/plugin marketplace add wbh604/UZI-Skill
/plugin install stock-deep-analyzer@uzi-skill

/analyze-stock 贵州茅台

克隆 https://github.com/wbh604/UZI-Skill ，读 AGENTS.md 了解怎么用，帮我深度分析 贵州茅台。

请按照 https://raw.githubusercontent.com/wbh604/UZI-Skill/main/.codex/INSTALL.md 的指引安装 UZI-Skill，然后帮我深度分析 贵州茅台。

gemini extensions install https://github.com/wbh604/UZI-Skill

git clone https://github.com/wbh604/UZI-Skill
cd UZI-Skill
pip install -r requirements.txt

/analyze-stock 水晶光电        # by name
/analyze-stock 002273          # by A-share code
/analyze-stock 00700.HK        # Hong Kong
/analyze-stock AAPL            # US stock

分析 贵州茅台，用远程模式，生成一个公网链接让我手机能看。

UZI-Skill/
├── .claude-plugin/
│   ├── plugin.json              # Plugin manifest
│   └── marketplace.json         # Marketplace config
├── commands/                    # 14 slash command definitions
├── skills/
│   ├── deep-analysis/           # Main workflow (6 Tasks)
│   │   ├── SKILL.md             # Agent analyst handbook
│   │   ├── references/          # Methodology docs (8 papers)
│   │   ├── assets/              # HTML templates + 51 investor avatars
│   │   └── scripts/
│   │       ├── lib/
│   │       │   ├── fin_models.py              # DCF/Comps/LBO/3-Stmt/Merger
│   │       │   ├── research_workflow.py       # 7 research output types
│   │       │   ├── deep_analysis_methods.py   # 6 PE/IB/WM methods
│   │       │   ├── investor_criteria.py       # 51 personas × 180 rules
│   │       │   ├── investor_evaluator.py      # Rule engine
│   │       │   ├── stock_features.py          # 108 normalized features
│   │       │   └── ...
│   │       ├── fetch_*.py                     # 22 dimension fetchers
│   │       ├── compute_deep_methods.py        # Institutional model calc
│   │       ├── assemble_report.py             # HTML assembly
│   │       └── run_real_test.py               # Main pipeline
│   ├── investor-panel/          # Standalone panel skill
│   ├── lhb-analyzer/            # 龙虎榜 (hot-money tracker) skill
│   └── trap-detector/           # Pump-and-dump detector skill
├── requirements.txt
├── LICENSE
└── README.md

Stage 1 (scripts)
  └─ fetch_*.py          → Pull 22 data dimensions (price, fundamentals,
  └─ compute_deep_methods.py   technicals, sentiment, supply chain…)
  └─ investor_evaluator.py     → Score each of 51 personas against 180 rules

        ⏸️  <HARD-GATE> — Agent must read data, role-play each investor,
            write qualitative judgments, override rules with context
            (e.g. Buffett knows Apple is BRK's top holding → override bullish)

Stage 2 (scripts)
  └─ assemble_report.py  → Synthesize judgments → render HTML + image cards

# skills/scripts/run_real_test.py is the main entry point
import subprocess

result = subprocess.run(
    ["python", "skills/deep-analysis/scripts/run_real_test.py",
     "--ticker", "002273",
     "--market", "A",      # A | HK | US
     "--output", "./reports/"],
    capture_output=True, text=True
)
print(result.stdout)

# Each dimension has its own fetcher with multi-source fallback
from skills.deep_analysis.scripts.lib.stock_features import StockFeatureEngine

engine = StockFeatureEngine(ticker="002273", market="A")
features = engine.fetch_all()   # returns dict of 108 normalized features

# Key feature groups:
print(features["valuation"])    # PE, PB, PS, EV/EBITDA, PCF
print(features["growth"])       # Revenue/profit YoY, QoQ, 3Y CAGR
print(features["quality"])      # ROE, ROIC, gross margin, FCF yield
print(features["technical"])    # MA, MACD, RSI, volume ratio, ATR
print(features["sentiment"])    # North-bound flow, margin balance, short interest
print(features["governance"])   # Insider ownership, pledge ratio, audit opinion

from skills.deep_analysis.scripts.lib.investor_criteria import INVESTOR_REGISTRY
from skills.deep_analysis.scripts.lib.investor_evaluator import InvestorEvaluator

evaluator = InvestorEvaluator(features=features)

# Score all 51 investors
results = evaluator.evaluate_all()

for investor in results:
    print(f"{investor['name']:12s} | Score: {investor['score']:3d} | "
          f"Stance: {investor['stance']:8s} | "
          f"Triggered: {investor['triggered_rules']}")

# Evaluate one investor persona against loaded features
result = evaluator.evaluate_single("巴菲特", features)

# Example output structure:
# {
#   "name": "巴菲特",
#   "score": 62,
#   "stance": "neutral",
#   "summary": "观望：护城河 27/40 可见；但 ROE 5 年最低 6.7%，达标率仅 0/5",
#   "triggered_rules": [
#       {"rule": "asset_debt_ratio < 0.4", "met": True,  "label": "资产负债率 30% 保守"},
#       {"rule": "roe_5y_min > 0.15",      "met": False, "label": "ROE 5 年最低 6.7%"},
#   ]
# }

from skills.deep_analysis.scripts.lib.fin_models import DCFModel

dcf = DCFModel(
    ticker="002273",
    market="A",
    # A-share defaults (override as needed):
    risk_free_rate=0.025,      # rf = 2.5%
    equity_risk_premium=0.06,  # ERP = 6%
    tax_rate=0.25,             # China corporate tax
    terminal_growth=0.025,     # Gordon Growth g = 2.5%
)

valuation = dcf.run()

print(f"WACC:            {valuation['wacc']:.2%}")
print(f"Intrinsic Value: ¥{valuation['intrinsic_value']:.2f}")
print(f"Current Price:   ¥{valuation['current_price']:.2f}")
print(f"Safety Margin:   {valuation['safety_margin']:.1%}")
print(f"Sensitivity:\n{valuation['sensitivity_table']}")  # 5×5 DataFrame

from skills.deep_analysis.scripts.lib.fin_models import CompsModel

comps = CompsModel(ticker="002273", market="A")
result = comps.run()

# result["peer_table"] → DataFrame with PE/PB/EV-EBITDA for each peer
# result["percentiles"] → where subject sits vs peers (0–100)
# result["implied_targets"] → target prices from each multiple
print(result["peer_table"].to_string())
print(f"PE Percentile: {result['percentiles']['pe']:.0f}th")
print(f"Implied target (PE-based): ¥{result['implied_targets']['pe']:.2f}")

from skills.deep_analysis.scripts.lib.research_workflow import ICMemo

memo = ICMemo(ticker="002273", market="A", features=features)
output = memo.generate()

# Sections: executive_summary, investment_thesis, risk_factors,
#           scenario_analysis, valuation_bridge, catalysts,
#           portfolio_fit, recommendation
print(output["scenario_analysis"])
# Bull: ¥26.95 (p=30%)  Base: ¥20.73 (p=50%)  Bear: ¥14.51 (p=20%)

# Example: fetch_realtime_price.py internal logic (simplified)
PRICE_SOURCES = [
    ("eastmoney_push2", fetch_eastmoney),   # Primary
    ("xueqiu",          fetch_xueqiu),      # Fallback 1
    ("tencent",         fetch_tencent),     # Fallback 2
    ("sina",            fetch_sina),        # Fallback 3
    ("baidu",           fetch_baidu),       # Fallback 4
]

for source_name, fetch_fn in PRICE_SOURCES:
    try:
        data = fetch_fn(ticker)
        if data and data.get("price"):
            return data
    except Exception as e:
        log.warning(f"{source_name} failed: {e}, trying next...")

raise DataFetchError(f"All price sources failed for {ticker}")

# From investor_criteria.py — each investor is a dataclass with:
# - group: A–G
# - style: value / growth / macro / technical / china_value / youzi / quant
# - rules: list of Rule objects (field, operator, threshold, weight, label)
# - skip_markets: markets this investor ignores (e.g. 赵老哥 skips US)
# - override_conditions: context-based manual overrides

INVESTOR_GROUPS = {
    "A": ["巴菲特", "格雷厄姆", "芒格", "费雪", "邓普顿", "卡拉曼"],          # Classic Value
    "B": ["林奇", "欧奈尔", "蒂尔", "木头姐"],                                # Growth
    "C": ["索罗斯", "达里奥", "霍华德马克斯", "德鲁肯米勒", "罗伯逊"],        # Macro Hedge
    "D": ["利弗莫尔", "米内尔维尼", "达瓦斯", "江恩"],                        # Technical
    "E": ["段永平", "张坤", "朱少醒", "谢治宇", "冯柳", "邓晓峰"],            # China Value
    "F": ["章盟主", "赵老哥", "炒股养家", "佛山无影脚", "北京炒家", "鑫多多",
           # ... 17 more 游资 personas],                                       # A-share 游资
    "G": ["西蒙斯", "索普", "大卫·肖"],                                       # Quant Systems
}

# Agent override examples (from SKILL.md):
# - Buffett analyzing Apple → agent knows BRK #1 holding → force bullish override
# - 赵老哥 analyzing US stock → agent skips (游资 don't trade US)
# - 木头姐 analyzing 白酒 → agent applies "not disruptive innovation" → bearish override

# A sample of Buffett's rules from investor_criteria.py:
BUFFETT_RULES = [
    Rule(field="roe_5y_avg",        op=">=", threshold=0.15,  weight=10, label="ROE 5年均值≥15%"),
    Rule(field="roe_5y_min",        op=">=", threshold=0.15,  weight=10, label="ROE 5年最低≥15%"),
    Rule(field="debt_to_equity",    op="<=", threshold=0.5,   weight=8,  label="负债权益比≤0.5"),
    Rule(field="gross_margin",      op=">=", threshold=0.4,   weight=8,  label="毛利率≥40%"),
    Rule(field="fcf_yield",         op=">=", threshold=0.05,  weight=7,  label="自由现金流收益≥5%"),
    Rule(field="moat_score",        op=">=", threshold=30,    weight=12, label="护城河评分≥30/40"),
    Rule(field="pe_ratio",          op="<=", threshold=25,    weight=6,  label="PE≤25x"),
    Rule(field="insider_ownership", op=">=", threshold=0.1,   weight=5,  label="内部人持股≥10%"),
    # ... more rules
]

reports/002273/
├── report.html          # Full self-contained report (~600KB), open in browser
├── share_vertical.png   # 1080×1920 portrait card for WeChat Moments
├── share_horizontal.png # 1920×1080 landscape card for group sharing
└── summary.txt          # Plain-text summary for copy-paste

# Test a single fetcher in isolation
python skills/deep-analysis/scripts/fetch_realtime_price.py --ticker 002273

# Run with verbose fallback logging
python run_real_test.py --ticker 002273 --verbose

pip install --upgrade akshare
# UZI requires akshare >= 1.10.0
python -c "import akshare; print(akshare.__version__)"

pip install -r requirements.txt
# Core deps: akshare, yfinance, pandas, numpy, jinja2, Pillow, requests, duckduckgo-search

ls -lh reports/002273/report.html   # Should be ~500KB–1MB
# If <10KB, the assembly step failed — check assemble_report.py logs

# Correct ticker formats:
# HK:  "00700.HK"  or  "0700.HK"   (with .HK suffix)
# US:  "AAPL"  or  "TSLA"          (plain uppercase)
# A:   "002273"  or  "600519"       (6-digit code)
# A:   "贵州茅台"  or  "水晶光电"    (Chinese name also works)

在继续生成报告之前，请先以每位评委的身份分别给出判断，不要直接运行 Stage 2 脚本。

git checkout develop
pip install -r requirements.txt

# Proxy (if in restricted network)
export HTTP_PROXY="http://127.0.0.1:7890"
export HTTPS_PROXY="http://127.0.0.1:7890"

# Output directory (default: ./reports/)
export UZI_OUTPUT_DIR="/path/to/reports"

# Report language (default: zh, options: zh | en)
export UZI_LANG="zh"

# Disable image card generation (faster, HTML only)
export UZI_NO_IMAGES=1

# Remote mode tunnel (Cloudflare Tunnel, for mobile access)
export UZI_REMOTE=1

# Basic analysis
/analyze-stock 贵州茅台

# With specific focus
分析 002273，重点看 DCF 估值和机构持仓变化

# Panel vote only (faster)
/panel-only 600519

# Check for manipulation patterns
/scan-trap 300999

# Generate IC memo for an investment decision
/ic-memo TSLA

# Quick pre-market scan
/quick-scan 00700.HK

# Full English report for US stock
/analyze-stock NVDA