trade_plan_builder_skill

trade_plan_builder_skill is a skill for Claude Code, Codex from chuancyzhang/deepseek-cowork. It costs 59 tokens per session (1,782 once invoked), scanned A, a copy of breakout_candidate_finder_skill, Apache-2.0.

A trading plan builder for one proposed trade, covering entry conditions, position size, stop-loss and profit-taking levels, review points, and emergency actions.

In plain words
What is it for?
Use it to prepare before trading, compare candidate stocks, organise a pre-market plan, or support team review.
Why use it?
It turns a trading idea into rules that can be checked before placing an order.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare before trading, compare candidate stocks, organise a pre-market plan, or support team review.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chuancyzhang/deepseek-cowork/trade_plan_builder_skill
Install

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.

Any agent
npx skills add chuancyzhang/deepseek-cowork --skill trade_plan_builder_skill
Clone the repo
git clone --depth 1 https://github.com/chuancyzhang/deepseek-cowork

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for trade_plan_builder_skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/trade_plan_builder_skill/github.svg)](https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/trade_plan_builder_skill)
Your own site
<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.

agentmods 80×15 button for trade_plan_builder_skill

Your own site · 80×15
<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>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,782 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 83% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash 5aa6a39c895f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

Origin

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.

ai_skills/wind-aifinmarket/skills/trade_plan_builder_skill/SKILL.md · 180 lines

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

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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}

Read the full file on GitHub · 180 lines

Changes

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.

  1. 1mo ago First seen · 180 lines · 59 tokens per session scan A 5aa6a39c895f

Subscribe to this mod's changes

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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