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 agentmods add skills/feicoder/skill-factory/quant-stock-selection-intronpx skills add FeiCoder/Skill-Factory --skill quant-stock-selection-introgit clone --depth 1 https://github.com/FeiCoder/Skill-FactoryWrote 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/feicoder/skill-factory/quant-stock-selection-intro)<a href="https://agentmods.dev/skills/feicoder/skill-factory/quant-stock-selection-intro"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/quant-stock-selection-intro.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 | $0.00058 | $0.00541 |
| Opus 5 | $0.00029 | $0.00270 |
| Sonnet 5 | $0.00012 | $0.00108 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
quant-stock-selection-intro 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 4d 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
量化选股入门
什么是量化选股
量化选股是利用数量化的方法选择股票组合，期望该股票组合能够获得超越基准收益率的投资行为。
量化选股策略可以分为两类：
- 基本面选股 - 基于公司财务、估值等指标
- 市场行为选股 - 基于资金流向、价格走势等市场数据
量化选股的优势
- 系统性：能够处理大量股票，覆盖面广
- 客观性：避免人为情绪干扰
- 可验证性：策略可以基于历史数据进行回测验证
- 纪律性：严格执行预先设定的交易规则
量化选股主要策略
| 策略类型 | 说明 |
|---|---|
| 多因子模型 | 综合多个因子进行选股 |
| 风格轮动 | 根据市场风格切换进行选股 |
| 行业轮动 | 根据行业轮动规律选股 |
| 资金流模型 | 根据资金流向选股 |
| 动量反转 | 根据历史涨跌规律选股 |
| 一致预期 | 根据分析师预期选股 |
| 趋势追踪 | 根据价格趋势选股 |
| 筹码选股 | 根据筹码分布选股 |
业绩评价指标
量化选股需要从两个方面评价：
- 收益率指标 - 年化收益率、超额收益率等
- 风险度指标 - 最大回撤、波动率、夏普比率等
A股市场特点
- 个人投资者较多，市场情绪影响大
- 存在明显的风格轮动和行业轮动效应
- 资金流信息相对有效
- 小盘股效应明显
使用指南
当需要：
- 理解量化选股的基本框架
- 选择合适的选股策略类型
- 设计量化选股系统架构
- 评估选股策略效果
请使用此 skill 获取指导。
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.
- 4d ago First seen · 58 lines · 58 tokens per session scan A 1594197ea2e2
quant-stock-selection-intro is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 541 once invoked, about $0.0003 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 2026-08-31.
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