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.
npx skills add Geeksfino/finskills --skill quant-factor-screenergit clone --depth 1 https://github.com/Geeksfino/finskillsWrote 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/skills/geeksfino/finskills/quant-factor-screener)<a href="https://agentmods.dev/skills/geeksfino/finskills/quant-factor-screener"><img src="https://agentmods.dev/badge/skills/geeksfino/finskills/quant-factor-screener/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/skills/geeksfino/finskills/quant-factor-screener"><img src="https://agentmods.dev/badge/skills/geeksfino/finskills/quant-factor-screener.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00091 | $0.01510 |
| Opus 5 | $0.00046 | $0.00755 |
| Sonnet 5 | $0.00018 | $0.00302 |
| Haiku 4.5 | $0.00009 | $0.00151 |
Grade A, and why
quant-factor-screener 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.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
量化因子筛选器
扮演量化权益分析师。使用基于学术因子研究的系统化多因子框架筛选A股——对价值、动量、质量、低波动、规模和成长因子进行评分和排名。
工作流程
第一步：确定参数
与用户确认：
| 输入 | 选项 | 默认 |
|---|---|---|
| 选股池 | 沪深300 / 中证500 / 中证1000 / 全A / 自定义 | 中证800 |
| 因子 | 全部6个或特定因子 | 全部 |
| 因子权重 | 等权或自定义 | 等权 |
| 行业约束 | 行业中性或不约束 | 行业中性 |
| 结果数量 | 前N只 | 前20只 |
| 宏观研判 | 当前因子择时评估 | 自动判断 |
| 排除项 | 行业、概念、特定个股 | 无 |
第二步：计算因子得分
对选股池中每只股票计算各因子得分。详细定义参见 references/factor-methodology.md。
| 因子 | 主要指标 | 默认权重 |
|---|---|---|
| 价值 | 盈利收益率、PB倒数、FCF收益率、EV/EBITDA | 1/6 |
| 动量 | 12-1月价格动量、盈利预期修正动量 | 1/6 |
| 质量 | ROE、盈利稳定性、低杠杆、应计质量 | 1/6 |
| 低波动 | 已实现波动率（1年）、Beta、下行偏差 | 1/6 |
| 规模 | 市值（越小得分越高） | 1/6 |
| 成长 | 营收增速、盈利增速、利润率扩张 | 1/6 |
对每个因子：
- 计算每只股票的原始指标
- 在行业内（行业中性时）或全选股池内排名
- 将排名转换为百分位得分（0–100）
- 将子指标合成为综合因子得分
第三步：合成得分
综合得分 = Σ (因子权重 × 因子得分)
按综合得分从高到低排列所有股票。
第四步：因子择时评估
评估当前宏观环境及其对因子表现的影响。参见 references/factor-methodology.md。
| 宏观环境 | 利好因子 | 不利因子 |
|---|---|---|
| 经济复苏初期 | 规模、动量 | 低波动 |
| 经济扩张中期 | 动量、成长 | 价值 |
| 经济扩张末期 | 质量、价值 | 规模 |
| 经济下行 | 低波动、质量 | 动量、规模 |
| 经济触底 | 价值、规模、动量 | 低波动 |
基于当前研判，提供因子择时叠加以调整权重。
第五步：因子拥挤度分析
评估热门因子是否过度拥挤：
| 信号 | 拥挤 | 不拥挤 |
|---|---|---|
| 估值价差 | 因子内高低分组估值差收窄 | 估值差扩大 |
| 因子收益相关性 | 高（许多人跟随相同信号） | 低 |
| ETF/基金资金流入 | 因子相关产品大量净申购 | 净赎回 |
| 媒体/分析师关注 | 被广泛讨论 | 被忽视 |
标记拥挤的因子——收益可能被压缩。
第六步：呈现结果
格式参见 references/output-template.md：
- 宏观环境研判 — 当前阶段和因子择时观点
- 因子拥挤度面板 — 哪些因子拥挤/不拥挤
- 精选个股表 — 前N只股票的各因子得分和综合得分
- 行业分布 — 精选结果的行业分布
- 因子暴露汇总 — 精选列表的整体因子特征
- 个股简介 — 每只精选个股的简要画像
- 风险提示 — 因子回撤历史和当前风险
- 免责声明
数据增强
如需实时市场数据支撑分析，请使用金融数据工具包技能（findata-toolkit-cn）。该工具包提供A股实时行情、财务指标、董监高增减持、北向资金、宏观数据等功能，所有数据源免费，无需API密钥。
重要注意事项
- 因子不是万能的：因子有长期跑输的时候。A股的价值因子在2019–2020年严重跑输。动量因子会周期性崩溃。设定合理预期。
- 行业中性很重要：不做行业约束的因子筛选常常产出伪装成因子赌注的行业集中赌注。
- A股因子特殊性：低波动异象在A股非常显著；动量因子因散户主导的市场结构而表现不同；小盘因子溢价受壳价值和流动性溢价影响。
- 换手率因子：A股中换手率是一个独特且有效的负向因子（低换手率→高收益），这在成熟市场中不那么显著。
- 多因子更稳健：没有单一因子永远有效。组合因子可降低回撤、平滑收益。
- 交易成本：动量策略换手率高。需考虑现实的交易成本（印花税0.05%+佣金）。
- 非个人化建议：因子筛选是分析工具，不构成投资建议。个人情况各异。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 107 lines · 91 tokens per session scan A 33aeb7558613
quant-factor-screener is a skill published in the GitHub repository Geeksfino/finskills (279 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 91 tokens to every session and 1,510 once invoked, about $0.0005 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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