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-returns-momentumgit 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-returns-momentum)<a href="https://agentmods.dev/skills/travisun/opptrix/lean-returns-momentum"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/lean-returns-momentum.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 | $0.00091 | $0.01315 |
| Opus 5 | $0.00046 | $0.00658 |
| Sonnet 5 | $0.00018 | $0.00263 |
| Haiku 4.5 | $0.00009 | $0.00131 |
Grade A, and why
lean-returns-momentum 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 收益动量
方法溯源 QuantConnect LEAN 中基于历史收益 / 动量排序的算法思路；本技能用平台行情做窗口收益与相对强弱解读，禁止假装跑完整 LEAN 引擎。
何时使用
用户要在 A股/场内 ETF 上看过去 N 日/周收益所定义的动量状态，或对小集合做收益排名（LEAN 方法溯源，非美股原版照搬）。
边界：因子历史检验用 @skill:factor-research；正式回测用 @skill:run-backtest；ETF 全球轮动用 @skill:lean-etf-global-rotation。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。
- 默认 CN 宇宙动量排序；涨跌停截断收益；T+1 与不可卖空 → 动量多空改为「多头动量排序」。
- 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：在约定回看窗口下，标的/集合的相对动量如何？是否与波动或回撤不匹配？
- 证据清单：价格序列推得收益（事实）、窗口与宇宙（假设）、「动量延续」叙述（推断）
- 多维交叉验证：多窗口一致性；收益 vs 最大回撤（若可估）
- 结论与不确定：动量拥挤、反转风险、幸存者偏差
- 风险与缺口：样本短、集合过大无法批取、复权口径不明
- 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/集合 | search_instruments / ask_user |
先确认宇宙 |
| 快照/批量 | get_instrument_snapshot / batch_instrument_snapshots |
缩小集合 |
| 价格序列 | get_instrument_chart |
标明无法算收益 |
| 窗口参数 | ask_user |
显式默认并标假设 |
| 计算 | opptrix_run + 可选 workspace_write |
手工表并说明 |
| 交付 | list_web_vendor → create_web |
可跳过口头要点 |
| 市场/微观结构 | CN 收益序列 | 样本/停牌缺口须披露 |
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）。
- 确认宇宙与回看窗口（及是否多窗口）。
- 声明非 LEAN Runtime。
- 取价算收益；大集合优先批量快照并说明局限。
- 排序/状态表 + 交叉验证。
- 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
- 收益计算方法与口径
- 动量状态或排名表（事实）
- 多窗口一致性检查
- 事实 / 假设 / 推断
- 局限与过拟合警示
- A股适配与限制（默认 CN；微观结构/代理/完整度）
- 免责声明（排名≠荐股）
禁止
- 荐股；把高动量写成「必买」
- 禁止假装跑完整 LEAN 引擎或口头编造全历史动量曲线
- 禁止无交付就结束（默认 web）
- 把未取得的收益数字当事实
- 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 · 84 lines · 91 tokens per session scan A 093b6b17e31e
lean-returns-momentum is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed yesterday), licensed Apache-2.0. It adds 91 tokens to every session and 1,315 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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