
User Research Analysis
Turns user interviews, surveys, and research data into structured insights using thematic coding and affinity mapping. You'll use this when you need to find patterns across multiple research sessions, extract meaningful quotes, identify pain points, and build actionable recommendations for your product team. The emphasis on triangulating findings across sources and separating raw findings from interpretation is solid advice that keeps analysis honest. It includes templates for research synthesis, validation matrices, and insight documentation. The do's and don'ts are practical reminders about sample size and the danger of cherry-picking quotes to support what you already believe.
- skills.sh installs
- 618
- GitHub stars
- 379
- last updated
- Aug 21, 2026
Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill user-research-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Files
User Research Analysis
Table of Contents
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)
}
Sponsors
Details
- Repository
- github.com/aj-geddes/useful-ai-prompts
- Categories
- AI & Agent Building, Data Science & ML, Design & UI/UX
- First seen
- Jun 3, 2026
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