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 pead_opportunity_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/pead_opportunity_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/pead_opportunity_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/pead_opportunity_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/pead_opportunity_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/pead_opportunity_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 | $0.00064 | $0.01783 |
| Opus 5.5 | $0.00026 | $0.00713 |
| Sonnet 5.5 | $0.00013 | $0.00357 |
| Haiku 4.5 | $0.00006 | $0.00178 |
Grade A, and why
pead_opportunity_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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PEAD Opportunity 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
本技能用于识别财报发布后不只在首日反应，而是在随后一段时间内继续沿着同一方向缓慢重定价的机会。输出重点不是解释财报当天为什么涨跌，而是判断预期修正是否仍在继续、市场是否还没有完全消化新信息，以及哪些标的更可能出现后续漂移行情。
功能简介
本技能的核心功能是：识别财报后漂移行情中值得跟踪的中短线机会。
它强调把财报信息增量、预期修正幅度、首日反应程度、后续量价表现和板块验证放在一起，帮助使用者抓住财后非首日的延续机会。
使用场景
- 财报公布后，用户希望寻找几日到数周的跟踪机会。
- 首日反应已过去，需要判断是否还有二次介入空间。
- 团队在财报季后需要整理持续跑赢的标的。
- 研究者想区分“一日游反应”和“持续漂移重定价”。
工作原则
- 漂移机会的关键是“市场消化未完成”，不能只看首日涨跌幅。
- 要同时分析基本面修正与价格延续，缺一不可。
- 必须给出漂移延续的时间窗口和失效边界。
- 若首日已过度透支，后续应下调漂移概率。
- 正文中不显式描述内部工具细节。
执行流程
Step 1：确认财报事件与观察窗口
先明确本次分析覆盖的是哪些财报事件，以及观察的是：
- 财后 2-5 日的短期漂移
- 财后 1-4 周的中期延续
- 某一行业的财后趋势群体
- 还是用户自选股中的持续强弱分化
时间窗口必须先定，否则很容易把不同阶段的行为混为一谈。
Step 2：评估财报带来的预期修正幅度
PEAD 的本质，是市场对新信息的消化分阶段完成。要先判断：
- 本次财报到底修正了哪些核心预期
- 修正是轻微、明显还是结构性改变
- 卖方、市场讨论和板块叙事是否出现同步上修
- 新信息是否足以改变未来几个季度的盈利或增长预期
没有足够强的预期修正，就很难支撑真正的漂移行情。
Step 3：判断首日反应是否仍留有空间
漂移机会往往发生在“好消息出现，但首日价格仍未完全反映”的场景。要重点看：
- 首日涨幅相对于财报惊喜幅度是否合理
- 首日成交是否显示新资金刚开始介入，还是一次性兑现
- 是否存在高开回落但后续仍稳步走强的可能
- 如果首日已经极度亢奋，后续漂移是否可能被压缩
Step 4：跟踪财后数日的价格与量能行为
真正的漂移，不是首日冲高，而是后续继续缓慢走强。要观察：
- 是否出现连续几日相对强于市场和板块的表现
- 回踩是否温和，是否有资金继续承接
- 成交结构是稳定放大，还是快速衰减
- 市场是否逐步将新估值与新预期内化到价格中
Step 5：检查板块与同业的验证情况
财后漂移通常不是孤立现象。还要判断：
- 同行业公司是否也出现类似预期修正
- 行业景气是否支持本次财报信息被持续强化
- 板块资金是否愿意持续配置这一方向
- 个股是否具备龙头溢价或独立逻辑
Step 6：给出候选排序与适合的跟踪方式
将候选分为：
- 具备明显漂移机会的重点对象
- 首日强但仍需观察延续性的中间对象
- 首日反应过度或后续承接不足的谨慎对象
并对每个重点标的写明：
- 漂移逻辑的来源
- 仍可观察的时间窗口
- 更适合追踪、等回踩，还是暂不参与
Step 7：明确失效边界
PEAD 机会一旦失败，通常失败得很明确。必须写清：
- 何种回落意味着市场已完成定价
- 哪些量价表现说明漂移逻辑中断
- 哪些新的负面信息会推翻财报后的正向修正
Step 8：整合成财后漂移成稿
最终输出应帮助使用者知道“哪些财后机会不是首日行情、而是值得继续跟踪的持续重定价”。正文只保留结论、依据与时间窗口。
输出结构
财后漂移机会筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最可能出现漂移的标的：{名称列表}
- 漂移驱动：{预期上修 / 指引强化 / 板块验证 / 定价未完成}
- 当前阶段判断：{正在形成 / 已有延续 / 接近完成}
- 主要失效风险：{一句话}
二、候选池总览
| 股票 | 预期修正幅度 | 首日定价是否充分 | 后续延续迹象 | 失效风险 | 优先级 |
|---|---|---|---|---|---|
| {标的1} | {高/中/低} | {不足/一般/偏充分} | {强/中/弱} | {高/中/低} | {A/B/C} |
| {标的2} | {等级} | {等级} | {等级} | {等级} | {等级} |
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 · 169 lines · 64 tokens per session scan A 7f2ad352ab3f
pead_opportunity_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 64 tokens to every session and 1,783 once invoked, about $0.0003 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 2026-09-04.
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