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 earnings_momentum_setup_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/earnings_momentum_setup_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/earnings_momentum_setup_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_momentum_setup_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/earnings_momentum_setup_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_momentum_setup_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 | $0.00073 | $0.01753 |
| Opus 5.5 | $0.00029 | $0.00701 |
| Sonnet 5.5 | $0.00015 | $0.00351 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
earnings_momentum_setup_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
80% identical to breakout_candidate_finder_skill — 190 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Momentum Setup 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“结果本身强、市场反应也强、且后续仍可能继续走强”的候选股。输出重点不是简单找财报大涨股，而是判断财报惊喜的质量、市场定价是否仍有延续空间，以及哪些个股具备从一次性事件走向阶段性强势的条件。
功能简介
本技能的核心功能是：寻找财报后强势延续、具备继续上行动能的机会股。
它强调把业绩质量、指引变化、市场反应、量价跟进和预期差延续结合起来判断，帮助使用者找到值得继续跟踪的财后强势股。
使用场景
- 财报季中需要快速找出值得重点跟踪的强势个股。
- 用户在业绩公布后判断是否存在进一步趋势交易机会。
- 团队要从大量财报结果中筛出最有延续性的赢家。
- 盘前或盘后需要更新财后候选池和观察顺序。
工作原则
- 财后强势必须同时满足结果质量与市场反应质量，不能只看涨幅。
- 要区分“一次性利好刺激”与“预期差持续扩散”。
- 必须把业绩、指引、价格、量能和行业背景联动判断。
- 所有结论都要说明延续条件和失效条件，避免把事件股直接视为趋势股。
- 正文中不显式描述内部实现方式。
执行流程
Step 1：确定财报窗口与观察清单
先确认本次分析覆盖的是：
- 最新一个财报日窗口
- 某个行业或板块的财报批次
- 用户持仓或自选股中的财后反应
- 还是全市场寻找强势财后机会
并同步确认观察目标是次日跟踪、几日延续，还是更长的波段机会。
Step 2：先判断财报“强”在什么地方
真正值得跟踪的财后强势，不应只来自表面超预期。要重点拆解：
- 收入、利润、利润率、现金流等关键指标是否同步改善
- 超预期来自核心经营变量，还是一次性项目
- 指引是否上修，管理层措辞是否更积极
- 市场此前最担心的问题是否在本次财报中被有效回应
只有先确认“财报质量高”，后续的价格反应才更有解释力。
Step 3：识别市场反应是否体现增量定价
对财后表现进行二次判断，重点包括：
- 价格反应是否显著且具有承接，而非高开低走
- 放量上涨是否显示增量资金愿意追随
- 若股价上涨有限，是否反映市场此前已有充分预期
- 若结果不错但股价不强，真正的压制因素是什么
市场反应的方向与力度，往往决定后续能否继续走出动量。
Step 4：判断强势是否具备延续条件
财后机会的核心，是判断事件后是否还能继续强化。重点确认：
- 新指引或经营趋势是否支持未来数周继续上修预期
- 估值是否仍可接受，未因一天上涨被迅速透支
- 行业内是否存在同向验证，带来板块层面的扩散
- 市场环境是否愿意给财后赢家更高溢价
Step 5：区分最强标的与普通赢家
财报后上涨的股票很多，但真正值得持续跟踪的往往很少。需要区分：
- 结果强且反应强的“核心赢家”
- 结果强但反应一般、仍需观察的“潜在赢家”
- 反应强但结果质量一般的“情绪赢家”
优先级排序必须让用户知道“谁最值得继续花时间”。
Step 6：给出跟踪窗口与验证节点
对重点候选，明确说明：
- 第一天看什么
- 接下来 3-5 个交易日看什么
- 哪类回踩仍属健康，哪类走弱意味着失败
- 是否适合追踪、等待回调，还是只做事件复盘
Step 7：补充反向风险与失效情形
财后强势股也常见失败场景，需要主动说明：
- 高开透支后冲高回落
- 板块无法跟随，个股孤立走强
- 结果超预期但指引不够支撑后续上修
- 交易过热导致短期拥挤
Step 8：整合成财后强势成稿
最终输出应帮助使用者知道“哪几只财后赢家最值得继续跟、为什么、下一步看什么”。正文只呈现结论、依据与验证框架。
输出结构
财后强势机会筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最值得继续跟踪的财后赢家：{名称列表}
- 强势来源：{业绩质量 / 指引上修 / 预期差释放 / 板块共振}
- 当前操作倾向：{继续跟踪 / 等待回踩 / 谨慎不追}
- 最大风险：{一句话}
二、候选池总览
| 股票 | 财报质量 | 市场反应质量 | 延续潜力 | 拥挤风险 | 优先级 |
|---|---|---|---|---|---|
| {标的1} | {强/中/弱} | {强/中/弱} | {高/中/低} | {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 · 168 lines · 73 tokens per session scan A 417c81a673cb
earnings_momentum_setup_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 73 tokens to every session and 1,753 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 80% identical to breakout_candidate_finder_skill, differing in 190 lines, and is treated as a copy.
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