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 trade_plan_builder_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/trade_plan_builder_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/trade_plan_builder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/trade_plan_builder_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/trade_plan_builder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/trade_plan_builder_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 | $0.00059 | $0.01782 |
| Opus 5.5 | $0.00024 | $0.00713 |
| Sonnet 5.5 | $0.00012 | $0.00356 |
| Haiku 4.5 | $0.00006 | $0.00178 |
Grade A, and why
trade_plan_builder_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
83% identical to breakout_candidate_finder_skill — 210 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Plan Builder 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
本技能用于把“我想买这只股票”转化为一份可执行、可复核、可追责的交易计划。输出重点不是给一句看多或看空结论,而是把交易假设、触发条件、仓位安排、退出规则和计划失效点写清楚,避免临盘时被情绪接管。
功能简介
本技能的核心功能是:为单笔交易生成完整的买卖执行计划。
它强调把交易理由、执行顺序、风控边界与事后验证串成一套方案,帮助使用者在下单前先完成纪律设计。
使用场景
- 用户已经看好某只股票,但还没有形成可以直接执行的交易方案。
- 盘前需要把若干候选股写成可比较的标准化计划。
- 团队希望在交易前先完成一次结构化复核,减少拍脑袋决策。
- 用户想把“想法”与“动作”拆开,先验证计划再决定是否下单。
工作原则
- 交易计划必须同时回答“为什么做、何时做、做多少、错了怎么办、对了怎么处理”五个问题。
- 任何方向判断都要附带失效条件,不能只有乐观情景没有退出纪律。
- 执行计划必须区分理想路径与备选路径,避免计划只适用于单一行情。
- 如果风险回报结构不成立,应明确给出“不执行或继续等待”的结论。
- 正文只呈现分析框架和结论,不讨论内部实现细节。
执行流程
Step 1:确认交易对象、周期与计划目标
先明确这份计划服务于什么类型的交易:
- 是短线事件驱动、波段趋势,还是中线布局
- 计划是用于首次建仓、加仓、减仓,还是观察等待
- 使用者更看重突破确认,还是回调承接
- 预期持有周期是日内、数日、数周还是更长
若用户没有明确说明,默认按“针对单只股票的短中线首次建仓计划”展开。
Step 2:定义交易假设与核心驱动
先把交易逻辑写成一句能够被验证的话,再展开支撑依据:
- 价格为什么可能朝预期方向运动
- 驱动来自趋势、事件、预期差、估值修复还是板块共振
- 当前最关键的验证变量是什么
- 市场真正的分歧点落在哪里
只有当假设足够清晰,后续的入场与退出规则才有约束力。
Step 3:识别入场区域与触发条件
把“想买”拆成更具体的执行语言:
- 理想入场区间在哪里
- 需要看到什么价格行为、量能特征或事件确认才算触发
- 哪种走法属于提前透支,不应追价
- 若开盘直接偏离计划,应该等待、放弃还是切换备选方案
入场条件必须尽量客观,避免出现“感觉差不多就上”的模糊表述。
Step 4:设计仓位结构与分批节奏
在方向判断成立后,再决定投入规模:
- 首笔仓位占计划总仓位多少
- 预留仓位是否用于回踩承接、确认加仓或仅作为观察冗余
- 若波动较大,是否需要降低单次投入比例
- 若标的流动性一般,是否要采用更缓和的分批方式
仓位安排应与不确定性水平匹配,而不是与主观信心直接挂钩。
Step 5:设置止损、止盈与时间止损
一份合格计划必须提前写清退出纪律:
- 什么情况下判定计划失效
- 价格跌破何处必须退出,或至少显著降仓
- 走势符合预期后,准备如何分批锁定利润
- 若价格横盘迟迟不启动,是否设置时间止损
退出规则应覆盖价格失效、逻辑失效和时间失效三个层次。
Step 6:评估风险回报与情景分布
对计划做一次现实检验:
- 预期收益空间是否足以覆盖计划风险
- 最乐观、基准、最差三种情景分别如何演化
- 哪些外部因素最可能打断原本路径
- 该交易在当前市场环境下胜率和赔率是否匹配
如果赔率不够,宁可降低优先级,也不要勉强执行。
Step 7:补充执行前检查与盘中应对
在成稿前补齐实操层面的注意点:
- 近期是否存在财报、公告、政策、停复牌等重要事件
- 流动性是否足以支持计划仓位
- 盘中若出现跳空、高开低走或板块急变,应如何调整
- 若首笔执行后走势与预期严重背离,是否暂停后续动作
Step 8:整合成交易计划成稿
最终输出应帮助使用者在极短时间内知道“现在能不能做、怎么做、做完以后看什么、错了怎么退”。正文只保留判断依据、执行框架和纪律要求。
输出结构
交易计划({YYYY-MM-DD})
一、30 秒结论
- 交易对象:{股票名称}
- 计划方向:{试多 / 试空 / 等待}
- 执行倾向:{可执行 / 需等待确认 / 暂不执行}
- 核心依据:{一句话}
- 最大风险:{一句话}
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 · 180 lines · 59 tokens per session scan A 5aa6a39c895f
trade_plan_builder_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 1,782 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to breakout_candidate_finder_skill, differing in 210 lines, and is treated as a copy.
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