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 shouldnotappearcalm/a-share-skill --skill tuige-shortline-tradinggit clone --depth 1 https://github.com/shouldnotappearcalm/a-share-skillWrote 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/shouldnotappearcalm/a-share-skill/tuige-shortline-trading)<a href="https://agentmods.dev/skills/shouldnotappearcalm/a-share-skill/tuige-shortline-trading"><img src="https://agentmods.dev/badge/skills/shouldnotappearcalm/a-share-skill/tuige-shortline-trading/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/shouldnotappearcalm/a-share-skill/tuige-shortline-trading"><img src="https://agentmods.dev/badge/skills/shouldnotappearcalm/a-share-skill/tuige-shortline-trading.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 | $0.00070 | $0.01352 |
| Opus 5 | $0.00035 | $0.00676 |
| Sonnet 5 | $0.00014 | $0.00270 |
| Haiku 4.5 | $0.00007 | $0.00135 |
Grade A, and why
tuige-shortline-trading 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tuige Shortline Trading
适用范围
这个 skill 用来把退哥体系中的短线规则整理成可复用的决策框架，重点回答 4 类问题：
- 当前市场环境适合做什么
- 哪些股票值得进入观察和候选池
- 不同交易场景下的触发条件与失效条件
- 仓位、纪律、退出规则如何统一约束
能力边界
- 输出的是决策参考，不是收益承诺
- 优先使用结构化规则，不直接复述口号
- 不自动下单，不替代回测，不代替投顾
- 遇到模糊概念时先回到
glossary.md
推荐使用顺序
- 先读 market-regime.md
- 再读 stock-selection.md
- 根据场景进入以下文档：
- 最后用 exit-failure-rules.md 和 position-discipline.md 复核
- 如需统一术语，查 glossary.md
场景地图
api-reference/market-regime.md- 先判断今天是否允许激进短线、仅允许回调确认，还是应该空仓
api-reference/stock-selection.md- 统一股票池构建、强势股筛选、回避规则
api-reference/trend-setups.md- 处理趋势延续、回踩均线、缩量整理后的再起
api-reference/limit-up-pullback-setups.md- 处理涨停后整理结构，例如三阴不破阳、揉搓线、缩倍量回调
api-reference/relay-setups.md- 处理一进二、二进三、2+2 这类高风险接力
api-reference/washout-breakout-setups.md- 处理黄金坑、假跌破、极度缩量后放量突破等洗盘末端确认
api-reference/exit-failure-rules.md- 统一止盈兑现、趋势失效、结构失败、风险离场
api-reference/position-discipline.md- 统一仓位等级、试错纪律、降频规则
标准输出模板
输出时统一包含以下结构：
- 当前市场环境
- 今日允许使用的场景模块
- 候选池分级
- 当前结构类型
- 触发条件
- 失效条件
- 卖出观察点
- 仓位建议
- 明确回避原因
单只标的建议使用以下字段组织：
- 标的
- 所属题材
- 当前结构
- 入场依据
- trigger
- invalidation
- 卖出参考
- risk
- position_grade
使用约束
- 所有结论尽量写成“条件 / 风险 / 失效”
- 没有触发，不算买点
- 市场环境是总开关，环境不支持时不强行升级结论
- 同一只标的若同时命中多个场景，优先保留风险更低、定义更清晰的场景解释
工程化转换
当前目录已提供可执行脚本，采用统一的 daily_decisions / realtime_quotes / strategy_lab 结构：
scripts/daily_decisions.py- 主板流动性池扫描 +
trend_pullback信号 - 输出两类买入参考：
from_previous_day_close与from_last_close - 支持持仓文件卖出信号检查
- 默认最大持仓上限
8（可通过--max-holdings下调）
- 主板流动性池扫描 +
scripts/realtime_quotes.py- 对指定代码批量拉取现价快照
scripts/strategy_lab/strategy_params.py- 参数与仓位上限配置
scripts/strategy_lab/strategies.py- 规则引擎核心（当前版本：趋势回踩 + RSI 区间 + 失效退出）
What ships with it
9 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.
- api-reference/exit-failure-rules.md 2.0 KB
- api-reference/glossary.md 3.1 KB
- api-reference/limit-up-pullback-setups.md 2.2 KB
- api-reference/market-regime.md 2.7 KB
- api-reference/position-discipline.md 2.2 KB
- api-reference/relay-setups.md 2.3 KB
- api-reference/stock-selection.md 2.0 KB
- api-reference/trend-setups.md 2.0 KB
- api-reference/washout-breakout-setups.md 2.5 KB
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.
- 11d ago First seen · 125 lines · 70 tokens per session scan A ac7822f60a96
tuige-shortline-trading is a skill published in the GitHub repository shouldnotappearcalm/a-share-skill (238 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 1,352 once invoked, about $0.0003 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.
Other skills, from other repositories
china-dcf-model
A documented discounted-cash-flow model for valuing non-financial A-share companies. A discounted-cash-flow model estimates today’s value from expected future cash flows and makes its assumptions, valuation date, and adjustments explicit.
industry-etf-research
A research workflow for studying Chinese A-share industry ETFs and the indexes they track. It compares index rules, holdings, company fundamentals, industry conditions, valuations and market evidence.
pptx-author
Produce a .pptx file on disk for headless Chinese A-share market research, using python-pptx with Chinese font support. Use when no controllable Office session is available.
china-market-data
A routing and quality-check process for obtaining Chinese stock-market and company-financial data from sources such as Tushare Pro and AKShare.
china-market-researcher
A research workflow for analysing Chinese-listed stocks by industry or investment theme, including companies, competitors, ETFs, and market data. A-share means shares listed on mainland Chinese stock exchanges.
a-share-factor-validation
A research checklist and toolkit for testing stock-selection factors and backtests in China's A-share market, the mainland Chinese stock market. It checks whether results avoid future-data leakage and account for trading rules, costs, and statistical uncertainty.