Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
npx skills add https://github.com/staskh/trading_skills --skill technical-analysis
Compute technical indicators using pandas-ta. Supports multi-symbol analysis and earnings data.
> Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.
uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]
SYMBOL - Ticker symbol or comma-separated list (e.g., AAPL or AAPL,MSFT,GOOGL)--period - Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)--indicators - Comma-separated list: rsi,macd,bb,sma,ema,atr,adx (default: all)--earnings - Include earnings data (upcoming date + history)Single symbol returns:
price - Current price and recent changeindicators - Computed values for each indicatorrisk_metrics - Volatility (annualized %) and Sharpe ratiosignals - Buy/sell signals based on indicator levelsearnings - Upcoming date and EPS history (if --earnings)Multiple symbols returns:
results - Array of individual symbol resultsindicators.macd.crossover - Most recent MACD line/signal crossover, or null:direction - "up" (MACD crossed above signal = bullish) or "down" (crossed below = bearish)days_ago - Trading bars since the crossover (0 = happened on the most recent bar)indicators.ema.crossover - Most recent EMA9/EMA21 crossover (same shape; null if none).indicators.ema also reports ema9 and ema21 alongside ema12/ema26.
crossover.days_ago of 0-5 = fresh signal# Single symbol with all indicators
uv run python scripts/technicals.py AAPL
# Multiple symbols
uv run python scripts/technicals.py AAPL,MSFT,GOOGL
# With earnings data
uv run python scripts/technicals.py NVDA --earnings
# Specific indicators only
uv run python scripts/technicals.py TSLA --indicators rsi,macd
Compute price correlation matrix between multiple symbols for diversification analysis.
uv run python scripts/correlation.py SYMBOLS [--period PERIOD]
SYMBOLS - Comma-separated ticker symbols (minimum 2)--period - Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)symbols - List of symbols analyzedperiod - Time period usedcorrelation_matrix - Nested dict with correlation values between all pairs# Portfolio correlation
uv run python scripts/correlation.py AAPL,MSFT,GOOGL,AMZN
# Sector comparison
uv run python scripts/correlation.py XLF,XLK,XLE,XLV --period 6mo
# Check hedge effectiveness
uv run python scripts/correlation.py SPY,GLD,TLT
numpypandaspandas-tayfinanceAll timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.
Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
> Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.
Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
Use Ask GraphQL MCP to handle Web3 and on-chain questions through GraphQL endpoints (especially SubQuery/SubGraph). Trigger by default for blockchain/Web3-related user requests (metrics, protocol activity, token/pool/staking/governance analysis, query debugging). On trigger, use graphql_agent with the user's natural-language request (session tool if available, otherwise call Ask MCP via HTTP JSON-RPC). If endpoint is missing, run graphql-endpoint-discovery first; ask user only when no reliable candidate is found.
Cryptofeed - Real-time cryptocurrency market data feeds from 40+ exchanges. WebSocket streaming, normalized data, order books, trades, tickers. Python library for algorithmic trading and market data analysis.
Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", "model review".
Generate professional client-facing performance reports with portfolio returns, allocation breakdowns, and market commentary. Suitable for quarterly or annual distribution. Triggers on "client report", "performance report", "quarterly report for [client]", "generate reports", or "client statement".
Take staskh/technical-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.