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기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/tools-only/X-Skills --skill openbb-equity명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SKILL.md 표시 중
SOC 직업 분류 기준
| name | openbb-equity |
| description | Comprehensive equity analysis using OpenBB - historical prices,... |
Perform comprehensive stock analysis using the OpenBB Platform.
/openbb-equity TICKER [--analysis fundamental|technical|all] [--period 1y]
Retrieves and analyzes equity data for any stock ticker using OpenBB's comprehensive data sources.
First, verify OpenBB is installed:
try:
from openbb import obb
print("✅ OpenBB installed")
except ImportError:
print("⚠️ Installing OpenBB...")
import subprocess
subprocess.run(["pip", "install", "openbb"], check=True)
from openbb import obb
# Parse user input
import sys
ticker = sys.argv[1].upper() if len(sys.argv) > 1 else "AAPL"
analysis_type = "all" # fundamental, technical, or all
period = "1y" # 1d, 1w, 1m, 3m, 6m, 1y, 5y
# Parse flags
for arg in sys.argv[2:]:
if arg.startswith("--analysis="):
analysis_type = arg.split("=")[1]
elif arg.startswith("--period="):
period = arg.split("=")[1]
# Get historical prices
price_data = obb.equity.price.historical(
symbol=ticker,
interval="1d",
period=period
)
df = price_data.to_dataframe()
print(f"\n📈 Historical Prices for {ticker}")
print(f"Period: {period}")
print(f"Latest Close: ${df['close'].iloc[-1]:.2f}")
print(f"52-Week High: ${df['high'].max():.2f}")
print(f"52-Week Low: ${df['low'].min():.2f}")
print(f"YTD Return: {((df['close'].iloc[-1] / df['close'].iloc[0]) - 1) * 100:.2f}%")
if analysis_type in ["fundamental", "all"]:
print(f"\n📊 Fundamental Analysis for {ticker}")
# Company profile
try:
profile = obb.equity.profile(symbol=ticker)
print(f"\nCompany: {profile.name}")
print(f"Sector: {profile.sector}")
print(f"Industry: {profile.industry}")
print(f"Market Cap: ${profile.market_cap / 1e9:.2f}B")
except:
print("Profile data not available")
# Financial metrics
try:
metrics = obb.equity.fundamental.metrics(symbol=ticker)
print(f"\nKey Metrics:")
print(f"P/E Ratio: {metrics.pe_ratio:.2f}")
print(f"EPS: ${metrics.eps:.2f}")
print(f"Dividend Yield: {metrics.dividend_yield:.2%}")
print(f"ROE: {metrics.roe:.2%}")
except:
print("Metrics data not available")
# Analyst ratings
:
ratings = obb.equity.estimates.analyst(symbol=ticker)
()
()
()
()
()
:
()
if analysis_type in ["technical", "all"]:
print(f"\n📉 Technical Analysis for {ticker}")
# Calculate technical indicators
import pandas as pd
# Simple Moving Averages
df['SMA_20'] = df['close'].rolling(window=20).mean()
df['SMA_50'] = df['close'].rolling(window=50).mean()
df['SMA_200'] = df['close'].rolling(window=200).mean()
current_price = df['close'].iloc[-1]
sma_20 = df['SMA_20'].iloc[-1]
sma_50 = df['SMA_50'].iloc[-1]
sma_200 = df['SMA_200'].iloc[-1]
print(f"\nMoving Averages:")
print(f"Current Price: ${current_price:.2f}")
print(f"SMA 20: ${sma_20:.2f} {'🟢' if current_price > sma_20 else '🔴'}")
print(f"SMA 50: ${sma_50:.2f} {'🟢' if current_price > sma_50 else '🔴'}")
()
delta = df[].diff()
gain = (delta.where(delta > , )).rolling(window=).mean()
loss = (-delta.where(delta < , )).rolling(window=).mean()
rs = gain / loss
df[] = - ( / ( + rs))
rsi = df[].iloc[-]
()
rsi > :
()
rsi < :
()
:
()
avg_volume = df[].rolling(window=).mean().iloc[-]
current_volume = df[].iloc[-]
()
()
()
()
Generate investment insights using Claude's analysis:
# Prepare summary for AI analysis
summary = {
"ticker": ticker,
"current_price": current_price,
"52w_high": df['high'].max(),
"52w_low": df['low'].min(),
"ytd_return": ((df['close'].iloc[-1] / df['close'].iloc[0]) - 1) * 100,
"technical": {
"sma_position": "bullish" if current_price > sma_200 else "bearish",
"rsi": rsi,
"volume_trend": "high" if current_volume > avg_volume else "normal"
}
}
print(f"\n🤖 AI Analysis for {ticker}:")
print("\nBased on the data above, here's my assessment:")
print(f"- Trend: {'Bullish' if current_price > sma_200 else 'Bearish'} (price {'above' if current_price > sma_200 else 'below'} 200-day SMA)")
print(f"- Momentum: {'Overbought' if rsi > 70 else rsi < } (RSI: )")
()
()
Create a formatted analysis report:
print(f"\n{'='*60}")
print(f"EQUITY ANALYSIS REPORT: {ticker}")
print(f"{'='*60}")
print(f"Generated: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"Data Source: OpenBB Platform")
print(f"\nAnalysis Type: {analysis_type.upper()}")
print(f"Period Analyzed: {period}")
print(f"\n{'='*60}")
/openbb-equity AAPL
/openbb-equity TSLA --analysis=fundamental
/openbb-equity NVDA --analysis=technical --period=6m
/openbb-equity GOOGL --analysis=all --period=1y
# Compare with crypto
/openbb-crypto BTC --compare=equity
# Portfolio context
/openbb-portfolio --add=AAPL
# Macro correlation
/openbb-macro --impact=equity
pip install openbb)obb.user.credentials)