/research-synthesis
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Synthesize user research data into actionable insights. See the user-research skill for research methods, interview guides, and analysis frameworks.
Usage
/research-synthesis $ARGUMENTS
What I Accept
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Interview transcripts or notes
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Survey results (CSV, pasted data)
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Usability test recordings or notes
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Support tickets or feedback
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NPS/CSAT responses
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App store reviews
Output
Research Synthesis: [Study Name]
Method: [Interviews / Survey / Usability Test] | Participants: [X] Date: [Date range] | Researcher: [Name]
Executive Summary
[3-4 sentence overview of key findings]
Key Themes
Theme 1: [Name]
Prevalence: [X of Y participants] Summary: [What this theme is about] Supporting Evidence:
- "[Quote]" — P[X]
- "[Quote]" — P[X] Implication: [What this means for the product]
Theme 2: [Name]
[Same format]
Insights → Opportunities
| Insight | Opportunity | Impact | Effort |
|---|---|---|---|
| [What we learned] | [What we could do] | High/Med/Low | High/Med/Low |
User Segments Identified
| Segment | Characteristics | Needs | Size |
|---|---|---|---|
| [Name] | [Description] | [Key needs] | [Rough %] |
Recommendations
- [High priority] — [Why, based on which findings]
- [Medium priority] — [Why]
- [Lower priority] — [Why]
Questions for Further Research
- [What we still don't know]
Methodology Notes
[How the research was conducted, any limitations or biases to note]
If Connectors Available
If ~~user feedback is connected:
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Pull support tickets, feature requests, and NPS responses to supplement research data
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Cross-reference themes with real user complaints and requests
If ~~product analytics is connected:
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Validate qualitative findings with usage data and behavioral metrics
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Quantify the impact of identified pain points
If ~~knowledge base is connected:
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Search for prior research studies and findings to compare against
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Publish the synthesis to your research repository
Tips
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Include raw quotes — Direct participant quotes make insights credible and memorable.
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Separate observations from interpretations — "5 of 8 users clicked the wrong button" is an observation. "The button placement is confusing" is an interpretation.
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Quantify where possible — "Most users" is vague. "7 of 10 users" is specific.