How does this skill reduce token usage?

The skill proceduralizes data fetching and calculation within the MCP environment, returning only the final processed results to Claude rather than raw, verbose data logs.

Which exchanges are supported for market data?

It supports over 24 major cryptocurrency exchanges via the CCXT-MCP integration, including Binance, Coinbase, and Kraken.

What technical indicators can be calculated?

The skill can calculate standard indicators such as RSI, MACD, Bollinger Bands, and more through its integration with the crypto-indicators-mcp dependency.

What are the requirements to run this skill?

It requires the Crypto-MCP-Suite servers to be running and accessible, along with standard Python libraries like pandas and numpy.

Can I use this for real-time sentiment analysis?

Yes, it aggregates data from Reddit, Telegram, Discord, and CryptoPanic to provide a unified sentiment score for specific symbols.

Crypto Data Extraction

Name: Crypto Data Extraction
Author: justmy2satoshis

byjustmy2satoshis

Data Science & ML

Extracts and normalizes multi-exchange market data, technical indicators, and social sentiment for comprehensive crypto analysis.

About

This skill provides a high-performance workflow for gathering cryptocurrency market intelligence by orchestrating multiple MCP servers. It automates the retrieval of OHLCV data from over 24 exchanges, calculates complex technical indicators like RSI and MACD, and aggregates real-time sentiment from platforms like Reddit, Telegram, and Discord. Designed to optimize token usage by up to 90%, it serves as a robust foundation for building automated trading agents, market research tools, and sentiment-aware applications that require standardized, high-confidence data formats.

Key Features

Real-time social sentiment aggregation from Reddit, Telegram, and Discord
Standardized JSON output optimized for database storage and agent consumption
Extreme token efficiency with up to 90% reduction compared to raw data handling
0 GitHub stars
Multi-exchange OHLCV data fetching across 24+ platforms
Automated technical indicator calculation including RSI, MACD, and Bollinger Bands

Use Cases

Generating comprehensive market sentiment and volatility reports for specific assets
Optimizing LLM token consumption during large-scale market data processing tasks
Developing automated crypto trading bots with pre-calculated technical analysis

About

Key Features

Real-time social sentiment aggregation from Reddit, Telegram, and Discord
Standardized JSON output optimized for database storage and agent consumption
Extreme token efficiency with up to 90% reduction compared to raw data handling
0 GitHub stars
Multi-exchange OHLCV data fetching across 24+ platforms
Automated technical indicator calculation including RSI, MACD, and Bollinger Bands

Use Cases

Generating comprehensive market sentiment and volatility reports for specific assets
Optimizing LLM token consumption during large-scale market data processing tasks
Developing automated crypto trading bots with pre-calculated technical analysis