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wbh604/uzi-skill179 installs

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。

How do I install this agent skill?

npx skills add https://github.com/wbh604/uzi-skill --skill investor-panel
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The investor-panel skill is a financial analysis tool that simulates a panel of 50+ investors to rate stocks. It is functionally safe but lacks boundary markers or sanitization when processing external data files, creating a potential surface for indirect prompt injection if the stock data files were to contain malicious instructions.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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:

{
  "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

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

{
  "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

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/wbh604/uzi-skill/investor-panel">View investor-panel on skillZs</a>