基于 Google Scholar API 的学术文献搜索工具，提供学术论文、研究报告、技术文献的专业搜索能力。
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionserper-scholarExecute the skills CLI command in your project's root directory to begin installation:
Fetches serper-scholar from fanzhidongyzby/openclaw-serper and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate serper-scholar. Access via /serper-scholar in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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基于 Google Scholar API 的学术文献搜索工具，提供学术论文、研究报告、技术文献的专业搜索能力。
当用户提到以下内容时自动激活：
用途： 执行学术文献搜索，返回论文详细信息
参数：
query (必选，string)：搜索关键词num (可选，number)：返回结果数量，默认 10，最大 20gl (可选，string)：国家代码，默认 cnhl (可选，string)：语言代码，默认 zh-CN返回字段：
title：论文标题url：论文链接snippet：摘要type：文献类型（PDF、HTML 等）year：发表年份authors：作者列表publication：发表刊物/会议citationCount：引用次数使用专业术语和技术关键词：
示例：
明确研究领域和方法：
示例：
关注最新研究进展：
示例：
查找特定研究者或机构的工作：
示例：
关注特定类型的文献：
示例：
根据需求调整：
num=5（核心文献）num=10（主流研究）num=20（全面覆盖）关注高引用论文和经典文献：
关注点：
citationCount 高的论文通常是领域经典用户提问： "研究一下 Transformer 架构的论文"
AI 行为：
// 搜索核心论文
serper_scholar({
query: "Transformer architecture attention is all you need",
num: 5
})
// 搜索后续研究
serper_scholar({
query: "BERT transformer NLP 2018 2019",
num: 8
})
回复示例：
核心论文：
1. **Attention Is All You Need** (2017)
- 作者：Vaswani et al.
- 引用：100,000+
- 📄 [PDF]
后续研究：
2. **BERT: Pre-training of Deep Bidirectional Transformers** (2019)
- 作者：Devlin et al.
- 引用：80,000+
- 发表：NAACL 2019
3. **Improving Language Understanding with Generative Pre-Training** (2018)
- 作者：Radford et al. (OpenAI)
- 引用：50,000+
用户提问： "找一些关于强化学习的综述论文"
AI 行为：
serper_scholar({
query: "Survey review reinforcement learning",
num: 8
})
回复示例：
强化学习综述论文：
1. **Reinforcement Learning: A Survey** (2021)
- 引用：5,000+
- 类型：综述
2. **Deep Reinforcement Learning: An Overview** (2020)
- 引用：8,000+
- 类型：综述
3. **Recent Advances in Deep Reinforcement Learning** (2022)
- 引用：2,000+
- 类型：综述
用户提问： "查找 Geoffrey Hinton 的深度学习论文"
AI 行为：
serper_scholar({
query: "Geoffrey Hinton deep learning backpropagation",
num: 10
})
回复示例：
Geoffrey Hinton 的经典论文：
1. **Deep Learning** (2015)
- 合作：LeCun, Bengio
- 发表：Nature
- 引用：50,000+
2. **Backpropagation Applied to Handwritten Zip Code Recognition** (1989)
- 引用：10,000+
3. **Reducing the Dimensionality of Data with Neural Networks** (2006)
- 引用：30,000+
用户提问： "2024-2025 年大模型有什么新进展"
AI 行为：
serper_scholar({
query: "Large language models 2024 2025 advances",
num: 10
})
回复示例：
2024-2025 大模型研究进展：
1. **Scaling Laws for Neural Language Models** (2024)
- 持续探索模型规模与性能关系
2. **Instruction Tuning for Large Language Models** (2024)
- 指令微调方法改进
3. **Chain-of-Thought Prompting** (2024)
- 推理链提示技术
用户提问： "搜索机器学习在医疗诊断中的应用论文"
AI 行为：
serper_scholar({
query: "Machine learning medical diagnosis healthcare",
num: 8
})
回复示例：
医疗诊断中的机器学习：
1. **Deep Learning for Medical Image Analysis** (2021)
- 引用：8,000+
- 应用：影像诊断
2. **Machine Learning in Clinical Diagnosis** (2022)
- 引用：3,000+
- 应用：辅助诊断
3. **AI in Healthcare: A Survey** (2023)
- 引用：2,000+
- 类型：综述
编辑 ~/.openclaw/gateway.env：
SERPER_API_KEY=your-api-key-here
访问 https://serper.dev/ 注册并获取 API Key。
免费额度：每月 2,500 次调用（Web 和 Scholar 共享）。
💡 提示： 学术搜索时，尽量使用英文关键词，英文论文数量和质量通常更高。
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
We added serper-scholar from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in serper-scholar — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
serper-scholar fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for serper-scholar matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: serper-scholar is focused, and the summary matches what you get after install.
serper-scholar reduced setup friction for our internal harness; good balance of opinion and flexibility.
serper-scholar is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: serper-scholar is the kind of skill you can hand to a new teammate without a long onboarding doc.
serper-scholar reduced setup friction for our internal harness; good balance of opinion and flexibility.
serper-scholar has been reliable in day-to-day use. Documentation quality is above average for community skills.
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