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-equal-weight-pcmgit 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-equal-weight-pcm)<a href="https://agentmods.dev/skills/travisun/opptrix/lean-equal-weight-pcm"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/lean-equal-weight-pcm.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 | $0.00099 | $0.01319 |
| Opus 5 | $0.00049 | $0.00660 |
| Sonnet 5 | $0.00020 | $0.00264 |
| Haiku 4.5 | $0.00010 | $0.00132 |
Grade A, and why
lean-equal-weight-pcm 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 等权组合（PCM）
方法溯源 QuantConnect LEAN 中等权 / Portfolio Construction Model 类思路；本技能产出等权目标权重与偏离说明，禁止假装跑完整 LEAN 引擎，也不是下单指令。
何时使用
用户要在 A股/场内 ETF 给定成分集合上构建或对照 1/N 等权目标（LEAN 方法溯源，非美股原版照搬）。
边界：用户已有任意目标权重、只需差额清单用 @skill:rebalance；均值方差优化用 @skill:lean-mean-variance；风险平价用 @skill:lean-risk-parity。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 股票/场内 ETF 篮子等权；再平衡受 T+1 与涨跌停可成交性约束。
- 禁止假设空头腿等权。
- 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：在等权规则下，各成分目标权重是多少？相对现持仓偏离多少？
- 证据清单：成分列表与市值/现持仓（事实）、等权规则（假设）、执行路径叙述（推断）
- 多维交叉验证：权重和=100%；偏离加总与现金项
- 结论与不确定：未含成本/税/停牌；等权≠最优
- 风险与缺口：无成分、无市值、无法读持仓
- 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 |
|---|---|---|
| 成分宇宙 | ask_user / search_instruments |
先确认 |
| 现持仓（可选） | get_portfolio_holdings / portfolio_summary |
仅出目标权重表 |
| 市值/价格 | batch_instrument_snapshots |
标明名义金额不可算 |
| 计算 | opptrix_run / workspace_write |
手工等权表 |
| 交付 | list_web_vendor → create_web |
可跳过口头要点 |
| A股组合 | CN 标的清单 + 价格 | ask_user 确认篮子 |
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）。
- 确认成分集合与是否对照现持仓。
- 声明非 LEAN Runtime / 非下单。
- 计算 1/N 目标权重；有持仓则算偏离。
- 校验权重和与缺口。
- 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
- 等权目标权重表（事实计算）
- 相对现持仓偏离（若有）
- 现金与约束说明（假设）
- 事实 / 假设 / 推断
- 成本/流动性局限
- A股适配与限制（默认 CN；微观结构/代理/完整度）
- 免责声明（方案≠下单；无荐股）
禁止
- 荐股；把等权表写成买卖指令
- 禁止假装跑完整 LEAN 引擎
- 擅自改成非等权却称为等权
- 禁止无交付就结束（默认 web）
- 与
@skill:rebalance混淆：本技能定义等权目标；rebalance 吃用户任意目标 - 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 · 99 tokens per session scan A f51fa6cf3617
lean-equal-weight-pcm is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed yesterday), licensed Apache-2.0. It adds 99 tokens to every session and 1,319 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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