequency[theme] = theme_frequency.get(theme, 0) + 1
# Sort by frequency
// ... (see reference guides for full implementation)Reference Guides
Detailed implementations in the references/ directory:
Guide
Contents
Research Synthesis Methods
Affinity Mapping
Insight Documentation
Research Validation Matrix
Best Practices
✅ DO
- Use multiple research methods
- Triangulate findings across sources
- Document quotes and evidence
- Look for patterns and frequency
- Separate findings from interpretation
- Validate findings with users
- Share insights across team
- Connect to design decisions
- Document methodology
- Iterate research approach based on learnings
❌ DON'T
- Over-interpret small samples
- Ignore conflicting data
- Base decisions on single data point
- Skip documentation
- Cherry-pick quotes that support assumptions
- Present without supporting evidence
- Forget to note limitations
- Analyze without involving participants
- Create insights without actionable recommendations
- Let research sit unused