Synthesize multiple research sources into coherent insights with clear evidence chains.
/research-synthesis-engine8+ hrs → 1 hr
Compared to doing it manually
/research-synthesis-engineType this in Claude to run the skill
Research insights live in scattered docs, slides, and people's heads. When you need evidence, you can't find it. Decisions get made without the full picture.
Agent workflows chain multiple skills into one command.
/welcome/research-synthesis-engine in Claude to run the skill/jtbd-extractorExtract Jobs-to-be-Done statements from research data to uncover innovation opportunities.
/user-interview-analyzerTransform interview transcripts into structured insights with quotes, patterns, and recommendations.
/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.
Research synthesis combines findings from multiple research activities (interviews, surveys, analytics) into coherent insights. It identifies patterns, contradictions, and key takeaways that inform product decisions.
Use affinity mapping: write insights on sticky notes, group related ones, identify themes. Look for patterns across sources. Distinguish between what users say, what they do, and what they need.
Lead with insights, not methodology. Use the format: "We learned [insight] which means [implication] so we should [recommendation]." Include supporting quotes but don't overwhelm with data.
Get all 70 skills running in a system that knows your company, product, and customers.
All 70 skills + your company context in one system.