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 FeiCoder/Skill-Factory --skill hurst-timinggit 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/hurst-timing)<a href="https://agentmods.dev/skills/feicoder/skill-factory/hurst-timing"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/hurst-timing/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/feicoder/skill-factory/hurst-timing"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/hurst-timing.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 | $0.00047 | $0.00880 |
| Opus 5.5 | $0.00019 | $0.00352 |
| Sonnet 5.5 | $0.00009 | $0.00176 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
hurst-timing 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 1mo 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
Hurst指数择时
基本概念
Hurst指数基于分形市场理论，用于判断市场趋势的持续性和转折点。
分形市场理论
- 市场由不同投资期限的投资者组成
- 信息对不同投资者影响不同
- 价格变化具有长期记忆性
- 不是随机游走
Hurst指数含义
H ∈ (0, 1)
- H = 0.5: 随机游走，无记忆
- H > 0.5: 趋势持续（动量）
- H < 0.5: 反转持续（均值回归）
R/S分析方法
重标极差法
计算步骤：
- 将时间序列分为n个子区间
- 计算每个子区间的均值和累积离差
- 计算极差R
- 计算标准差S
- 计算R/S
长期记忆长度
Hurst指数峰值对应的n值：
- A股市场：n ≈ 233个交易日
- 含义：约1年的平均循环周期
策略模型
Local Hurst指数
移动计算Hurst指数，反映市场短期记忆性变化。
趋势判断
H > 0.55: 趋势持续 → 顺势操作
H < 0.55: 趋势可能反转 → 谨慎
H ≈ 0.5: 无明显趋势 → 观望
交易信号
买入信号
- Local Hurst指数连续5日低于E(H)
- 且市场较233日前上涨
- 第6个交易日发出买入信号
卖出信号
- Local Hurst指数连续5日低于E(H)
- 且市场较233日前上涨
- 满仓状态下第6个交易日发出卖出信号
A股实证
长期记忆特征
- 上证综指：H = 0.61（n=233）
- 深证成指：H = 0.61（n=233）
- 结论：A股具有长期记忆性
市场转折识别
- Hurst指数低位（<0.55）与市场反转期吻合
- 市场反转时H接近0.5
- 说明A股不完全有效
策略特点
优点
- 理论扎实：基于分形市场理论
- 长期视角：考虑市场长期记忆性
- 转折识别：能识别主要市场拐点
局限
- 参数选择：n值影响结果
- 信号延迟：转折后才会出现信号
- 不精准：只能判断大致位置
实践建议
参数设置
- n值：200-250个交易日
- 阈值：0.55-0.60
- 连续天数：5-10天
配合使用
- 结合趋势指标确认信号
- 结合成交量验证
- 设置止损
注意事项
- 市场环境：对趋势明显的市场更有效
- 参数稳定：不需频繁调整
- 辅助工具：作为辅助判断工具使用
适用场景
- 大盘趋势判断
- 主要转折点识别
- 长期择时
- 配合其他择时指标
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.
- 1mo ago First seen · 128 lines · 47 tokens per session scan A ea2fa6829ab7
hurst-timing is a skill published in the GitHub repository FeiCoder/Skill-Factory (11 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 880 once invoked, about $0.0002 per session on Opus 5.5. 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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