Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill alpha-vantagegit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/alpha-vantage)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/alpha-vantage"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/alpha-vantage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/alpha-vantage"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/alpha-vantage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.01431 |
| Opus 5.5 | $0.00050 | $0.00572 |
| Sonnet 5.5 | $0.00025 | $0.00286 |
| Haiku 4.5 | $0.00013 | $0.00143 |
Grade A, and why
alpha-vantage scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(BASE_URL, params={"function": function, "apikey": API_KEY, **params}) Copies of this mod
4 near-identical copies found in the catalogue:
- alterlab-alpha-vantage — 89% identical, 14 lines differ
- alpha-vantage — 86% identical, 3 lines differ
- alpha-vantage — 86% identical, 3 lines differ
- alpha-vantage — 86% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alpha Vantage — Financial Market Data
Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
API Key Setup (Required)
- Get a free key at https://www.alphavantage.co/support/#api-key (premium plans available for higher rate limits)
- Set as environment variable:
export ALPHAVANTAGE_API_KEY="your_key_here"
Installation
uv pip install requests pandas
Base URL & Request Pattern
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()
Quick Start Examples
# 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")
API Categories
| 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 |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 140 lines · 126 tokens per session scan A 7eef56a121c7
alpha-vantage is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,770 stars, last pushed yesterday), licensed Apache-2.0. It adds 126 tokens to every session and 1,431 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
stock-liquidity
Analyze how liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about…
company-valuation
Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks…
fintel-data
Query Fintel (fintel.io) institutional market data through its REST API or official MCP server. Use this skill whenever the user mentions Fintel or asks about short interest, short interest ratio or days to cover, short squeeze data, borrow rate or cost to borrow, shares available to borrow, daily short volume…
earnings-recap
Analyze a company's most recent (or a specified past) earnings report from Yahoo Finance data (yfinance): actual vs estimated EPS, surprise size, revenue and margin trends, and the stock's price reaction. Use this skill whenever the user asks how earnings went — beat or miss, earnings surprise, quarterly results, the…
estimate-analysis
Analyze sell-side analyst estimates and how they are changing, using Yahoo Finance data (yfinance): EPS and revenue consensus by period, estimate ranges and dispersion, revision trends over 7/30/60/90 days and up/down revision breadth, growth estimates vs industry, sector, and the S&P 500, and historical estimate…
twitter-reader
Read Twitter/X for financial research through opencli: the user's home timeline, tweet search, trending topics, bookmarks, a user's recent tweets, threads, articles, profiles, followers and following, and notifications. Use this skill whenever the user wants to know what people are saying on Twitter/X or fintwit about…