From stock-deep-analyzer
Runs a panel of 65 simulated investors across 9 investment schools to evaluate a stock. Processes dimensions.json and raw_data.json, outputs structured signals with confidence scores and a consensus vote. Useful for getting simulated investor opinions on a stock.
How this skill is triggered — by the user, by Claude, or both
Slash command
/stock-deep-analyzer:investor-panelThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
读取以下输入:
assets/investor-cards.jsonreferences/group-a-classic-value.mdreferences/group-b-growth.mdreferences/group-c-macro-hedge.mdreferences/group-d-technical.mdreferences/group-e-china-value.mdreferences/group-f-china-youzi.mdreferences/group-g-quant.mdreferences/group-i-serenity.mdreferences/quotes-knowledge-base.mdreferences/serenity-voice.md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:
from lib.investor_db import INVESTORS, by_group
from lib.seat_db import SEATS, is_in_range
fields 白名单对 22 位游资,先用 is_in_range(nickname, ticker_features) 判断是否在射程内:
signal: "neutral", verdict: "不适合", confidence: 90, comment: "{nick}的射程是{style},这只票不在风格内。"{
"panel_consensus": (bullish_count / 50) * 100,
"vote_distribution": Counter(verdict for i in investors),
"signal_distribution": Counter(signal for i in investors),
"investors": [...]
}
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 |
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。
每位投资者的 comment 字段必须像他本人:
每组 reference 文件末尾有 3-5 句真实公开语录作为 few-shot。
npx claudepluginhub jinzaizhichi/uzi-skill8plugins reuse this skill
First indexed Jun 10, 2026
Showing the 6 earliest of 8 plugins
Runs a panel of 65 simulated investors across 9 investment schools to evaluate a stock. Processes dimensions.json and raw_data.json, outputs structured signals with confidence scores and a consensus vote. Useful for getting simulated investor opinions on a stock.
Runs deep stock analysis on A-share/HK/US markets using 22 data dimensions, 17 institutional methods (DCF, Comps, LBO, IC Memo), and 51 investor personas. Generates HTML reports and image cards. Zero API keys required.
Routes investor reasoning overlays (Buffett, Graham, Lynch, etc.) grounded in LLMQuant Data for valuation and fundamental analysis workflows.