Research Synthesis
AnthropicOfficialPublished by Anthropic
- Skill
- Content generator
- Business and productivity
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The research-synthesis skill provides a structured framework for distilling qualitative and quantitative user data into a professional research report. It processes diverse inputs such as interview transcripts, survey results, and support tickets to identify recurring patterns, define user segments, and map findings to actionable product opportunities. By enforcing a consistent output format, it ensures that research findings are credible, evidence-based, and ready for stakeholder review.
What it does
- Extracts key themes and patterns from raw research data
- Generates an executive summary of research findings
- Maps insights to specific product opportunities with impact and effort assessments
- Identifies and characterizes distinct user segments
- Prioritizes recommendations based on research evidence
- Suggests follow-up questions to address knowledge gaps
When to use it
- After completing a round of user interviews or usability testing
- When you need to make sense of a large volume of survey or NPS responses
- When consolidating customer support tickets to identify product pain points
- Before planning a product roadmap to ensure features are based on user needs
Advantages
- Standardizes the presentation of research findings
- Encourages evidence-based decision making by linking insights to raw quotes
- Helps distinguish between raw observations and researcher interpretations
- Facilitates prioritization through structured impact and effort analysis
Usage examples
- “Synthesize these 10 interview transcripts from our recent onboarding study.”
- A structured report containing key themes, identified user segments, and a prioritized list of onboarding improvements.
- “Analyze these survey results and support tickets to find the top 3 pain points.”
- A summary table mapping specific user complaints to potential product opportunities and their estimated impact.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/research-synthesis/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip research-synthesis.zip -d .agents/skills
Documentation by Anthropic
/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 $ARGUMENTSWhat 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer909 B
- LICENSELicence file added by AutoGineer11.3 KiB
- SKILL.md2.9 KiB
SKILL.md
/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 $ARGUMENTSWhat 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.
Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · design/skills/research-synthesis · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anthropic.
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download research-synthesis.zip