Earnings Reaction Interpreter Skill logo

Earnings Reaction Interpreter Skill

Organization
Wind-Alice
earnings_reaction_interpreter_skill

解读财报发布后的涨跌反应、超预期来源、市场真实分歧与后续观察点。适用于财报夜复盘、次日开盘前判断、会后更新观点等场景。

Overview

PublisherWind-Alice
RepositoryAliceMarket
Skill nameearnings_reaction_interpreter_skill
Stars
135
Forks
18
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Wind-Alice on GitHub. Read the source before you install it.

Installation

Install the Earnings Reaction Interpreter Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Wind-Alice/AliceMarket/tree/main/skills/earnings_reaction_interpreter_skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Wind-Alice/AliceMarket.git /tmp/AliceMarket
mkdir -p .claude/skills
cp -r /tmp/AliceMarket/skills/earnings_reaction_interpreter_skill .claude/skills/earnings_reaction_interpreter_skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Reaction Interpreter Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Earnings Reaction Interpreter Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Earnings Reaction Interpreter Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
# GitHub
npx skills add Wind-Information-Co-Ltd/wind-skills --skill wind-mcp-skill -g -y

# Gitee 镜像(国内)
npx skills add https://gitee.com/wind_info/wind-skills.git --skill wind-mcp-skill -g -y

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

  1. 必须同时解释“财报内容”和“市场反应”,不能只写其中一半。
  2. 若股价表现与 headline 不一致,要优先解释错位原因。
  3. 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。
  4. 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。
  5. 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。

Frequently asked questions

What does the Earnings Reaction Interpreter Skill AI skill do?

解读财报发布后的涨跌反应、超预期来源、市场真实分歧与后续观察点。适用于财报夜复盘、次日开盘前判断、会后更新观点等场景。

Why use Earnings Reaction Interpreter Skill on TypingMind?

Because you install it once and use it with any model. Earnings Reaction Interpreter Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Earnings Reaction Interpreter Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Wind-Alice/AliceMarket/tree/main/skills/earnings_reaction_interpreter_skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Earnings Reaction Interpreter Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Earnings Reaction Interpreter Skill?

As many as you like. As long as a model supports skills, you can use Earnings Reaction Interpreter Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Earnings Reaction Interpreter Skill AI skill free?

It is published on GitHub by Wind-Alice. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇