fin-lit-review

fin-lit-review is a skill for Claude Code from csmar432/finai-research. It costs 49 tokens per session (2,303 once invoked), scanned A, original, MIT.

A systematic literature-review workflow for economics and finance. It searches several academic sources, maps how papers cite one another, and organizes the main findings and research gaps.

In plain words
What is it for?
Use it to break a question into population, intervention, comparison, and outcome—for example, companies, carbon trading, non-trading firms, and green patents. It searches sources such as OpenAlex, arXiv, NBER, and Chinese academic search services, then produces review files and a citation-network file.
Why use it?
It brings papers from different sources into one review process and makes the selection rules visible. This helps replace an unsorted reading list with a structured map of a research area.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to break a question into population, intervention, comparison, and outcome—for example, companies, carbon trading, non-trading firms, and green patents. It searches sources such as OpenAlex, arXiv, NBER, and Chinese academic search services, then produces review files and a citation-network file.

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Install with agentmods
npx agentmods add skills/csmar432/finai-research/fin-lit-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add csmar432/finai-research --skill fin-lit-review
Clone the repo
git clone --depth 1 https://github.com/csmar432/finai-research

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for fin-lit-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/csmar432/finai-research/fin-lit-review/github.svg)](https://agentmods.dev/skills/csmar432/finai-research/fin-lit-review)
Your own site
<a href="https://agentmods.dev/skills/csmar432/finai-research/fin-lit-review"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-lit-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for fin-lit-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/csmar432/finai-research/fin-lit-review"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-lit-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,303 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.02303
Opus 5 $0.00024 $0.01151
Sonnet 5 $0.00010 $0.00461
Haiku 4.5 $0.00005 $0.00230

Measured 13d ago against content hash 08470494fd5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fin-lit-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/fin-lit-review/SKILL.md · 299 lines

How it starts

The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.

经济金融系统性文献综述

整合多源学术数据库,构建引文网络,识别研究缺口。

核心功能

  • 多源并行检索:OpenAlex / ArXiv / NBER / 百度学术
  • PRISMA 筛选流程:Inclusion/Exclusion 标准透明
  • 引文网络构建:NetworkX 有向图,可视化知识结构
  • 研究缺口识别:LLM 分析 + 人工标注
  • 结构化输出:LIT_REVIEW.md / LIT_SUMMARY.md / CITATION_GRAPH.json

工作流程

Step 1: PICO 解析

将研究问题拆解为 PICO 四要素:

P (Population): 研究对象 — 企业/投资者/银行/政府
I (Intervention): 处理变量 — 政策/工具/事件
C (Comparator): 对照组 — 处理前/未受政策影响
O (Outcome): 结果变量 — 创新/绩效/风险/效率

示例输入:"碳排放权交易对企业绿色创新的影响" → PICO: P=制造业企业, I=碳排放权交易试点, C=非试点企业, O=绿色专利/研发投入

Step 2: 多源并行检索

使用以下 MCP 工具并行搜索:

OpenAlex(优先,推荐 50 篇)
server: user-openalex
tool: get_openalex_works
params: {
    "query": "carbon trading OR carbon emission trading green innovation",
    "per_page": 50,
    "sort": "citation_count"
}
ArXiv(预印本,最新方法)
server: user-arxiv
tool: semantic_search
params: {
    "query": "carbon trading innovation policy effect DID",
    "max_results": 30
}
NBER(工作论文,高质量)
server: user-nber-wp
tool: get_nber_papers
params: {
    "category": "corporate finance OR environmental economics",
    "year_from": 2021
}
中文文献(百度学术 + CNKI)
server: user-brave-search
tool: brave_web_search
params: {
    "query": "碳排放权交易 绿色创新 DID 双重差分 经济研究"
}
研报补充(东方财富)
server: user-eastmoney-reports
tool: get_stock_news
params: {
    "ts_code": "000001.SZ",
    "limit": 20
}
# 用于补充行业背景,不作为核心文献

Step 3: PRISMA 筛选

对检索到的所有文献应用筛选标准:

Inclusion Criteria:
✓ 实证研究(排除纯理论/综述)
✓ 经济金融领域(或跨学科应用)
✓ 英文/中文全文可获取
✓ 2000年后发表

Exclusion Criteria:
✗ 纯工程/技术类研究(非金融视角)
✗ 无DOI/无法溯源
✗ 样本量<100 或 方法严重缺陷
✗ 与研究问题无关

Checkpoint:筛选完成后,向用户展示筛选数量统计:

## PRISMA 筛选结果

- 检索总数: [N]
- 去重后: [N]
- 标题/摘要筛选排除: [N]
- 全文筛选排除: [N]
- 最终纳入: [N]

是否继续生成文献综述?
 [1] 继续
 [2] 调整筛选标准
 [3] 补充更多文献

Step 4: 引文网络构建

使用 scripts/citation_graph.py 构建知识图谱:

from scripts.citation_graph import CitationGraphBuilder

builder = CitationGraphBuilder()
graph = builder.build(papers)  # papers: list of dict with title/doi/cite_count

# 提取高影响力文献
influential = builder.get_influential_papers(top_n=20)

# 提取引文聚类(研究主题簇)
clusters = builder.get_citation_clusters()

# 导出 JSON
graph_json = builder.to_json()

Read the full file on GitHub · 299 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 299 lines · 49 tokens per session scan A 08470494fd5b

Subscribe to this mod's changes

fin-lit-review is a skill published in the GitHub repository csmar432/finai-research (100 stars, last pushed 4d ago), licensed MIT. It adds 49 tokens to every session and 2,303 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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