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Skills/Testing & QA
anthropics avatar

Research Synthesis

anthropics/knowledge-work-pluginsOfficial

Takes messy user research data like interview transcripts, survey results, or support tickets and turns it into structured themes with quotes, user segments, and prioritized recommendations. The output format is solid: executive summary, key themes with prevalence counts, an insights-to-opportunities matrix with impact/effort scoring, and clear next steps. Works best when you have qualitative data that needs patterns pulled out. If you have analytics or a knowledge base connected, it'll cross-reference findings with usage data or pull in prior studies. The "separate observations from interpretations" tip is worth keeping in mind since it's easy to blur those lines when synthesizing.

installs
4.1K
GitHub stars
33.3K
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add anthropics/knowledge-work-plugins --skill research-synthesis --agent claude-code

Installs into .claude/skills of the current project.

Files

/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

  1. Include raw quotes — Direct participant quotes make insights credible and memorable.
  2. Separate observations from interpretations — "5 of 8 users clicked the wrong button" is an observation. "The button placement is confusing" is an interpretation.
  3. Quantify where possible — "Most users" is vague. "7 of 10 users" is specific.
SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/anthropics/knowledge-work-plugins
Categories
Testing & QA, Data Science & ML
First seen
Apr 16, 2026

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