Earnings Momentum Setup Skill logo

Earnings Momentum Setup Skill

Organization
Wind-Alice
earnings_momentum_setup_skill

寻找财报发布后业绩与指引共同强化、量价表现积极、具备继续上行动能的机会股,并输出跟踪优先级、延续条件与失效边界。适用于财报交易、强势延续判断、事件后候选池整理等场景。

Overview

PublisherWind-Alice
RepositoryAliceMarket
Skill nameearnings_momentum_setup_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 Momentum Setup 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_momentum_setup_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_momentum_setup_skill .claude/skills/earnings_momentum_setup_skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Momentum Setup 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 Momentum Setup 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 Momentum Setup 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 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:

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

  1. 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。
  2. 要区分“一次性利好刺激”与“预期差持续扩散”。
  3. 必须把业绩、指引、价格、量能和行业背景联动判断。
  4. 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。
  5. 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}

三、重点候选拆解

{股票名称}

  • 财报亮点:{内容}
  • 市场反应解读:{内容}
  • 延续条件:{内容}
  • 失效边界:{内容}
  • 更适合的跟踪方式:{内容}

四、后续观察节点

  • {观察点1}
  • {观察点2}
  • {观察点3}

五、风险提示

  • {风险1}
  • {风险2}
  • {风险3}

质量要求

  1. 必须区分财报质量和股价反应质量两个层次。
  2. 不得把单日大涨直接等同于后续有趋势机会。
  3. 需给出延续条件与失效条件。
  4. 要解释预期差是否已被充分计入。
  5. 输出应直接服务财后候选池管理与跟踪。

Frequently asked questions

What does the Earnings Momentum Setup Skill AI skill do?

寻找财报发布后业绩与指引共同强化、量价表现积极、具备继续上行动能的机会股,并输出跟踪优先级、延续条件与失效边界。适用于财报交易、强势延续判断、事件后候选池整理等场景。

Why use Earnings Momentum Setup Skill on TypingMind?

Because you install it once and use it with any model. Earnings Momentum Setup 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 Momentum Setup Skill in TypingMind?

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

Which AI models can use Earnings Momentum Setup 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 Momentum Setup Skill?

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

Is the Earnings Momentum Setup 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.

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