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Market Sentiment

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lxyeternal
market-sentiment

用数据判断市场处于"亢奋/分歧/冰点/修复"哪个阶段。用于A股短线交易,辅助判断仓位和交易策略。

Overview

Publisherlxyeternal
RepositoryMalSkillBench
Skill namemarket-sentiment
Stars
64
Forks
14
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lxyeternal on GitHub. Read the source before you install it.

Installation

Install the Market Sentiment 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/lxyeternal/MalSkillBench/tree/main/Dataset/Skills/benign/a-stock-market-sentiment
  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/lxyeternal/MalSkillBench.git /tmp/MalSkillBench
mkdir -p .claude/skills
cp -r /tmp/MalSkillBench/Dataset/Skills/benign/a-stock-market-sentiment .claude/skills/market-sentiment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Market Sentiment 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 Market Sentiment 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 Market Sentiment 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.

市场情绪量化 Skill

核心能力

用数据判断市场处于"亢奋/分歧/冰点/修复"哪个阶段,而不是靠感觉猜。

情绪判断规则

情绪冰点

  • 涨停家数 < 20家
  • 跌停家数 > 10家
  • 连板高度 < 3板
  • 操作建议:轻仓或空仓

情绪亢奋

  • 涨停家数 > 80家
  • 炸板率 < 15%
  • 连板高度 > 5板
  • 操作建议:重仓主线龙头

分歧阶段

  • 涨停家数波动大
  • 炸板率 > 25%
  • 操作建议:半仓参与辨识度高的标的

数据获取

调用 akshare-stock 获取:

  • 涨停家数:akshare_stock_limit_up()
  • 跌停家数:akshare_stock_limit_down()
  • 炸板率:(封板失败数 / 涨停家数) × 100%
  • 连板高度:最高连板数

实战流程

  1. 每天开盘前花5分钟获取数据
  2. 判断当前情绪阶段
  3. 匹配对应仓位策略
  4. 避免在冰点期盲目出手

触发场景

  • "今天市场情绪怎么样"
  • "现在是冰点还是亢奋"
  • "能不能加仓"
  • "今天适合重仓还是轻仓"

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Market Sentiment AI skill do?

用数据判断市场处于"亢奋/分歧/冰点/修复"哪个阶段。用于A股短线交易,辅助判断仓位和交易策略。

Why use Market Sentiment on TypingMind?

Because you install it once and use it with any model. Market Sentiment 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 Market Sentiment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lxyeternal/MalSkillBench/tree/main/Dataset/Skills/benign/a-stock-market-sentiment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Market Sentiment?

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 Market Sentiment?

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

Is the Market Sentiment AI skill free?

It is published on GitHub by lxyeternal. 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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