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 take_profit_ladder_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/take_profit_ladder_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/take_profit_ladder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/take_profit_ladder_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/take_profit_ladder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/take_profit_ladder_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 | $0.00058 | $0.01488 |
| Opus 5.5 | $0.00023 | $0.00595 |
| Sonnet 5.5 | $0.00012 | $0.00298 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
take_profit_ladder_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
86% identical to breakout_candidate_finder_skill — 197 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.
Take Profit Ladder 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
本技能用于解决“赚了以后怎么卖”这一类最常见、也最容易失序的问题。输出重点不是追求卖在最高点，而是帮助使用者在兑现利润、保护浮盈和保留趋势空间之间找到平衡，建立一套可重复执行的卖出框架。
功能简介
本技能的核心功能是：设计分批止盈路径与兑现规则。
它强调先定义什么情况下该锁定部分利润，什么情况下应继续让利润奔跑，再把卖出节奏写成清晰动作。
使用场景
- 持仓已经盈利，但不知道该一次卖、分批卖还是继续拿。
- 强势股快速上冲，用户担心“卖飞”和“利润回吐”两头挨打。
- 波段交易接近目标区，需要预先设计退出节奏。
- 团队想把卖出纪律标准化，减少盘中拍板式止盈。
工作原则
- 止盈的目标是优化收益分布，不是追求最高卖点。
- 卖出规则要同时考虑目标位、走势强弱、量价结构和市场环境。
- 兑现利润与保留趋势空间必须并存，不能极端化。
- 分批安排应提前写定，避免盈利后临时随意改规则。
- 正文只给出判断与方案，不讨论内部实现过程。
执行流程
Step 1：确认持仓阶段与盈利性质
先搞清楚当前盈利来自哪里：
- 是事件驱动后的快速拉升，还是趋势中的稳步推进
- 当前盈利处于初期、中段还是偏后段
- 这笔交易原本是快进快出，还是愿意留一部分跟趋势
- 用户更担心利润回吐，还是更担心过早卖飞
Step 2：定义基础目标位与关键兑现区
把“差不多该卖了”变成更明确的层级：
- 第一兑现区通常对应什么位置
- 第二兑现区是否与前高、目标区或明显阻力相关
- 若价格远超预期，是否保留趋势尾仓
- 若未到目标区但结构显著转弱，是否提前调整计划
Step 3：设计分批卖出梯度
将仓位拆成更容易执行的节奏：
- 第一批用于锁定确定性利润
- 第二批用于对冲回撤风险
- 最后一批用于保留趋势延续弹性
同时明确：
- 每一批的触发条件是什么
- 是按价格层级卖，还是按走势变化卖
- 是否允许盘中部分兑现、收盘再确认剩余仓位
Step 4：设置保本上移与保护浮盈规则
盈利管理不只是卖出，还包括动态保护：
- 首次兑现后，是否把止损上移至成本线以上
- 若趋势继续强化，是否逐步抬高保护位
- 若回撤发生但仍未破坏结构，哪些波动可以容忍
- 在何种情况下应把“趋势单”切回“兑现单”
Step 5：结合环境判断是否加快或放缓兑现
卖出节奏必须服从环境：
- 市场情绪高涨时，可否适当延后卖出
- 板块退潮时，是否需要更积极兑现
- 个股是否出现放量滞涨、冲高回落、加速末端等信号
- 若外部风险提升，是否优先保护已有利润
Step 6：补充意外情景与追踪节点
提前写清常见变体：
- 高开直接到目标区怎么处理
- 未到目标区但出现明显转弱怎么处理
- 回踩后再创新高，是否允许卖后回补
- 如何定义“剩余仓位继续持有”的前提
Step 7：形成止盈成稿
最终输出应帮助使用者清楚知道“什么时候先卖、卖多少、为什么还留、剩余仓位靠什么继续拿”。正文保留层级计划、保护规则和情景应对。
输出结构
分批止盈方案（{YYYY-MM-DD}）
一、30 秒结论
- 持仓对象：{股票名称}
- 当前盈利阶段：{初期 / 中段 / 后段}
- 止盈倾向：{积极兑现 / 分批锁盈 / 趋势尾仓继续拿}
- 首个兑现触发点：{内容}
- 剩余仓位持有前提：{一句话}
二、止盈梯度
| 批次 | 触发条件 | 建议动作 | 剩余仓位逻辑 |
|---|---|---|---|
| 第一批 | {内容} | {内容} | {内容} |
| 第二批 | {内容} | {内容} | {内容} |
| 尾仓 | {内容} | {内容} | {内容} |
三、保护浮盈规则
- 保本上移条件：{内容}
- 动态保护位：{内容}
- 趋势持有前提：{内容}
四、异常情景应对
- {情景1}
- {情景2}
- {情景3}
五、风险提示
- {风险1}
- {风险2}
- {风险3}
质量要求
- 止盈方案必须体现分批逻辑，而不是单点目标价。
- 每一批动作都要有清晰触发依据。
- 需要说明何时保护浮盈、何时继续放利润。
- 环境变化对止盈节奏的影响必须被写明。
- 输出应帮助盈利仓管理，而不是制造“卖在最高点”执念。
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 · 58 tokens per session scan A ab069c757427
take_profit_ladder_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 1,488 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to breakout_candidate_finder_skill, differing in 197 lines, and is treated as a copy.
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