Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/csmar432/finai-researchnpx agentmods add commands/csmar432/finai-research/lit-reviewWrote 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.
[](https://agentmods.dev/commands/csmar432/finai-research/lit-review)<a href="https://agentmods.dev/commands/csmar432/finai-research/lit-review"><img src="https://agentmods.dev/badge/commands/csmar432/finai-research/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.
<a href="https://agentmods.dev/commands/csmar432/finai-research/lit-review"><img src="https://agentmods.dev/badge/commands/csmar432/finai-research/lit-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00264 |
| Opus 5 | $0.00000 | $0.00132 |
| Sonnet 5 | $0.00000 | $0.00053 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
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 12d 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.
What it actually says
lit-review — 系统性文献综述
description: 对指定研究主题进行系统性文献综述，构建引文网络
arguments
<topic>: 研究主题（中文或英文均可）
描述
运行文献综述流水线，使用 MCP 工具搜索学术论文并构建引文网络。
等价于：
python scripts/literature_download.py "<topic>" --source arxiv,semantic,openalex --limit 25
# or Skill: fin-lit-review
MCP 数据源
| 数据源 | 覆盖范围 |
|---|---|
user-arxiv |
cs.AI / cs.LG / q-fin.GN |
user-nber-wp |
NBER Working Papers |
user-semantic-scholar |
2亿+学术论文 |
user-openalex |
学术引用图谱 |
user-brave-search |
中文期刊（经济研究/金融研究） |
输出
output/fin-literature/LIT_REVIEW.md— 结构化文献综述output/fin-literature/CITATION_GRAPH.json— 引文网络图
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.
- 12d ago First seen · 34 lines · 0 tokens per session scan A 22c6afeff032
lit-review is a command published in the GitHub repository csmar432/finai-research (100 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 264 tokens. 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.
Other commands, from other repositories
latex-polish
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latex
Compile one specified LaTeX document, resolve build errors, audit citations, and report build quality. Use when a .tex source must be built or a concrete compilation failure repaired. Not for corpus-wide build checks, visual polish after a clean build, or creating a project; use $latex-health-check, $latex-polish, or…
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causal-design
Design or audit the identification strategy for an observational study. Use when the task concerns estimands, causal assumptions, threats to identification, or defensible research design rather than model implementation.
experiment-design
Design empirical studies through power analysis, pre-analysis planning, QSF parsing, and survey architecture. Use when specifying sampling, measurement, treatment, or analysis before data collection. Not for causal identification alone; use $causal-design.
proof-readability
Improve the exposition and readability of a mathematical proof already verified as correct without changing its mathematics. Use when polishing a lemma, theorem, proof, or appendix after correctness checks. Not for verifying the proof; use $verify-math.