UZI-Skill is a collection of coding-agent skills for analyzing individual stocks across Chinese, Hong Kong, and United States markets using public data, investor viewpoints, quantitative rules, and institutional analysis methods. It is for users who want an agent to produce detailed stock-analysis reports. The catalogue entries are the commands, skills, instructions, plugin, agent, and hook that provide this workflow in supported coding agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wbh604/UZI-Skill --skill investor-panelgit clone --depth 1 https://github.com/wbh604/UZI-SkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wbh604/uzi-skill/investor-panel)<a href="https://agentmods.dev/skills/wbh604/uzi-skill/investor-panel"><img src="https://agentmods.dev/badge/skills/wbh604/uzi-skill/investor-panel/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wbh604/uzi-skill/investor-panel"><img src="https://agentmods.dev/badge/skills/wbh604/uzi-skill/investor-panel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00135 | $0.01367 |
| Opus 5 | $0.00068 | $0.00683 |
| Sonnet 5 | $0.00027 | $0.00273 |
| Haiku 4.5 | $0.00014 | $0.00137 |
Grade A, and why
investor-panel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- investor-panel — 86% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- 取出
fields白名单 - 从 dimensions.json 提取相关字段
- 读取该投资者所在 group 的 reference 文件（按需）
- 用该投资者的方法论 + 语言样本生成 Signal（Claude 自己生成）
- 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.md |
6 |
| B 成长投资 | references/group-b-growth.md |
4 |
| C 宏观对冲 | references/group-c-macro-hedge.md |
5 |
| D 技术趋势 | references/group-d-technical.md |
4 |
| E 中国价投 | references/group-e-china-value.md |
6 |
| F 游资 | references/group-f-china-youzi.md |
22 |
| G 量化系统 | references/group-g-quant.md |
3 |
📚 语料库 (必读)
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/investor-cards.json 9.0 KB
- references/group-a-classic-value.md 4.4 KB
- references/group-b-growth.md 2.8 KB
- references/group-c-macro-hedge.md 2.7 KB
- references/group-d-technical.md 2.4 KB
- references/group-e-china-value.md 2.9 KB
- references/group-f-china-youzi.md 5.6 KB
- references/group-g-quant.md 1.6 KB
- references/group-i-serenity.md 8.2 KB
- references/quotes-knowledge-base.md 59 KB
- references/serenity-voice.md 11 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 122 lines · 135 tokens per session scan A 17054fcf13cb
investor-panel is a skill published in the GitHub repository wbh604/UZI-Skill (6,861 stars, last pushed 6d ago), licensed MIT. It adds 135 tokens to every session and 1,367 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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