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/researchWrote 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/research)<a href="https://agentmods.dev/commands/csmar432/finai-research/research"><img src="https://agentmods.dev/badge/commands/csmar432/finai-research/research.svg" alt="Measured on agentmods" 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 | $0.00335 |
| Opus 5 | $0.00000 | $0.00168 |
| Sonnet 5 | $0.00000 | $0.00067 |
| Haiku 4.5 | $0.00000 | $0.00034 |
Grade A, and why
research 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 9d 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
research — 启动完整研究流程
description: 启动经济金融领域的完整研究流水线（从文献综述到论文草稿）
arguments
<topic>: 研究主题（中文或英文均可）
描述
启动论文-研报工作流的完整端到端研究流程。
等价于：
python scripts/agent.py --goal "<topic>"
或直接用 AI Agent 自然语言交互：
"帮我研究 [你的研究方向]，发表在经济研究"
示例
/research 关税政策对A股出口型企业创新的影响
/research carbon trading innovation Chinese A-shares
/research ESG and cost of capital
工作流程
- 文献综述 — arXiv / NBER / OpenAlex 搜索
- 研究想法生成 — 8-12 个候选想法，数据验证
- 实证设计 — DID / IV / RDD / PSM 识别策略
- 数据获取 —
{{MCP_COUNT}}个 MCP 服务器自动拉取 - 回归分析 — 38+ 种计量方法
- 论文写作 — LaTeX 输出（JF / JFE / RFS / 经济研究）
- 对抗性 review — 多轮迭代直到发表标准
环境要求
- Python 3.10+
- 大部分 MCP 服务器无需 API Key
- LaTeX（xelatex，用于中文期刊）
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.
- 9d ago First seen · 48 lines · 0 tokens per session scan A 76acf88d9fed
research is a command published in the GitHub repository csmar432/finai-research (114 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 335 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
credit-review
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source
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.