首页/数据 & AI/research-synthesis
R

research-synthesis

by @anthropicsv1.0.0
4.1(6)

综合分析和整理研究成果,提取关键信息并生成结构化报告

researchsynthesispythonmachine-learningdataGitHub
安装方式
npx skills add anthropics/knowledge-work-plugins --skill research-synthesis
compare_arrows

Before / After 效果对比

1
使用前

手动完成综合分析和整理研究成果,提取关相关任务,需要反复查阅文档和调试,整个过程大约需要52分钟,容易出错且效率低下

使用后

使用该 Skill 自动化处理,3分钟内完成全部工作,流程标准化且准确率高

description 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

forum用户评价 (0)

发表评价

效果
易用性
文档
兼容性

暂无评价,来写第一条吧

统计数据

安装量201
评分4.1 / 5.0
版本1.0.0
更新日期2026年3月25日
对比案例1 组

用户评分

4.1(6)
5
0%
4
0%
3
0%
2
0%
1
0%

为此 Skill 评分

0.0

兼容平台

🔧Claude Code

时间线

创建2026年3月26日
最后更新2026年3月25日