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Fin Novelty Check

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csmar432
fin-novelty-check

验证经济金融研究想法的新颖性。在JF、JFE、RFS、JME等顶刊及arXiv中搜索近三年文献,输出结构化新颖性报告和定位策略。

Overview

Publishercsmar432
Repositoryfinai-research
Skill namefin-novelty-check
Stars
108
Forks
4
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by csmar432 on GitHub. Read the source before you install it.

Installation

Install the Fin Novelty Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/csmar432/finai-research/tree/main/.agents/skills/fin-novelty-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/csmar432/finai-research.git /tmp/finai-research
mkdir -p .claude/skills
cp -r /tmp/finai-research/.agents/skills/fin-novelty-check .claude/skills/fin-novelty-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fin Novelty Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fin Novelty Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fin Novelty Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

研究想法新颖性验证

在顶刊数据库中系统性检索,评估研究想法的原创性,输出定位策略。

核心功能

  • 四维评估:相似度 / 方法差异 / 样本独特性 / 机制新颖性
  • 顶刊检索:JF / JFE / RFS / JME / arXiv / NBER
  • 中文顶刊:经济研究 / 金融研究 / 管理世界
  • 综合评分:HIGH (≥8) / MEDIUM (6-8) / LOW (<6)
  • 定位策略:差异化路径 + 潜在风险规避

工作流程

Step 1: 想法解析

将研究想法拆解为 3-5 个可检验的核心主张(Claims):

原始想法: "数字金融对中小企业创新的影响"

核心主张:
1. 数字金融能显著提升中小企业创新投入
2. 融资约束是主要作用机制
3. 东部地区效果强于西部地区
4. 对民营企业效果强于国有企业

每个 Claim 需包含:

  • 变量关系:[X] → [Y]
  • 假设方向:[正向/负向/倒U型]
  • 适用情境:[样本范围]

Step 2: 多源并行检索

对每个 Claim 分别检索,使用以下模板:

JF/JFE/RFS(英文顶刊)
python
server: user-brave-search
tool: brave_web_search
params: {
    "query": "site:jf.com digital finance SME innovation empirical"
}
# 替换: site:jfe.oxfordjournals.org, site:rfs.org
arXiv(近3年预印本)
python
server: user-arxiv
tool: semantic_search
params: {
    "query": "digital finance AND (SME OR \"small business\") AND (innovation OR R&D) AND (2023 OR 2024 OR 2025)"
}
NBER(工作论文)
python
server: user-nber-wp
tool: get_nber_papers
params: {
    "category": "corporate finance OR financial economics",
    "year_from": 2023
}
中文顶刊
python
server: user-brave-search
tool: brave_web_search
params: {
    "query": "site:er.cngp.org.cn OR site:jr.cass.org.cn 数字金融 中小企业 创新 实证"
}
# 经济研究: site:er.cngp.org.cn
# 金融研究: site:jr.cass.org.cn
# 管理世界: site:管理与世界.ajcass.com
补充检索(更宽泛)
python
server: user-openalex
tool: get_openalex_works
params: {
    "query": "digital finance innovation SMEs empirical",
    "per_page": 30
}

Step 3: 四维评估

对每个 Claim 逐一评估:

维度评估问题评分 (1-10)
相似度 (S)已有多少研究做了一样的 X→Y?1=完全相同, 10=从未做过
方法差异 (M)你的方法与已有研究有何不同?1=方法相同, 10=全新方法
样本独特性 (U)数据/样本是否独特?1=常用数据, 10=独有数据
机制新颖性 (N)机制解释是否新颖?1=常见机制, 10=全新机制

评估标准详解:

相似度 (S):

  • 1-3:完全相同的 X→Y 已有多个顶刊研究
  • 4-6:相近主题(如数字金融→创新),但 X/Y 定义不同
  • 7-10:X→Y 组合从未被研究过

方法差异 (M):

  • 1-3:直接复用已有研究方法(如标准DID)
  • 4-6:有改进(如异质性DID、动态DID)
  • 7-10:引入全新方法或组合(如合成DID + 机器学习)

样本独特性 (U):

  • 1-3:常用数据库(AShare、Compustat)
  • 4-6:独特样本(特定地区/行业/时间段)
  • 7-10:独有数据(自建数据库、实地调查)

机制新颖性 (N):

  • 1-3:融资约束/资源获取等常见机制
  • 4-6:细分机制(如融资约束的某个维度)
  • 7-10:全新机制(如心理账户、数字化转型路径)

Step 4: 综合评分

计算每个 Claim 的加权得分:

Claim_Score = 0.3*S + 0.25*M + 0.25*U + 0.2*N

整体评分:

  • 取所有 Claim 的平均分
  • 考虑最薄弱 Claim 的下限约束

评级:

  • HIGH (≥8):可以继续,建议在 Introduction 中明确差异化
  • MEDIUM (6-8):需要调整,建议按 Step 6 调整策略
  • LOW (<6):建议更换想法或重大调整

Step 5: 识别竞争论文

找出与本研究最相似的 3 篇论文:

选择标准:
1. X→Y 组合相同或高度相似
2. 发表在同级及以上期刊
3. 时间在近5年内

对每篇论文说明:
- 为什么相似(Claim 重叠度)
- 本研究的差异化点
- 如何在 Introduction 中委婉处理

Step 6: 定位策略(MEDIUM/LOW 专用)

如果评分为 MEDIUM 或 LOW,提供调整建议:

问题类型调整策略
相似度过高聚焦细分样本(特定行业/地区/规模)
方法无差异引入新方法或数据来源
样本无独特性使用独有数据或组合多个数据库
机制常见挖掘新机制或异质性分析

定位策略模板:

本研究与 [竞争论文] 的区别:
1. [维度1]: [本研究做法] vs [竞争论文做法]
2. [维度2]: ...
3. [维度3]: ...

Contribution 重申:
- 本研究首次在 [样本/情境] 下检验 [X→Y]
- 采用 [新方法/改进方法] 解决了 [问题]
- 发现 [新机制/异质性模式]

Step 7: 生成报告

output/fin-novelty/NOVELTY_REPORT.md
markdown
# 新颖性报告: [研究想法标题]

> 评估日期: [日期]
> 评估者: fin-novelty-check

## 整体评级: HIGH / MEDIUM / LOW
## 综合评分: X/10

---

## 研究想法摘要

**核心主张**:
1. [主张1]
2. [主张2]
3. [主张3]

**预期贡献**:
- 理论贡献: [1-2句话]
- 实证贡献: [1-2句话]
- 政策贡献: [1-2句话]

---

## 主张分析

| 主张 | 相似度(S) | 方法(M) | 样本(U) | 机制(N) | 单项得分 | 权重分 |
|------|-----------|---------|---------|---------|----------|--------|
| 1. X→Y | 7 | 8 | 6 | 5 | 6.55 | 0.30 |
| 2. M中介 | 6 | 7 | 8 | 7 | 6.95 | 0.25 |
| 3. 异质性 | 8 | 6 | 7 | 8 | 7.30 | 0.25 |
| 4. [样本] | 9 | 8 | 7 | 8 | 8.15 | 0.20 |

**加权综合得分**: 7.19/10

---

## 顶刊检索结果

### JF/JFE/RFS
- [竞争论文1] — [期刊] [年份] — [相似点] / [差异点]
- [竞争论文2] — ...

### arXiv / NBER
- [预印本1] — [年份] — [相似点] / [差异点]

### 中文顶刊
- [文献1] — [期刊] [年份] — [相似点] / [差异点]

---

## Top 3 竞争论文

### 1. [Citation]
**期刊**: [期刊名] | **年份**: [年份]
**相似点**: [为什么与本研究重叠]
**差异点**: [本研究的独特之处]
**在 Introduction 中的处理**: [如何委婉地承认相关研究同时指出差异]

### 2. [Citation]
...

### 3. [Citation]
...

---

## 审稿人风险评估

| 风险 | 审稿人可能质疑 | 缓解策略 |
|------|---------------|----------|
| R1 | "这个选题太老了" | 强调样本/方法/情境的独特性 |
| R2 | "方法没有新意" | 展示方法改进或新方法应用 |
| R3 | "数据不够独特" | 说明数据来源的不可替代性 |

---

## 定位策略

[仅当 MEDIUM/LOW 时填写]

### 当前问题
- 相似度较高维度: [维度]
- 需要改进方向: [方向]

### 建议调整
1. **样本聚焦**: [具体建议]
2. **方法改进**: [具体建议]
3. **机制挖掘**: [具体建议]

### 差异化定位

本研究的核心差异化:


---

## 建议期刊

根据研究主题和评分建议目标期刊:
- [期刊1] — [理由]
- [期刊2] — [理由]

---

## 下一步

- [ ] 如果 HIGH:可直接进入 `fin-experiment-design`
- [ ] 如果 MEDIUM:按调整建议修改想法后重新评估
- [ ] 如果 LOW:建议重新生成想法或更换研究方向

Checkpoint:向用户展示报告,询问是否继续:

markdown
## 新颖性评估完成

**评级**: HIGH (7.8/10)
**核心风险**: 相似度中等,需加强方法差异

**是否继续**?
 [1] 继续 — 进入实验设计 (`fin-experiment-design`)
 [2] 调整 — 按建议调整想法后重新评估
 [3] 更换 — 重新生成研究想法

MCP 工具快速参考

数据源工具查询模板
JFbrave_web_searchsite:jf.com [keywords]
JFEbrave_web_searchsite:jfe.oxfordjournals.org [keywords]
arXivsemantic_search[topic] AND [method] AND (2023 OR 2024 OR 2025)
NBERget_nber_paperscategory, year_from=2023
OpenAlexget_openalex_worksquery, per_page=30
论文全文get_context7_by_queryquery, max_results

与其他技能的关系

  • 上游:
    • fin-generate-idea → 输出候选想法供验证
    • fin-lit-review → 文献综述为新颖性判断提供依据
  • 下游:
    • fin-experiment-design → 新颖性验证通过后进入设计
    • fin-paper-writing → 在 Introduction 中引用竞争论文

注意事项

  1. 评分是主观的:基于检索结果的定性评估,需结合实际文献
  2. HIGH 不代表无风险:高评分仍需在 Introduction 中妥善处理竞争文献
  3. 中文顶刊同样重要:经济研究/金融研究/管理世界的相关研究必须检索
  4. 预印本也是竞争:arXiv/NBER 工作论文代表最新进展
  5. 评分阈值灵活:可根据目标期刊调整(如 RFS 可接受 MEDIUM)

Frequently asked questions

What does the Fin Novelty Check AI skill do?

验证经济金融研究想法的新颖性。在JF、JFE、RFS、JME等顶刊及arXiv中搜索近三年文献,输出结构化新颖性报告和定位策略。

Why use Fin Novelty Check on TypingMind?

Because you install it once and use it with any model. Fin Novelty Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fin Novelty Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/csmar432/finai-research/tree/main/.agents/skills/fin-novelty-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fin Novelty Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fin Novelty Check?

As many as you like. As long as a model supports skills, you can use Fin Novelty Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fin Novelty Check AI skill free?

Yes. It is published on GitHub by csmar432 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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