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 ma-converge-divergegit 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/ma-converge-diverge)<a href="https://agentmods.dev/skills/travisun/opptrix/ma-converge-diverge"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/ma-converge-diverge/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/travisun/opptrix/ma-converge-diverge"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/ma-converge-diverge.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00097 | $0.00705 |
| Opus 5 | $0.00048 | $0.00352 |
| Sonnet 5 | $0.00019 | $0.00141 |
| Haiku 4.5 | $0.00010 | $0.00071 |
Grade A, and why
ma-converge-diverge 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 7d 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
均线收敛与发散
方法溯源 开源证券量化评论（91）：对价格与多条均线做截面标准差，因子 −log(1+std)，值越大越收敛。
何时使用
用户要识别 多均线收敛/发散 形态，并作因子/状态解读（选股或择时辅助）。
边界：易混 @skill:lean-ma-cross-trend、@skill:lean-ema-cross-universe（勿合并）。
分析架构
- 问题：均线束是在收敛还是发散？
- 证据：日收盘；窗口列表（默认 5/10/20/60）
- 信号：
delta（因子差分）或zscore模式
数据维度
| 维度 | 取数方向 | 缺失时 |
|---|---|---|
| 标的/宇宙 | search_instruments |
先确认 |
| 日 K | get_instrument_chart / batch → workspace_write |
— |
| 计算 | opptrix_run |
— |
| 交付 | create_web |
— |
步骤
- 确认
params.windows与signal_mode。 - 写入日 K（单标的主路径；多标的可分次跑）。
python scripts/ma_converge_diverge.py --input data.json --output result.json- 解读
series.convergence_factor（越大越收敛）与signal。 create_web。
依赖
- 纯 Python 标准库。
禁止
- 荐股；把信号写成买卖指令
- 脚本内联网取数（jqdata / tushare / qlib / HTTP 行情）
- 假装完整回测引擎或伪造胜率
- 无交付就结束（默认 web）
- 与
lean-*技能合并或冒充
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.
- 7d ago First seen · 62 lines · 97 tokens per session scan A 1b6567475f15
ma-converge-diverge is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 3d ago), licensed Apache-2.0. It adds 97 tokens to every session and 705 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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