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依据实证论文的规范结构与写作风格生成学术论文；适用于生成金融、经济、会计等领域的研究论文框架与正文内容
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Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.
Search academic papers and conduct literature reviews using OpenAlex API (free, no key needed). Use when the user needs to find scholarly papers by topic/author/DOI, explore citation chains, get structured paper metadata (title, authors, abstract, citations, DOI, open access URL), fetch full text of open access papers, or conduct automated literature reviews with theme identification and synthesis. Triggers on requests involving academic search, paper lookup, citation analysis, literature review, research synthesis, or scholarly reference gathering.
Use AI4Scholar author tools to search scholars, inspect author profiles, retrieve an author's papers, compare experts, identify potential reviewers, map labs or collaborators, and verify whether an author is the correct person.
Use AI4Scholar auto_cite to add real citations to academic text and return formatted references and BibTeX. Trigger for automatic citation insertion, APA/IEEE/Vancouver/Nature citation support, reference generation, BibTeX export, or checking whether claims have real supporting papers.
Use AI4Scholar to trace citation networks: citing papers, references, PubMed related papers, backward/forward citation search, classic paper discovery, mechanism literature expansion, and reviewer-style citation gap checks.
SOC 職業分類に基づく
| name | empirical-paper-writer |
| description | 依据实证论文的规范结构与写作风格生成学术论文；适用于生成金融、经济、会计等领域的研究论文框架与正文内容 |
| dependency | {"python":["requests==2.28.0"]} |
依据实证论文的规范结构与写作风格，生成符合学术期刊要求的完整论文框架与正文内容。
要求:
生成指南:
规范:
模板句式:
We study/conduct/investigate [RESEARCH QUESTION]. Using [METHODOLOGY]
on [DATA SAMPLE], we find that [KEY FINDING]. [ECONOMIC IMPLICATION].
标准结构:
3.1 研究背景与问题 (Research Motivation)
- 阐述领域核心问题
- 引用2-3篇经典文献说明研究缺口
3.2 研究贡献 (Main Contributions)
- 理论贡献
- 方法贡献
- 实证贡献
- 实践贡献
3.3 论文结构 (Paper Organization)
- "Section 2 describes... Section 3 presents... Section 4 concludes..."
写作要点:
组织方式:
4.1 [领域主题A]
- 研究现状
- 研究空白
4.2 [领域主题B]
- 研究现状
- 研究空白
注意事项:
必要组成部分:
5.1 数据与样本 (Data and Sample)
- 数据来源
- 样本选择标准
- 变量定义
- 描述性统计
5.2 研究设计 (Research Design)
- 理论框架
- 模型设定
- 识别策略
5.3 估计方法 (Empirical Methodology)
- 基准模型
- 稳健性检验
- 内生性处理
数学公式规范:
结构模板:
6.1 基准回归 (Baseline Results)
6.2 经济显著性 (Economic Significance)
6.3 稳健性检验 (Robustness Checks)
- 替代变量
- 子样本分析
- 方法稳健性
6.4 内生性分析 (Endogeneity)
结果呈现:
结构:
7.1 研究总结
7.2 理论贡献
7.3 实践启示
7.4 研究局限与未来方向
推荐表达:
避免表达:
标准段落(5-7句):
文中引用:
表格规范:
图形规范:
见 references/asset_pricing_template.md
输入: 研究主题:机器学习在资产定价中的应用 研究假设:神经网络预测股票收益优于线性模型 数据类型:CRSP股票收益数据，1957-2020
生成步骤:
输入: 已有Introduction草稿，需要润色
操作: