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 Travisun/Opptrix --skill lean-drawdown-riskgit clone --depth 1 https://github.com/Travisun/OpptrixWrote 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/travisun/opptrix/lean-drawdown-risk)<a href="https://agentmods.dev/skills/travisun/opptrix/lean-drawdown-risk"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/lean-drawdown-risk.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector 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 | $0.00098 | $0.01359 |
| Opus 5 | $0.00049 | $0.00679 |
| Sonnet 5 | $0.00020 | $0.00272 |
| Haiku 4.5 | $0.00010 | $0.00136 |
Grade A, and why
lean-drawdown-risk 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
LEAN 回撤风控
方法溯源 QuantConnect LEAN 中基于最大回撤 / 路径风险的风控与仓位缩放思路；本技能做回撤度量与规则状态解读，禁止假装跑完整 LEAN 引擎。
何时使用
用户要在 A股/场内 ETF 持仓或标的上关注历史或当前路径回撤、回撤阈值、减仓/熔断类规则是否触发（LEAN 方法溯源，非美股原版照搬）。
边界：一次性显式情景冲击（指数跌 X%）用 @skill:stress-test；稳健性参数网格用 @skill:robustness-check；正式策略回测 KPI 用 @skill:run-backtest。默认交付网页。
A股适配（默认）
- 默认市场 CN（A股 / 场内 ETF）。用户点名美股/港股再切换，并声明数据口径与微观结构差异。
- 组合默认 CN 持仓/关注列表；回撤规则在 T+1 与涨跌停下可能无法按美股假设即时减仓，须声明执行缺口。
- 不做空对冲假设。
- 不可硬适配或数据缺口时：首页横幅写清完整度（partial 或更严）+ 必要时
ask_user。
分析架构（投研方法）
- 问题/假设：在约定净值/价格路径上，最大回撤与当前回撤深度如何？是否触及用户阈值？
- 证据清单：净值或价格路径（事实）、阈值与规则（假设）、是否应缩放仓位的叙述（推断）
- 多维交叉验证：峰值日期 vs 谷底；单票回撤 vs 组合回撤（若有持仓）
- 结论与不确定：窗口选择敏感；未建模流动性
- 风险与缺口：无路径数据、阈值未定义
- 微观/制度风险：涨跌停钝化、T+1、ST/停牌、融券受限（及相关会计口径差异）；不得按美股连续可成交或自由做空假设叙事
- 事实 | 假设 | 推断 分栏强制
数据维度
| 维度 | 取数方向 | 缺失时 |
|---|---|---|
| 标的/组合 | ask_user / get_portfolio_holdings |
先确认对象 |
| 价格/净值路径 | get_instrument_chart / 用户提供序列 |
not-feasible |
| 回撤阈值 | ask_user |
显式默认并标假设 |
| 计算 | opptrix_run / workspace_write |
手工表并说明 |
| 交付 | list_web_vendor → create_web |
可跳过口头要点 |
| A股持仓微观结构 | CN 组合行情序列 | 无持仓清单 → ask_user |
步骤
- 确认默认 CN：标的/宇宙为 A股或场内 ETF（用户点名其他市场再切换并声明差异）。应用涨跌停/T+1/融券受限等微观约束（见 A股适配）。
- 确认分析对象（单标的净值路径或组合近似）与回撤阈值。
- 声明非 LEAN Runtime；与压力测试边界写清。
- 计算峰值—谷底回撤与当前深度。
- 对照规则状态（是否触发）；不做下单。
- 分栏结论 → 默认
create_web。
网页报告建议目录
- 范围：默认 A股/场内 ETF + LEAN 溯源
- 回撤定义与阈值（假设）
- 最大回撤与当前深度（事实）
- 规则触发状态
- 事实 / 假设 / 推断
- 与情景压力测试的差异说明
- A股适配与限制（默认 CN；微观结构/代理/完整度）
- 免责声明（风控规则解读≠买卖建议）
禁止
- 荐股；把「触及阈值」写成强制卖出指令
- 禁止假装跑完整 LEAN 引擎
- 用情景冲击替代路径回撤却不声明（应转
@skill:stress-test） - 禁止无交付就结束（默认 web）
- 编造未计算的回撤数字
- 禁止把美股成分/ETF 清单不经映射直接当 A股结果
- 禁止假设可自由融券做空
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 · 85 lines · 98 tokens per session scan A 584e303af95f
lean-drawdown-risk is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed yesterday), licensed Apache-2.0. It adds 98 tokens to every session and 1,359 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 2026-09-03.
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