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Lhb Analyzer

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wbh604
lhb-analyzer

龙虎榜深度分析器。识别游资席位、判断机构 vs 游资博弈、对照同板块龙虎榜找辨识度龙头。当用户问"谁在买这只票/最近龙虎榜怎么样/X游资有没有上榜/这是不是X的票"时使用。

Overview

Publisherwbh604
RepositoryUZI-Skill
Skill namelhb-analyzer
Stars
6.9K
Forks
971
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

    Published by wbh604 on GitHub. Read the source before you install it.

Installation

Install the Lhb Analyzer 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.
  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/wbh604/UZI-Skill.git /tmp/UZI-Skill
mkdir -p .claude/skills
cp -r /tmp/UZI-Skill/skills/lhb-analyzer .claude/skills/lhb-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lhb Analyzer 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 Lhb Analyzer 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 Lhb Analyzer 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.

龙虎榜深度分析

调用上下文

输入:股票代码或个股名 输出:龙虎榜分析 + 游资识别 + 同板块对比

数据流

  1. 调用 scripts/fetch_lhb.py {ticker} 拿到原始龙虎榜数据
  2. 调用 lib/seat_db.py::match_seats_in_lhb() 识别游资席位
  3. lib/seat_db.py::is_in_range() 判断该游资是否在射程内
  4. 拉取同板块龙虎榜对比(找辨识度龙头)

输出 markdown 结构

markdown
# {name} ({ticker}) 龙虎榜分析

## 📅 近 30 天上榜 X 次

(列表)

## 🐉 识别到的游资 (Y 位)

| 游资 | 风格 | 在不在射程 | 买入 / 卖出 |
|---|---|---|---|
| 章盟主 | 大资金趋势 | ✅ 在射程 | 买 1.2 亿 |
| 佛山无影脚 | 一日游 | ❌ 不在 | 卖 0.3 亿 (反向预警) |

## ⚖️ 机构 vs 游资

- 机构净买入: ¥X 亿
- 游资净买入: ¥Y 亿
- 主导方: {机构 / 游资}

## 🏆 同板块辨识度龙头

| 排名 | 代码 | 名称 | 上榜次数 | 累计涨幅 |
|---|---|---|---|---|
| 1 | ... | ... | ... | ... |

本股在板块中的位置: 第 N

## 💡 结论一句话

"这是一只机构主导 + 章盟主格局票,板块辨识度排第 2,可以跟。"

参考资料

详细的 22 位游资席位百科见 references/seat-encyclopedia.md

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 Lhb Analyzer AI skill do?

龙虎榜深度分析器。识别游资席位、判断机构 vs 游资博弈、对照同板块龙虎榜找辨识度龙头。当用户问"谁在买这只票/最近龙虎榜怎么样/X游资有没有上榜/这是不是X的票"时使用。

Why use Lhb Analyzer on TypingMind?

Because you install it once and use it with any model. Lhb Analyzer 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 Lhb Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wbh604/UZI-Skill/tree/main/skills/lhb-analyzer. 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 Lhb Analyzer?

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 Lhb Analyzer?

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

Is the Lhb Analyzer AI skill free?

Yes. It is published on GitHub by wbh604 under the MIT license. 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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