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_preview_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_preview_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/earnings_preview_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_preview_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_preview_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_preview_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 | $0.00051 | $0.01546 |
| Opus 5.5 | $0.00020 | $0.00618 |
| Sonnet 5.5 | $0.00010 | $0.00309 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
earnings_preview_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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Preview 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
本技能用于在财报发布前,把“市场在等什么、公司最该证明什么、如果结果不同会怎样”整理成一份高密度前瞻底稿。输出重点不是泛泛罗列财务指标,而是帮助使用者在财报前明确预期基线、关键验证点、潜在预期差和会前应重点关注的问题。
执行流程
Step 1:确认分析对象、财报周期与使用目的
先确认股票名称、上市地、本次对应的财报周期,以及用户此时要解决的问题:
- 是为了会前快速准备
- 是为了判断是否值得参与财报交易
- 是为了给团队做前瞻讨论底稿
- 是为了在持仓前明确风险暴露
若用户未说明,默认按“财报公布前的研究准备”展开,并聚焦最近一次待发布财报。
Step 2:建立市场预期基线
先把市场已经形成的预期整理清楚,重点包括:
- 收入、利润、利润率等核心指标的市场预期区间
- 管理层上次给出的指引或暗示
- 卖方与市场讨论最集中的关键经营变量
- 上一季度财报后遗留的验证任务
- 当前股价隐含的预期温度是保守、中性还是偏热
这一阶段的目标不是直接下结论,而是先明确“市场已经知道什么、已经押注什么、还在争论什么”。
Step 3:拆解本次财报最重要的验证点
围绕公司当前阶段,提炼 3-5 个真正决定财报解读方向的核心问题。优先关注:
- 本季度业绩是否只是表面超预期,还是质量也同步改善
- 增长来源是量、价、结构还是一次性因素
- 利润率、费用率、现金流是否支持当前增长叙事
- 订单、用户、产能、库存、开店、出货等经营变量是否验证主线
- 管理层是否需要在本次财报中回应市场最大的疑虑
验证点必须足够具体,能够在财报正文、业绩会或后续公告里被验证,而不是宽泛口号。
Step 4:做情景推演与反应框架
在预期基线和关键变量明确后,对财报结果做条件化推演,至少覆盖:
- 明显超预期时,真正的惊喜会来自哪里
- 略超预期但股价不涨时,市场可能在担心什么
- 表面达标但指引偏弱时,解读应如何调整
- 若核心经营变量不及预期,市场会优先交易哪条风险链条
- 哪些情况下需要把“好财报”和“好股票表现”区分开看
这一步的重点是把“结果”与“市场反应”拆开处理,避免把财报前瞻写成单向押注。
Step 5:形成会前关注清单
基于前述分析,整理出财报前最值得带着去看的问题,包括:
- 财报发布后要先核对的三个数字
- 业绩会最值得听的两个问题方向
- 哪些措辞变化会明显改变判断
- 哪些风险即使财报不错也不能忽略
- 若结果模糊,应等待哪些补充信息再下结论
Step 6:整合成前瞻成稿
最终输出应是面向行动的前瞻底稿,不展示中间检索路径、信息收集过程或内部草稿。正文不需要解释信息如何获得,只需要呈现预期、分歧、推演和行动重点。
输出结构
{股票名称}财报前瞻({YYYY-MM-DD})
一、30 秒结论
- 本次财报最重要的看点:{一句话}
- 市场当前最关注的变量:{一句话}
- 最容易出现预期差的地方:{一句话}
- 财报前整体判断:{偏积极 / 中性等待 / 偏谨慎}
二、市场预期基线
| 维度 | 当前预期 | 说明 |
|---|---|---|
| 收入 | {数值或区间} | {说明} |
| 利润/EPS | {数值或区间} | {说明} |
| 关键经营指标 | {数值或区间} | {说明} |
| 指引预期 | {上修 / 持平 / 下修预期} | {说明} |
三、本次最关键的验证点
- 验证点 1:{内容}
- 验证点 2:{内容}
- 验证点 3:{内容}
四、情景推演
情景 A:明显超预期
- 触发条件:{条件}
- 可能反应:{判断}
情景 B:结果平稳但反应分化
- 触发条件:{条件}
- 可能反应:{判断}
情景 C:不及预期
- 触发条件:{条件}
- 可能反应:{判断}
五、财报前风险提示
- {风险 1}
- {风险 2}
- {风险 3}
六、会前问题清单
- {问题 1}
- {问题 2}
- {问题 3}
质量要求
- 前瞻必须围绕“预期差”展开,而不是重复公司背景资料。
- 关键看点应尽量具体到指标、业务模块或管理层表态方向。
- 要区分“财报结果好坏”和“股价反应强弱”这两个层次。
- 若市场预期并不清晰,应明确写出预期不一致,而不是强行给出单一口径。
- 文本要能直接服务财报前准备与会后验证,避免写成泛泛研究摘要。
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 · 149 lines · 51 tokens per session scan A 7384f8707831
earnings_preview_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,546 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 2026-09-04.
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