Research Synthesis
OfficialFreeTransform user research into actionable insights.
Free · Opens the source repo
What Research Synthesis does
The Research Synthesis skill is designed to help users distill complex user research data into clear themes, insights, and actionable recommendations. By utilizing various forms of qualitative data such as interview transcripts, survey results, usability test notes, support tickets, and NPS responses, this skill enables you to synthesize findings into a structured format. This is particularly useful for product managers, UX researchers, and designers who need to make informed decisions based on user feedback.
When you provide the skill with your research data, it organizes the information into a comprehensive report that includes an executive summary, key themes identified from the data, insights paired with opportunities for product improvement, user segments, and prioritized recommendations. The output is structured in a markdown format, making it easy to integrate into documentation or presentations.
Additionally, if you have connections to user feedback, product analytics, or a knowledge base, the skill can enhance its outputs by pulling in relevant data to validate findings or provide historical context. This allows for a more robust synthesis that not only highlights user pain points but also suggests data-driven solutions.
This skill is ideal for teams looking to streamline their research analysis process. By automating the synthesis of user research, it saves time and ensures that critical insights are not overlooked. It allows teams to focus on implementing changes rather than getting bogged down in data analysis.
When to use it
Use this skill when you have qualitative user research data that needs to be analyzed and summarized into actionable insights.
When not to use it
This skill may not be suitable for quantitative data analysis or when detailed statistical analysis is required.
What you can build with it
Conducting User Interviews
After conducting user interviews, use this skill to synthesize the findings into key themes and recommendations.
Analyzing Survey Results
Input survey results to identify user segments and actionable insights that can guide product development.
Summarizing Usability Tests
Utilize this skill to distill usability test notes into clear recommendations for improving user experience.
How to install Research Synthesis
View source1. Install with the skills CLI
npx skills add anthropics/knowledge-work-plugins/research-synthesis --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by anthropics/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
- Interview transcripts or notes
- Survey results (CSV, pasted data)
- Usability test recordings or notes
- Support tickets or feedback
- NPS/CSAT responses
- 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
1. **[High priority]** — [Why, based on which findings]
2. **[Medium priority]** — [Why]
3. **[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:
- Pull support tickets, feature requests, and NPS responses to supplement research data
- Cross-reference themes with real user complaints and requests
If ~~product analytics is connected:
- Validate qualitative findings with usage data and behavioral metrics
- Quantify the impact of identified pain points
If ~~knowledge base is connected:
- Search for prior research studies and findings to compare against
- Publish the synthesis to your research repository
Tips
- Include raw quotes — Direct participant quotes make insights credible and memorable.
- Separate observations from interpretations — "5 of 8 users clicked the wrong button" is an observation. "The button placement is confusing" is an interpretation.
- Quantify where possible — "Most users" is vague. "7 of 10 users" is specific.
Frequently asked questions about Research Synthesis
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