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research-synthesis

by @anthropicsv
4.4(20)

面向用户体验研究员和产品经理的研究综合工具。将访谈记录、问卷结果、可用性测试笔记等多源研究数据转化为结构化洞察。自动提取主题、识别用户细分、生成洞察-机会矩阵,并提供基于证据的产品改进建议。适用于敏捷研究周期,支持多格式数据输入。

researchuxproduct-managementdata-analysisinterviewsGitHub
安装方式
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill research-synthesis
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Before / After 效果对比

1 组
使用前

在没有 `research-synthesis` 技能时,研究人员需要手动处理大量的原始研究数据,包括访谈记录、调查结果和用户反馈。这涉及耗时的人工阅读、标记和归纳,以识别关键主题和模式。研究人员必须逐一提取支持性引证,并手动计算主题的普遍性。随后,还需要自行设计并填充洞察-机会矩阵、用户细分表和建议列表。整个报告的结构、格式和一致性都依赖于人工操作,耗费大量精力,且容易因数据量大而遗漏关键信息或引入主观偏差,导致报告生成周期长、效率低下。

使用后

使用 `research-synthesis` 技能后,研究人员只需将原始数据(如访谈记录、调查结果、用户反馈)作为输入提供。该技能将自动进行数据综合分析,智能提取关键主题、计算其普遍性,并提供支持性引证。它能快速生成一个结构化的研究报告,包含执行摘要、关键主题、洞察-机会矩阵、用户细分、建议和待研究问题等预定义部分。如果连接了其他工具(如用户反馈、产品分析),还能自动整合更多数据源,验证定性发现。这极大地减少了手动工作量,加速了报告生成过程,确保了报告的全面性、一致性和专业性,使研究人员能专注于更高价值的战略思考。

SKILL.md

research-synthesis

/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.

Weekly Installs266Repositoryanthropics/know…-pluginsGitHub Stars10.3KFirst Seen12 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex257gemini-cli256opencode255cursor255kimi-cli254github-copilot254

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统计数据

安装量4.0K
评分4.4 / 5.0
版本
更新日期2026年10月17日
对比案例1 组

用户评分

4.4(20)
5
25%
4
55%
3
20%
2
0%
1
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创建2026年3月26日
最后更新2026年10月17日
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