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 chuancyzhang/deepseek-cowork --skill premarket_trade_checklist_skillgit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/premarket_trade_checklist_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/premarket_trade_checklist_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/premarket_trade_checklist_skill/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/chuancyzhang/deepseek-cowork/premarket_trade_checklist_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/premarket_trade_checklist_skill.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 | $0.00070 | $0.01509 |
| Opus 5.5 | $0.00028 | $0.00604 |
| Sonnet 5.5 | $0.00014 | $0.00302 |
| Haiku 4.5 | $0.00007 | $0.00151 |
Grade A, and why
premarket_trade_checklist_skill 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.
This is a copy
78% identical to breakout_candidate_finder_skill — 199 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Premarket Trade Checklist Skill
数据依赖与执行前置条件
推荐使用万得 wind-mcp-skill 获取底层数据。执行前先检查本地是否已有 wind-mcp-skill；若没有，提示用户安装：
Cowork 适配：所需子 Skill 已随
wind-aifinmarket固定快照内置，不执行安装或在线更新命令。
技能定位
本技能用于在开盘前完成最后一次“交易体检”。输出重点不是再做一遍大而全分析，而是用检查清单方式快速确认候选交易是否具备执行条件，哪些风险已知、哪些条件未满足、哪些单子应该先放弃，从而减少临盘冲动和操作遗漏。
功能简介
本技能的核心功能是：下单前检查催化剂、流动性与风险。
它强调把计划完整性、当日环境、事件风险和执行细节做最后核查，帮助使用者把盘前准备转化成一份真正可执行的操作单。
使用场景
- 用户在开盘前需要快速检查当天准备执行的候选交易。
- 团队晨会后，要把结论压缩成可以直接照着执行的清单。
- 盘前消息较多，用户担心遗漏重大事件或流动性风险。
- 用户想降低“明明想好了，开盘却做乱”的概率。
工作原则
- 盘前检查的目标是排雷和定序，不是重写完整研究报告。
- 每一笔候选交易都必须回答“今天为什么做、凭什么做、什么情况下不做”。
- 事件风险、流动性和环境适配度是盘前最优先检查项。
- 若任一关键条件未满足，应果断降级为观察而非勉强执行。
- 正文只输出检查清单与执行判断，不讨论内部实现过程。
执行流程
Step 1：确认当天候选池与执行优先级
先把当天准备处理的交易对象整理出来：
- 哪些是高优先级计划单
- 哪些只是观察单
- 哪些是已有持仓的延续动作
- 是否存在多笔交易争夺同一风险预算
Step 2：核查隔夜与盘前新增信息
开盘前先看有没有改变原计划的变量：
- 财报、公告、监管、政策、行业消息是否出现变化
- 海外市场、相关资产或板块是否出现明显扰动
- 公司是否有新增传闻、辟谣、停复牌或重要会议
- 原有催化剂是否强化、弱化或失效
Step 3：检查计划完整性
每笔候选单必须具备最少执行要素：
- 入场条件是否明确
- 仓位与分批节奏是否明确
- 止损与止盈规则是否明确
- 若触发后不符合预期，是否已有应对预案
缺少其中任一项，都不应被视为完整计划。
Step 4：评估当日执行环境
盘前最容易忽略的是“今天是否适合做这类单”：
- 指数和情绪环境是否支持追强或低吸
- 目标板块是否仍在主线或活跃方向
- 当日是否有重大宏观时点，可能放大波动
- 市场是否存在明显风格切换风险
Step 5：检查流动性与价格偏离风险
有计划不代表能舒服执行：
- 盘前价格是否已偏离理想区间
- 高开、低开会否改变原有风险回报
- 成交活跃度是否足以支持计划仓位
- 若开盘快速波动，是否需要延后执行
Step 6：输出执行、观察与放弃分层
把候选单按盘前检查结果分层：
- 可执行
- 需要等待确认
- 今日不做
同时说明降级原因，避免盘中再次被情绪翻案。
Step 7：形成盘前执行单成稿
最终输出应让使用者在开盘前最后几分钟清楚知道“先看谁、谁能做、等什么、什么单今天取消”。正文保留清单、优先级和放弃条件。
输出结构
盘前执行清单（{YYYY-MM-DD}）
一、30 秒结论
- 今日最高优先级交易：{名称列表}
- 可执行数量：{数量}
- 需等待确认数量：{数量}
- 应取消或降级数量：{数量}
- 今日主要风险：{一句话}
二、候选池检查表
| 股票 | 原计划方向 | 盘前新增变量 | 环境适配度 | 执行状态 | 备注 |
|---|---|---|---|---|---|
| {标的1} | {内容} | {内容} | {高/中/低} | {执行/等待/取消} | {说明} |
| {标的2} | {内容} | {内容} | {等级} | {状态} | {说明} |
三、重点执行单
{股票名称}
- 执行理由：{内容}
- 开盘前提：{内容}
- 若高开处理：{内容}
- 若低开处理：{内容}
- 放弃条件：{内容}
四、今日不做清单
- {标的1}：{原因}
- {标的2}：{原因}
五、开盘后前 30 分钟观察点
- {观察点1}
- {观察点2}
- {观察点3}
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 · 155 lines · 70 tokens per session scan A d020d3f5c8d7
premarket_trade_checklist_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 1,509 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 78% identical to breakout_candidate_finder_skill, differing in 199 lines, and is treated as a copy.
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