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From product-skills
Synthesizes raw user interview notes, survey responses, support tickets, or feedback into key themes, pain points, unmet needs, surprises, opportunities, and confidence levels.
npx claudepluginhub amplitude/builder-skills --plugin product-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/product-skills:craft-discovery-synthesisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**Take raw user interview notes or feedback and extract themes and insights.**
Synthesizes user research like interview transcripts, surveys, usability tests, and feedback into themes, insights, user segments, and prioritized recommendations.
Synthesizes user interview transcripts into structured research findings with themes, quotes, implications, and next steps. Use for analyzing qualitative research and turning raw data into actionable product insights.
Synthesizes user research from interviews, surveys, feedback into themes, prioritized findings by frequency/impact, and roadmap recommendations.
Share bugs, ideas, or general feedback.
Take raw user interview notes or feedback and extract themes and insights.
You've done the research — interviews, surveys, support tickets, feedback threads — and now you're staring at a wall of unstructured notes. This skill helps you pull out the patterns, themes, and actionable insights without losing the nuance.
You are an experienced product manager and UX researcher synthesizing qualitative data.
Here are the raw notes from user research:
<context>
$ARGUMENTS
</context>
> If the above is blank, ask the user: "{{PASTE YOUR INTERVIEW NOTES, SURVEY RESPONSES, SUPPORT TICKETS, OR FEEDBACK HERE}}"
Synthesize this into a research summary that includes:
1. **Key Themes** — The 3-5 most prominent patterns across the data. For each theme, include supporting quotes or examples.
2. **Pain Points** — Specific problems users described, ranked by frequency and severity.
3. **Unmet Needs** — Things users want to do but currently can't, or workarounds they've built.
4. **Surprises** — Anything unexpected that challenges our assumptions.
5. **Opportunities** — Product or design opportunities that emerge from the data.
6. **Confidence Level** — How confident should we be in these findings? Flag where more research is needed.
Preserve the user's voice — use direct quotes where they're powerful. Distinguish between what users said vs. what you're inferring. Be honest about thin data.