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 after_close_watchlist_recap_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/after_close_watchlist_recap_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/after_close_watchlist_recap_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/after_close_watchlist_recap_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/after_close_watchlist_recap_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/after_close_watchlist_recap_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 | $0.00062 | $0.01070 |
| Opus 5.5 | $0.00025 | $0.00428 |
| Sonnet 5.5 | $0.00012 | $0.00214 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
after_close_watchlist_recap_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.
What it actually says
After Close Watchlist Recap 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
本技能用于把一组自选股的收盘后复盘工作标准化，帮助使用者回答三件事：今天谁强谁弱、强弱背后因为什么、明天还值不值得继续看。目标是为下一交易日输入更清晰的观察清单，而不是只做涨跌幅汇总。
执行流程
Step 1：回顾当日表现
逐只检查当日的关键表现，包括：
- 涨跌幅、振幅、成交变化、换手变化
- 日内是否出现冲高回落、探底回升、尾盘异动
- 与所属板块、指数相比是领涨、跟涨还是明显掉队
Step 2：解释表现驱动
优先判断今日表现主要由哪类因素驱动：
- 新增消息或公告
- 板块轮动或市场风格切换
- 技术位置触发
- 资金博弈或情绪宣泄
- 无明显催化，仅随市场波动
Step 3：识别强弱性质
把强弱分成更有决策意义的类型，而不只是看涨跌：
- 主动走强：脱离板块仍强
- 被动跟涨：板块带动明显
- 承压转弱：反弹无力或跌破结构
- 噪音波动：无明确主线变化
Step 4：提炼次日观察点
针对每只值得继续跟踪的股票，提炼一个最关键的次日验证点，例如：
- 是否能延续放量强势
- 是否会回补缺口
- 是否守住新形成的关键位
- 是否还有事件催化延续
Step 5：更新关注优先级
给出次日关注等级调整建议：
- 提高优先级：今天出现新主线、新强度或新催化
- 维持优先级：逻辑未变，但暂无增量信号
- 降低优先级：结构破坏、催化落空或关注度明显衰退
Step 6：输出收盘复盘摘要
最终输出应以“结果 + 解释 + 次日观察”为主，不展示逐步排查过程。
输出结构
自选股收盘复盘（{YYYY-MM-DD}）
一、今日最重要的复盘结论
- 最强观察对象：{股票/原因}
- 最弱观察对象：{股票/原因}
- 最值得带到明天的线索：{线索}
二、分股票复盘
| 股票 | 当日表现 | 驱动解释 | 强弱性质 | 次日观察点 | 关注级别 |
|---|---|---|---|---|---|
| {股票A} | {简述} | {原因} | 主动走强 | {看什么} | 提高 |
| {股票B} | {简述} | {原因} | 承压转弱 | {看什么} | 降低 |
三、板块与市场背景
- {哪些板块对自选股表现有明显影响}
- {市场风格是否变化}
四、明日准备
- 明天优先盯盘名单：{股票列表}
- 需要等待确认的股票：{股票列表}
- 可暂时移出核心关注的股票：{股票列表}
质量要求
- 复盘必须回答“为什么今天会这样”，而不只是记录结果。
- 结论要服务次日准备，避免写成纯记录型日报。
- 没有增量变化的股票可简写，不要平均用力。
- 强弱判断要结合板块与市场环境，不能脱离背景孤立评价。
- 若找不到明确驱动，应写明“暂无明确新增驱动”，避免过度解释。
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 · 109 lines · 62 tokens per session scan A cc8e4c742b73
after_close_watchlist_recap_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 1,070 once invoked, about $0.0002 per session on Opus 5.5. 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.
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