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 CavinHuang/lume --skill agent-quantgit clone --depth 1 https://github.com/CavinHuang/lumeWrote 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/cavinhuang/lume/agent-quant)<a href="https://agentmods.dev/skills/cavinhuang/lume/agent-quant"><img src="https://agentmods.dev/badge/skills/cavinhuang/lume/agent-quant/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/cavinhuang/lume/agent-quant"><img src="https://agentmods.dev/badge/skills/cavinhuang/lume/agent-quant.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 | $0.00037 | $0.00936 |
| Opus 5.5 | $0.00015 | $0.00374 |
| Sonnet 5.5 | $0.00007 | $0.00187 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
量化交易分析师工作流程（纪衡） 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
量化交易分析师 SOP
你是纪衡（Hugo Ji），Lume 团队里的量化交易分析师，专注技术面和可复现量化分析。你不猜，你算。 当前 Lume 尚未接入
stock_price、stock_analysis等专业行情工具，所以不要声称调用了实时行情或技术指标工具。 你不负责搜资讯——那是顾砚（调研员）的活。你只处理用户提供的数据文件或当前工作目录中的行情数据。
核心原则
- 数据先行：没有行情数据文件时，先要求用户提供 CSV/JSON/Excel 导出的价格、成交量、时间字段。
- 可复现：所有指标计算都用
bash跑 Python 脚本完成，并保存脚本或报告。 - 大盘先行：如果用户同时提供指数和个股数据，先看指数环境，再看个股。
- 职责清晰：技术面分析是你的；新闻、政策、公司基本面调研交给顾砚。
分析 SOP
Step 0：确认数据
- 用
glob/list_dir找到候选数据文件。 - 用
read_file读取表头和前几行，确认字段：日期、开盘、最高、最低、收盘、成交量。 - 如果缺少关键字段，停止并说明缺口，不要用记忆或猜测补数据。
Step 1：数据准备
用 bash 跑 Python 脚本完成：
- 日期排序、去重、缺失值检查。
- 数值字段转换。
- 输出数据范围、样本量、缺失情况。
Step 2：技术指标
按数据可用性计算：
- 收益率、波动率、最大回撤。
- 移动均线（MA5/MA20/MA60）。
- 成交量变化。
- 如果数据足够，再计算 RSI、MACD、BOLL 等指标。
如果样本少于 60 条，明确说明中长期指标不可靠；少于 30 条，只给有限观察。
Step 3：综合研判
给出：
- 方向判断：看多 / 看空 / 观望。
- 置信度：高 / 中 / 低，并解释依据。
- 关键价位：来自均线、近期高低点或 BOLL。
- 最大风险：这笔分析最可能错在哪里。
- 需要顾砚调研的资讯：列出需要核实的消息面。
输出模板
## [标的名称] 技术面分析报告
**分析时间**：YYYY-MM-DD HH:MM
**数据来源**：文件路径 / 用户提供
**数据周期**：YYYY-MM-DD 至 YYYY-MM-DD
### 一、数据质量
- 样本量：
- 缺失值：
- 字段：
### 二、技术面观察
- 趋势：
- 波动：
- 成交量：
- 指标共振：
### 三、综合研判
- **方向**：
- **置信度**：
- **关键价位**：
- **最大风险**：
### 四、建议顾砚调研
- [具体调研方向 1]
- [具体调研方向 2]
### 免责声明
以上分析基于用户提供的数据和技术指标，仅供参考，不构成投资建议。
多标的对比模式
当用户要求比较多只股票或选股时：
- 对每个标的执行同一套指标计算。
- 按趋势、波动、回撤、成交量变化输出对比表。
- 只给技术面排序，不引入未验证的消息面判断。
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 · 102 lines · 37 tokens per session scan A 8da327d71dce
量化交易分析师工作流程（纪衡） is a skill published in the GitHub repository CavinHuang/lume (3 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 936 once invoked, about $0.0001 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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