Investor Panel Stock Review

What it does

When your agent uses it

• Getting a simulated multi-investor vote on a stock after its data is prepared
• Asking how a specific famous investor might view a given company
• Comparing value, growth, macro and quant viewpoints on one ticker

Example prompts

• “Run the investor panel on this stock using the dimensions and raw data files in its cache folder.”
• “Would Buffett buy this company? Show me his signal and the panel consensus.”
• “Do a vote of the famous investors on this ticker and show the distribution of verdicts.”

Requirements

• Prepared `dimensions.json` and `raw_data.json` files for the ticker
• The Python helper modules `investor_db.py` and `seat_db.py`

Workflow steps

4 steps, taken from the step headings in SKILL.md.

1. 1加载元数据
2. 2对每位投资者
3. 3游资射程预过滤（F 组特殊）
4. 4汇总投票

What it can do on your machine

Read from SKILL.md and the folder at commit 650788c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python and json).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Investor Panel Stock Review loads about 757 tokens when it runs, and up to ~27k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 169 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~62

When it runs · the whole SKILL.md, loaded when a task matches

~757

With references · SKILL.md plus every file in references/, read only if the agent opens them

~27k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wbh604/UZI-Skill at commit 650788c, republished under its MIT licence (© wbh604). 169 words, ~757 tokens.

name

investor-panel

description

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。

version

3.9.4

author

FloatFu-true

license

MIT

{
  "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"
}

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

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

© wbh604, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (references, assets) in skills/investor-panel of wbh604/UZI-Skill.

• SKILL.md
• assets/investor-cards.json
• references/group-a-classic-value.md
• references/group-b-growth.md
• references/group-c-macro-hedge.md
• references/group-d-technical.md
• references/group-e-china-value.md
• references/group-f-china-youzi.md
• references/group-g-quant.md
• references/group-i-serenity.md
• references/quotes-knowledge-base.md
• references/serenity-voice.md

Open the folder on GitHubat commit 650788c