Fin Review Loop

Q: Why use Fin Review Loop on TypingMind?

Because you install it once and use it with any model. Fin Review Loop 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.

Q: How do I install Fin Review Loop in TypingMind?

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

Q: Which AI models can use Fin Review Loop?

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.

Q: How many AI models can I use with Fin Review Loop?

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

Q: Is the Fin Review Loop 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.

Q: 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.

Q: 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.

Community

csmar432

fin-review-loop

经济金融论文的对抗性review循环。对草稿进行多轮严格评审，检查实证严谨性、方法正确性、理论贡献和写作质量，给出可操作的修改建议。（AI review 不能替代同行评审，草稿必须经研究者核实后投稿。）

Overview

Publisher	csmar432
Repository	`finai-research`
Skill name	`fin-review-loop`
Stars	108
Forks	4
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 Review Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/csmar432/finai-research/tree/main/.agents/skills/fin-review-loop
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.

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-review-loop .claude/skills/fin-review-loop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fin Review Loop 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 Review Loop 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 Review Loop 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.

fin-review-loop

触发条件

关键词: review 评审 审稿 检查论文 对抗性review 论文检查
Skill语法: Skill: fin-review-loop

评分维度与权重

维度	权重	通过阈值
新颖性 (Novelty)	30%	>= 6.0
实证严谨性 (Empirical Rigour)	30%	>= 6.0
文献覆盖 (Literature Coverage)	15%	>= 5.0
写作清晰 (Writing Clarity)	15%	>= 5.0
学术影响 (Academic Impact)	10%	>= 5.0

其中"写作清晰"维度必须包含 AI 味检测：全文不得出现 AI 典型句式（详见 docs/writing-guide/ANTI_AI_WRITING_GUIDE.md），结论段必须包含底气要素（具体数字/经济规模/机制描述/对比发现之一）。

评审难度级别

standard: 模拟标准学术审稿人
strict: 模拟顶刊审稿人 (JF/JFE 级别)
nightmare: 模拟严苛批评型审稿人 (如被拒稿后的防御性检查)

评审难度示例

standard

发现问题时会给出温和建议
接受主流方法选择
关注核心贡献是否清晰

strict

要求所有实证细节完备
质疑识别策略的每一步
检查文献是否覆盖最新顶刊

nightmare

预设论文会被拒，准备攻击
寻找方法论上的致命缺陷
模拟最严格的匿名审稿人

停止条件 (立即终止评审并报告用户)

满足以下任一条件时，立即停止评审：

新颖性 < 6.0 → 建议重新评估研究定位
实证严谨性 < 6.0 → 必须修复实证问题才能继续
已达到最大评审轮次 (4轮)

评审流程

第一步：解析论文

读取 output/fin-manuscript/ 下的所有 .tex 文件
提取论文结构：Introduction, Literature Review, Data, Methodology, Results, Conclusion
如文件不存在，扫描项目根目录和 papers/ 目录

第二步：诊断性检查

自动运行以下检查：


□ 平行趋势检验结果是否存在
□ 稳健性检验 >= 6 种
□ 异质性分析是否包含
□ 机制分析是否包含
□ 参考文献是否包含近3年顶刊论文
□ 变量定义表是否完整
□ 数据来源是否标注
□ 实证方法选择是否合理

第三步：逐维度评分

对每个维度进行 1-10 分评分，并说明理由：

维度	评分	理由
新颖性	X	边际贡献是什么？与现有文献区别？
实证严谨性	X	识别策略是否合理？数据是否可靠？
文献覆盖	X	是否覆盖最新顶刊？经典文献？
写作清晰	X	逻辑是否清晰？论证是否连贯？
学术影响	X	对该领域的潜在影响？引用潜力？

第四步：生成逐节反馈

为论文每个章节生成具体、可操作的反馈：


### Introduction
- 问题: 边际贡献描述不够具体
- 建议: 明确说明与X论文的区别，本文的增量贡献是什么

### Data & Methodology
- 问题: 平行趋势图缺少统计显著性标注
- 建议: 在图中标注pre-treatment各期系数的置信区间

### Results
- 问题: 基准回归系数解读不够严谨
- 建议: 添加经济显著性解释（1个标准差变动对应Y变化X%）

第五步：识别审稿人攻击点

识别论文中最可能被审稿人攻击的弱点：


## 审稿人攻击点
1. [高风险] 审稿人会质疑平行趋势假设——需要pre-trends test p值
2. [中风险] 样本期间选择——为何选择2012-2022年？
3. [低风险] 稳健性检验中未包含安慰剂检验

第六步：生成修订计划

生成 REVISION_PLAN.md，按优先级列出修复项：

markdown
# 修订计划 — Round N

## 优先级 P0 (必须修复)
1. [实证] 添加平行趋势检验的p值到图中
2. [实证] 补充安慰剂检验

## 优先级 P1 (强烈建议)
1. [写作] 明确边际贡献表述
2. [文献] 补充近3年JF/JFE论文引用

## 优先级 P2 (可选优化)
1. [写作] 优化摘要结构
2. [格式] 检查参考文献格式

第七步：等待用户确认修订


[CHECKPOINT] 评审完成。请确认：
1. 接受修订计划 → 开始修订
2. 修改修订计划 → 告知修改内容
3. 终止评审 → 记录当前状态

第八步：重复评审

修订完成后，重新运行评审流程。重复直到通过所有阈值或达到停止条件。

输出格式

markdown
# Review Report — Round N

## Overall Score: X/10 (WEIGHTED)

## Dimension Scores
| Dimension | Score | Pass? |
|-----------|-------|-------|
| Novelty | 7.5 | ✅ |
| Rigour | 6.0 | ✅ |
| Literature | 7.0 | ✅ |
| Clarity | 6.5 | ✅ |
| Impact | 7.0 | ✅ |

## Diagnostic Checks
| Check | Status |
|-------|--------|
| Parallel trends test | ✅ |
| Robustness >= 6 types | ✅ |
| Heterogeneity analysis | ✅ |
| Mechanism analysis | ✅ |
| Recent top-journal refs | ✅ |

## Section-by-Section Feedback

### Introduction
- Issue: 边际贡献描述不够具体
- Suggestion: 明确说明与X论文的区别

### Data & Methodology
- Issue: 平行趋势图缺少统计显著性标注
- Suggestion: 在图中标注pre-treatment各期系数的置信区间

## Reviewer Attack Vectors
1. [HIGH RISK] 审稿人会质疑平行趋势假设——需要pre-trends test p-value
2. [MEDIUM RISK] 样本期间选择——为何选择2012-2022年？

## PASS/FAIL/REVISION NEEDED

输出文件

output/fin-review/REVIEW_REPORT_ROUND_N.md — 本轮评审报告
output/fin-review/REVISION_PLAN.md — 修订计划

依赖项

scripts/research_framework/modern_did.py — DID诊断工具
scripts/research_framework/robustness_runner.py — 稳健性检验运行器
scripts/journal_template.py — 期刊格式验证

约束

每轮评审必须完整执行所有8个步骤
停止条件满足时必须立即报告，不得继续评审
反馈必须具体、可操作，避免泛泛而谈
攻击点识别必须基于真实审稿人行为模式
修订计划必须标注优先级 (P0/P1/P2)

Frequently asked questions

What does the Fin Review Loop AI skill do?

Why use Fin Review Loop on TypingMind?

Because you install it once and use it with any model. Fin Review Loop 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 Review Loop in TypingMind?

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

Which AI models can use Fin Review Loop?

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 Review Loop?

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

Is the Fin Review Loop 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

fin-review-loop

触发条件

评分维度与权重

评审难度级别

评审难度示例

standard

strict

nightmare

停止条件 (立即终止评审并报告用户)

评审流程

第一步：解析论文

第二步：诊断性检查

第三步：逐维度评分

第四步：生成逐节反馈

第五步：识别审稿人攻击点

第六步：生成修订计划

第七步：等待用户确认修订

第八步：重复评审

输出格式

输出文件

依赖项

约束

Frequently asked questions

Related AI Skills

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