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 agentmods add skills/travisun/opptrix/candle-shadow-factornpx skills add Travisun/Opptrix --skill candle-shadow-factorgit 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/candle-shadow-factor)<a href="https://agentmods.dev/skills/travisun/opptrix/candle-shadow-factor"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/candle-shadow-factor.svg" alt="Measured on agentmods" 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 | $0.00074 | $0.00826 |
| Opus 5 | $0.00037 | $0.00413 |
| Sonnet 5 | $0.00015 | $0.00165 |
| Haiku 4.5 | $0.00007 | $0.00083 |
Grade A, and why
candle-shadow-factor 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
上下影线因子
溯源东吴「技术分析拥抱选股因子」上下影线系列：蜡烛图或威廉定义的上下影线，经近 5 日均值标准化，再对近 20 日取 mean/std；综合因子近似 Upper_std + Williams_lower_mean（无市值中性时须声明）。
何时使用
- 单标的时序影线因子，或小集合截面（bars 带
symbol） - 需要
factor序列 JSON 再网页解读
非目标：全 A 月度回测引擎；荐股排序当买卖单。
算法要点（事实）
| 模式 | 上影 | 下影 |
|---|---|---|
candle |
high − max(open,close) | min(open,close) − low |
williams（默认） |
high − close | close − low |
标准化：当日影线 / 过去 norm_lookback（默认 5）日影线均值。聚合：过去 agg_lookback（默认 20）日的 mean/std。
数据维度
| 维度 | 取数方向 | 缺失时 |
|---|---|---|
| 标的/宇宙 | search_instruments / ask_user |
先确认 |
| OHLCV | get_instrument_chart / batch_instrument_snapshots |
无法计算 |
| 模式参数 | ask_user |
默认 williams |
| 落盘 | workspace_write |
无法跑脚本 |
| 脚本 | get_agent_skill_file |
说明读出 |
| 计算 | opptrix_run |
标明失败 |
| 交付 | create_web |
可跳过口头 |
步骤
- 确认标的与
mode（candle/williams）。 - 取日 K OHLCV →
workspace_write。 - 准备
scripts/candle_shadow_factor.py。 opptrix_run：python scripts/candle_shadow_factor.py --input … --output …- 输出
series.factor；禁止据此点名「应买哪几只」。 - 分栏结论 → 默认
create_web。
依赖
仅 Python 标准库。禁止联网取数。
禁止
- 荐股；把低因子值写成「强烈买入」
- 假装已做市值中性/行业中性（未做须写假设）
- 无交付结束（默认 web）
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 70 lines · 74 tokens per session scan A 739bd0ccec83
candle-shadow-factor is a skill published in the GitHub repository Travisun/Opptrix (229 stars, last pushed yesterday), licensed Apache-2.0. It adds 74 tokens to every session and 826 once invoked, about $0.0004 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 2026-08-30.
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