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-rsi-reversiongit 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-rsi-reversion)<a href="https://agentmods.dev/skills/travisun/opptrix/lean-rsi-reversion"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/lean-rsi-reversion.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 | $0.00092 | $0.01313 |
| Opus 5 | $0.00046 | $0.00656 |
| Sonnet 5 | $0.00018 | $0.00263 |
| Haiku 4.5 | $0.00009 | $0.00131 |
Grade A, and why
lean-rsi-reversion 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 RSI 均值回归
方法溯源 QuantConnect LEAN 中 RSI 超买/超卖与均值回归类算法思路；本技能做阈值状态与规则解读，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 RSI 是否进入超买/超卖区、是否出现回归迹象（LEAN 方法溯源，非美股原版照搬）。
边界：指标手册用 @skill:lean-indicator-playbook；趋势均线用 @skill:lean-ma-cross-trend；平台综合信号用 @skill:instrument-signals。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 标的；涨跌停区间 RSI 常钝化（持续极限值），回归假设须分层说明。
- 禁止自由做空「超买做空」模板。
- 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 RSI 周期与阈值下，标的是否处于极端区？价格是否已出现背离或回归？
- 证据清单：RSI 序列与价格（事实）、阈值/周期（假设）、回归概率叙述（推断）
- 多维交叉验证：RSI 极值 vs 价格新高新低；与趋势均线是否冲突（趋势市慎用回归）
- 结论与不确定：强趋势中「超买可更超买」；阈值任意性
- 风险与缺口：缺 RSI、周期过短、阈值未约定
- 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 | search_instruments / ask_user |
先确认 |
| 快照 | get_instrument_snapshot |
标明无现价 |
| RSI/指标 | get_instrument_indicators |
not-feasible |
| 图表 | get_instrument_chart |
不虚构 |
| 阈值/周期 | ask_user |
显式默认（如 30/70）并标假设 |
| 计算 | opptrix_run |
可选 |
| 交付 | list_web_vendor → create_web |
可跳过口头要点 |
| 市场/微观结构 | CN 行情 + RSI | 涨跌停钝化则标注 |
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）。
- 确认标的、RSI 周期与超买超卖阈值。
- 声明非 LEAN Runtime。
- 取 RSI 与价格结构，标注时效。
- 判定区域与背离（若可观察）；注明趋势市冲突。
- 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
- RSI 定义与阈值规则
- 当前读数与区域（事实）
- 背离/冲突检查
- 事实 / 假设 / 推断
- 局限与观察清单
- A股适配与限制（默认 CN；微观结构/代理/完整度）
- 免责声明（无买卖建议）
禁止
- 荐股；把超卖写成「抄底建议」
- 禁止假装跑完整 LEAN 引擎或编造 RSI 历史胜率
- 禁止无交付就结束（默认 web）
- 静默改阈值却当作事实
- 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 · 86 lines · 92 tokens per session scan A 0d64b6e9d6e8
lean-rsi-reversion is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed yesterday), licensed Apache-2.0. It adds 92 tokens to every session and 1,313 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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