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Investor Panel

CommunityPopular
wbh604
investor-panel

66 位投资大佬评审团。给定一只股票的 dimensions.json 和 raw_data.json，让 66 位投资者各自按自己的方法论打分并输出 Pydantic Signal（signal/confidence/score/verdict/comment）。覆盖经典价值派、成长投资派、宏观对冲派、技术趋势派、中国价投派、A股游资派、量化系统派、科技领袖派、AI 卡位猎手 9 大流派。当用户请求"评审团/65 大佬怎么看/某某会买吗/做一次大佬投票"时使用。

Overview

Publisherwbh604
RepositoryUZI-Skill
Skill nameinvestor-panel
Stars
6.9K
Forks
971
Bundled files
11
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 11 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 Investor Panel 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/investor-panel .claude/skills/investor-panel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Investor Panel 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 Investor Panel 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 Investor Panel 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.

Investor Panel · 50 贤评审团

调用上下文

读取以下输入：

  • .cache/{ticker}/dimensions.json — 19 维评分
  • .cache/{ticker}/raw_data.json — 原始数据
  • scripts/lib/investor_db.py — 65 人元数据
  • scripts/lib/seat_db.py — 22 位游资射程规则

输出：

  • .cache/{ticker}/panel.json — 50 个 Signal + 投票统计

严格输出格式（Pydantic Signal，抄自 ai-hedge-fund）

每个投资者必须返回严格 JSON：

json
{
  "investor_id": "buffett",
  "name": "巴菲特",
  "group": "A",
  "avatar": "avatars/buffett.svg",
  "signal": "bullish | neutral | bearish",
  "confidence": 87,
  "score": 82,
  "verdict": "强烈买入 | 买入 | 关注 | 观望 | 等待 | 回避 | 不达标 | 不适合",
  "reasoning": "1-3 句具体逻辑",
  "comment": "用该投资者语言风格的金句 1-2 句",
  "pass": ["..."],
  "fail": ["..."],
  "ideal_price": 16.20,
  "period": "3-5 年"
}

Confidence 校准规则：

  • 85-100：核心方法论硬指标全部命中或全部不命中
  • 60-84：多数命中
  • 30-59：部分命中、需要等待信号
  • 0-29：方法论不适用此股 / 信息不足

执行步骤

Step 1: 加载元数据

python
from lib.investor_db import INVESTORS, by_group
from lib.seat_db import SEATS, is_in_range

Step 2: 对每位投资者

  1. 取出 fields 白名单
  2. 从 dimensions.json 提取相关字段
  3. 读取该投资者所在 group 的 reference 文件（按需）
  4. 用该投资者的方法论 + 语言样本生成 Signal（Claude 自己生成）
  5. 校验 JSON 合法性

Step 3: 游资射程预过滤（F 组特殊）

对 22 位游资，先用 is_in_range(nickname, ticker_features) 判断是否在射程内：

  • 在射程 → 正常评分
  • 不在射程 → signal: "neutral", verdict: "不适合", confidence: 90, comment: "{nick}的射程是{style}，这只票不在风格内。"

Step 4: 汇总投票

python
{
  "panel_consensus": (bullish_count / 50) * 100,
  "vote_distribution": Counter(verdict for i in investors),
  "signal_distribution": Counter(signal for i in investors),
  "investors": [...]
}

7 大流派详细方法论

按需读取下列 references：

组文件人数
A 经典价值references/group-a-classic-value.md6
B 成长投资references/group-b-growth.md4
C 宏观对冲references/group-c-macro-hedge.md5
D 技术趋势references/group-d-technical.md4
E 中国价投references/group-e-china-value.md6
F 游资references/group-f-china-youzi.md22
G 量化系统references/group-g-quant.md3

📚 语料库 (必读)

每次生成 comment 之前必须读 references/quotes-knowledge-base.md 查找该投资者的真实公开原话和"风格"字段。这是知识库 single source of truth。

语言风格守则

每位投资者的 comment 字段必须像他本人：

  • 巴菲特：温和、引用奥马哈、用"我们"
  • 芒格：刻薄、反向思维、引用心理学偏误
  • 索罗斯：哲学化、提"反身性"
  • 章盟主：豪迈、提"格局"、不谈细节
  • 赵老哥：直接、谈"题材"、谈"二板"
  • 段永平：朴素、问"商业模式""人""价格"
  • 陈小群：江湖气、谈"分歧""一线天""核按钮"

每组 reference 文件末尾有 3-5 句真实公开语录作为 few-shot。

完成检查

  • panel.json 包含 50 个 Signal
  • 每个 Signal 字段齐全
  • 22 位游资里至少有 N 位返回"不适合"（除非这只票是热门题材龙头）
  • panel_consensus / vote_distribution / signal_distribution 三个汇总字段已计算

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 Investor Panel AI skill do?

66 位投资大佬评审团。给定一只股票的 dimensions.json 和 raw_data.json，让 66 位投资者各自按自己的方法论打分并输出 Pydantic Signal（signal/confidence/score/verdict/comment）。覆盖经典价值派、成长投资派、宏观对冲派、技术趋势派、中国价投派、A股游资派、量化系统派、科技领袖派、AI 卡位猎手 9 大流派。当用户请求"评审团/65 大佬怎么看/某某会买吗/做一次大佬投票"时使用。

Why use Investor Panel on TypingMind?

Because you install it once and use it with any model. Investor Panel 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 Investor Panel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wbh604/UZI-Skill/tree/main/skills/investor-panel. 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 Investor Panel?

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 Investor Panel?

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

Is the Investor Panel 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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