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_reaction_interpreter_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_reaction_interpreter_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/earnings_reaction_interpreter_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_reaction_interpreter_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_reaction_interpreter_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_reaction_interpreter_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 | $0.01416 |
| Opus 5.5 | $0.00023 | $0.00566 |
| Sonnet 5.5 | $0.00012 | $0.00283 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
earnings_reaction_interpreter_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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Reaction Interpreter 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后，快速回答“股票为什么这样涨跌、真正超预期的是哪里、市场到底在交易什么、接下来还该盯什么”。输出重点不是把财报数字重抄一遍，而是把结果、反应和后续判断拆解清楚。
执行流程
Step 1：确认财报事实与价格反应窗口
先确认本次对应的财报周期、发布时间、盘后或盘前反应，以及当前观察窗口。优先梳理：
- 财报与指引是否已经完整披露
- 股价是在盘后、盘前还是次日盘中发生主要反应
- 反应幅度是否明显超出历史常态
- 同板块可比公司是否存在联动
若价格反应仍在快速变化，要明确当前解读仅代表该时点判断。
Step 2：拆开看“结果”与“表现”
把财报结果拆成多个层次，而不是只看单一 headline：
- 收入、利润、利润率等核心结果是否超预期
- 关键经营指标是否支持 headline 的表面好看
- 指引是否同步上修、持平或转弱
- 业绩质量是来自主业改善、结构优化，还是一次性项目
- 价格反应是在放大财报结论，还是与财报内容出现错位
这一步的目标是避免“数字超预期就等于利好”的机械判断。
Step 3：识别市场真正交易的核心分歧
从财报结果中提炼市场最在意的那条主线，常见包括：
- 增长是否可持续
- 利润率是否触顶或开始修复
- 某条业务线是否已经进入拐点
- 管理层指引是否反映更深层的问题
- 估值原本定价的故事是否被强化或削弱
如果股价与 headline 相反，需要重点解释“市场为何忽略表面利好”或“为何放大边际变化”。
Step 4：判断财报后的持续性与二次验证点
并非所有财报反应都能延续。需要进一步判断：
- 本次反应更像一次性情绪释放，还是趋势级重估起点
- 哪些细节可能在次日、次周被继续交易
- 哪些问题仍需等待业绩会、补充文件或管理层澄清
- 若短期市场解读过度，后续可能怎样回摆
Step 5：形成更新后的研究判断
结合财报结果、指引、市场反应和后续验证点，明确：
- 原有投资逻辑被强化、削弱还是基本未变
- 是否需要调整关注优先级
- 若继续跟踪，下一步最值得验证什么
- 若不立刻下结论，真正缺的是什么信息
Step 6：整合成财后复盘成稿
最终输出应直接服务财报夜复盘和次日判断，不展示中间搜集过程或内部推理。内容应优先呈现解释与判断，而非材料堆砌。
输出结构
{股票名称}财后反应解读（{YYYY-MM-DD}）
一、30 秒结论
- 结果层面：{明显好于预期 / 基本符合 / 低于预期}
- 反应层面：{涨跌幅与核心原因}
- 真正被交易的变量：{一句话}
- 当前判断：{逻辑强化 / 逻辑不变 / 逻辑转弱}
二、财报结果拆解
| 项目 | 市场预期 | 实际结果 | 初步解读 |
|---|---|---|---|
| 收入 | {数值} | {数值} | {说明} |
| 利润/EPS | {数值} | {数值} | {说明} |
| 关键经营指标 | {数值} | {数值} | {说明} |
| 指引 | {预期} | {实际} | {说明} |
三、为什么市场这样反应
- 正向驱动：{内容}
- 压制因素：{内容}
- 表面与实质的差异：{内容}
四、财报后真正的分歧点
- {分歧点 1}
- {分歧点 2}
- {分歧点 3}
五、后续还要看什么
- 短期验证点：{内容}
- 中期验证点：{内容}
- 可能改变判断的信号：{内容}
六、当前动作建议
- 建议：{继续跟踪 / 提高优先级 / 暂缓乐观 / 等待补充信息}
- 原因：{说明}
质量要求
- 必须同时解释“财报内容”和“市场反应”，不能只写其中一半。
- 若股价表现与 headline 不一致，要优先解释错位原因。
- 不能把所有变动都归因为单一指标，需突出真正决定反应的主变量。
- 财后判断应带时间窗口意识，避免把盘后情绪写成长期结论。
- 输出应帮助使用者决定“现在该如何理解这份财报”，而不是停留在结果复述。
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 · 133 lines · 58 tokens per session scan A b9279597d48c
earnings_reaction_interpreter_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 1,416 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
twitter-reader
Read Twitter/X for financial research through opencli: the user's home timeline, tweet search, trending topics, bookmarks, a user's recent tweets, threads, articles, profiles, followers and following, and notifications. Use this skill whenever the user wants to know what people are saying on Twitter/X or fintwit about…
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
energy-procurement
Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis…
airdrop-hunting-guide
Guide to finding and qualifying for crypto airdrops — eligibility criteria, farming strategies, on-chain activity requirements, Sybil detection avoidance, and tracking tools. Use when helping users understand airdrops, check eligibility, or plan participation strategies.
recipe-daily-pnl-report
Generate a daily profit and loss summary from trades and balances.