Uzi Skill Stock Analyzer

UZI Skill — Stock Deep Analyzer

Skill by ara.so — Daily 2026 Skills collection.

UZI Skill transforms any AI coding agent into a private stock analyst. Feed it a ticker and it runs 22 data dimensions, applies 17 institutional analysis methods (DCF, Comps, LBO, IC Memo, etc.), and simulates 51 distinct investor personas (Buffett through Chinese retail游资) each scoring the stock against their own quantitative rule sets. Output is a self-contained HTML report, shareable image cards, and a plain-text summary.

Supported markets: A股 (SZ/SH), 港股 (HK), 美股 (US) Data sources: All free — akshare, 东方财富, 雪球, yfinance, DuckDuckGo (zero API keys required)

Installation

Claude Code (recommended)

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

Then run:

/analyze-stock 贵州茅台

Other Agents — Universal Install

Paste this into any agent (Codex, Cursor, Gemini CLI, Windsurf, Devin):

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。

Codex

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 CLI

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

Manual Clone

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

Quick Start

Full Deep Analysis (5–8 minutes)

/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

Mobile / Remote Mode

When away from a computer, ask any agent:

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。

The agent launches with --remote to start a Cloudflare Tunnel and returns a https://xxx.trycloudflare.com URL.

All Slash Commands

Project Structure

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

Architecture: Two-Stage Agent Pipeline

The analysis is split into two script stages with a mandatory agent gate in between. The <HARD-GATE> tag in SKILL.md forces the agent to intervene — it cannot be skipped.

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

Core Python Modules — Usage Examples

Running the Full Pipeline Directly

# 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)

Fetching Stock Features (22 Dimensions)

# 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

Running the Investor Panel

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']}")

Single Investor Deep Score

# 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%"},
#   ]
# }

DCF Valuation

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()