skills/academy-skills/alpha-vantage/SKILL.md
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
npx skillsauth add lunartech-x/superpowers alpha-vantageInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
export ALPHAVANTAGE_API_KEY="your_key_here"
uv pip install requests pandas
All requests go to:
https://www.alphavantage.co/query?function=FUNCTION_NAME&apikey=YOUR_KEY&...params
import requests
import os
API_KEY = os.environ.get("ALPHAVANTAGE_API_KEY")
BASE_URL = "https://www.alphavantage.co/query"
def av_get(function, **params):
response = requests.get(BASE_URL, params={"function": function, "apikey": API_KEY, **params})
return response.json()
# Stock quote (latest price)
quote = av_get("GLOBAL_QUOTE", symbol="AAPL")
price = quote["Global Quote"]["05. price"]
# Daily OHLCV
daily = av_get("TIME_SERIES_DAILY", symbol="AAPL", outputsize="compact")
ts = daily["Time Series (Daily)"]
# Company fundamentals
overview = av_get("OVERVIEW", symbol="AAPL")
print(overview["MarketCapitalization"], overview["PERatio"])
# Income statement
income = av_get("INCOME_STATEMENT", symbol="AAPL")
annual = income["annualReports"][0] # Most recent annual
# Crypto price
crypto = av_get("DIGITAL_CURRENCY_DAILY", symbol="BTC", market="USD")
# Economic indicator
gdp = av_get("REAL_GDP", interval="annual")
# Technical indicator
rsi = av_get("RSI", symbol="AAPL", interval="daily", time_period=14, series_type="close")
| Category | Key Functions | |----------|--------------| | Time Series (Stocks) | GLOBAL_QUOTE, TIME_SERIES_INTRADAY, TIME_SERIES_DAILY, TIME_SERIES_WEEKLY, TIME_SERIES_MONTHLY | | Options | REALTIME_OPTIONS, HISTORICAL_OPTIONS | | Alpha Intelligence | NEWS_SENTIMENT, EARNINGS_CALL_TRANSCRIPT, TOP_GAINERS_LOSERS, INSIDER_TRANSACTIONS, ANALYTICS_FIXED_WINDOW | | Fundamentals | OVERVIEW, ETF_PROFILE, INCOME_STATEMENT, BALANCE_SHEET, CASH_FLOW, EARNINGS, DIVIDENDS, SPLITS | | Forex (FX) | CURRENCY_EXCHANGE_RATE, FX_INTRADAY, FX_DAILY, FX_WEEKLY, FX_MONTHLY | | Crypto | CURRENCY_EXCHANGE_RATE, CRYPTO_INTRADAY, DIGITAL_CURRENCY_DAILY | | Commodities | GOLD (WTI spot), BRENT, NATURAL_GAS, COPPER, WHEAT, CORN, COFFEE, ALL_COMMODITIES | | Economic Indicators | REAL_GDP, TREASURY_YIELD, FEDERAL_FUNDS_RATE, CPI, INFLATION, UNEMPLOYMENT, NONFARM_PAYROLL | | Technical Indicators | SMA, EMA, MACD, RSI, BBANDS, STOCH, ADX, ATR, OBV, VWAP, and 40+ more |
| Parameter | Values | Notes |
|-----------|--------|-------|
| outputsize | compact / full | compact = last 100 points; full = 20+ years |
| datatype | json / csv | Default: json |
| interval | 1min, 5min, 15min, 30min, 60min, daily, weekly, monthly | Depends on endpoint |
| adjusted | true / false | Adjust for splits/dividends |
import time
# Add delay to avoid rate limits
time.sleep(0.5) # 0.5s between requests on free tier
data = av_get("GLOBAL_QUOTE", symbol="AAPL")
# Check for API errors
if "Error Message" in data:
raise ValueError(f"API Error: {data['Error Message']}")
if "Note" in data:
print(f"Rate limit warning: {data['Note']}")
if "Information" in data:
print(f"API info: {data['Information']}")
Load these for detailed endpoint documentation:
tools
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
testing
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
development
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
tools
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.