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skill Claude Code CX Codex Cursor GitHub Copilot official · Anthropic

research-synthesis — Claude Code skill: what it does, how to install

Synthesizes interview transcripts, surveys, and support tickets into structured reports with themes, user segments, and prioritized actions.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add anthropics/knowledge-work-plugins --skill research-synthesis -a claude-code
Open on GitHub

What research-synthesis does

This skill turns raw research into a structured markdown document. It handles interview notes, survey tables, reviews, and support tickets. Direct quotes remain central. The final output includes key themes, an opportunities matrix, and identified user segments.

With connectors enabled, the skill pulls live support tickets and checks qualitative findings against product analytics. This confirms the true scale of user friction. The output format strictly separates raw observations from interpretations to keep insights factual.

Running this workflow without participant quotes weakens the final synthesis, because the recommendations depend directly on evidence.

What it helps with

  • distill interview transcripts and notes into structured themes
  • group survey feedback and support tickets into user segments
  • build an insights-to-opportunities matrix with effort scores
  • prioritize product recommendations based on direct user evidence

Example prompts for the agent

  • /research-synthesis analyze the interview notes in the transcripts folder
  • /research-synthesis summarize user feedback from tickets.csv into core themes
  • /research-synthesis generate an executive report from recent usability testing notes

How to install research-synthesis

1Skills CLI (Vercel)

npx skills add anthropics/knowledge-work-plugins --skill research-synthesis -a claude-code

2Manually: put the files in place

~/.claude/skills/research-synthesis/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/research-synthesis/

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Questions about research-synthesis

How to install research-synthesis in Claude Code?

Run npx skills add anthropics/knowledge-work-plugins --skill research-synthesis -a claude-code in your terminal.

What inputs does research-synthesis accept?

It accepts interview transcripts, survey exports, usability session notes, support tickets, CSAT responses, and app store reviews.

How does research-synthesis use external connectors?

External connectors allow the skill to validate user friction points against product analytics and check prior findings stored in your knowledge base.

Source text

SKILL.md · Apache-2.0
---
name: research-synthesis
description: Synthesize user research into themes, insights, and recommendations. Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps.
argument-hint: "<research data, transcripts, or survey results>"
---

# /research-synthesis

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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

```markdown
## 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.

© anthropics · Apache-2.0 · anthropics/knowledge-work-plugins

See also

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