Transform interview transcripts into structured insights with quotes, patterns, and recommendations.
/user-interview-analyzer1-2 hrs → 15 min
Compared to doing it manually
/user-interview-analyzerType this in Claude to run the skill
Interview notes sit unprocessed. Insights fade. By the time you write them up, you've forgotten the nuance. Opportunities slip through the cracks.
Agent workflows chain multiple skills into one command.
/welcome/user-interview-analyzer in Claude to run the skill/jtbd-extractorExtract Jobs-to-be-Done statements from research data to uncover innovation opportunities.
/feedback-categorizerAnalyze and categorize customer feedback into actionable themes using affinity mapping.
/app-review-analyzerExtract themes, complaints, and feature requests from app reviews at scale.
/feature-request-analyzerTurn scattered feature requests into a prioritized list based on actual demand.
Look for patterns across interviews: recurring themes, common frustrations, unexpected insights. Tag quotes by topic, then synthesize into key findings. Focus on behaviors and motivations, not just what users say they want.
For most discovery research, 5-8 interviews reveal 80% of usability issues. For deeper research, 12-15 interviews. You've done enough when you stop hearing new information (saturation).
Users often say one thing ("I'd pay for that!") but do another (don't actually pay). Triangulate: combine interview insights with behavioral data. Watch what they do, not just what they say.
Get all 70 skills running in a system that knows your company, product, and customers.
All 70 skills + your company context in one system.