MCP Skills

MCP Skills are pre-built prompt templates that teach AI assistants specific workflows using the Pyth MCP server. Instead of figuring out which tools to call and how to combine them, you can describe what you want in natural language and the skill handles the rest.

Skills work with Claude Code and are installed alongside the MCP server. Each skill listed below includes example prompts you can try directly in your AI assistant.

Installation

Skills are Claude Code slash commands that live in your project's .claude/skills/ directory. Each skill is a folder containing a SKILL.md file.

Prerequisites

• Claude Code installed
• Pyth MCP server configured (at minimum, claude mcp add pyth --transport http https://mcp.pyth.network/mcp)

Setup

git clone https://github.com/pyth-network/pyth-crosschain.git

cp -r pyth-crosschain/apps/mcp/skills/ your-project/.claude/skills/

your-project/
└── .claude/
    └── skills/
        ├── pyth-alert-conditions/
        │   └── SKILL.md
        ├── pyth-cross-asset-comparison/
        │   └── SKILL.md
        ├── pyth-fx-converter/
        │   └── SKILL.md
        └── ... (9 skills total)

/pyth-alert-conditions Is BTC above $100k?
/pyth-fx-converter Convert 1000 EUR to JPY
/pyth-portfolio-tracker I hold 2 BTC and 50 ETH

Available Skills

Market Data

Price Alerts

Evaluates one-time price conditions — checks if prices are above or below thresholds, if assets have moved by a percentage, or if prices are near period highs or lows. These are point-in-time checks, not persistent alerts.

How it works: Decomposes your question into the data needed, fetches it, evaluates the condition, and answers YES or NO with supporting numbers.

What you can ask:

• "Is BTC above $100k?"
• "Has ETH dropped 5% today?"
• "Is gold near its weekly high?"
• "Has SOL changed more than 10% since June 1?"
• "Is the BTC bid-ask spread above $10?"

Try it:

"Is gold near its weekly high?"

The assistant fetches this week's daily candles and the current price, computes the weekly high from the candle data, and tells you how close the current price is as a percentage.

Cross-Asset Comparison

Compares price performance across multiple assets by normalizing OHLC data to a common baseline. Converts absolute prices to relative performance so you can compare assets at different price scales (e.g., BTC at $97k vs Gold at$2k).

How it works: Fetches candlestick data for each asset over the same time range, divides each close by the first close (1.0 = starting point), and computes period returns.

What you can ask:

• "Compare Bitcoin vs Gold this quarter"
• "ETH vs SOL performance over the last 30 days"
• "EUR/USD vs GBP/USD over the past week"
• "Which performed better this month: BTC, ETH, or SOL?"

Try it:

"Compare Bitcoin vs Gold this quarter"

The assistant fetches daily candles for both assets, normalizes each series, and presents a side-by-side table showing that, for example, BTC returned +12.4% while Gold returned +4.2%.

Volatility Analysis

Analyzes price volatility using candlestick data. Computes annualized volatility from close-to-close returns, average true range (ATR), and daily range metrics.

How it works: Fetches candlestick data, calculates daily returns, then computes standard deviation and annualizes it (using 365 days for crypto, 252 for equities/FX).

What you can ask:

• "How volatile is SOL?"
• "Compare the volatility of BTC, ETH, and AAPL"
• "What's the average daily range of gold?"
• "Is ETH more volatile than BTC?"

Try it:

"Compare the volatility of BTC, ETH, and AAPL"

The assistant fetches 30 days of daily candles for each, computes annualized volatility and ATR, and presents a comparison table showing relative risk levels.

Financial Workflows

FX Converter

Converts currencies using Pyth FX and crypto price feeds. Supports fiat-to-fiat, crypto-to-fiat, and historical rate conversions.

How it works: All conversions go through USD. For cross rates (e.g., EUR to JPY), the assistant fetches both USD-based rates and computes the cross rate by dividing them.

What you can ask:

• "Convert 1000 EUR to JPY"
• "How much is 5 BTC in EUR?"
• "What was the GBP/JPY rate last Friday?"
• "Convert 500 USD to GBP"

Try it:

"Convert 1000 EUR to JPY"

The assistant fetches EUR/USD and JPY/USD rates, computes the cross rate (e.g., 1.08 / 0.0067 = 161.19), and returns: 1000 EUR = 161,194 JPY.

Portfolio Tracker

Tracks a portfolio of assets with current prices, allocation percentages, and P&L calculations. Supports cost-basis and date-based P&L.

How it works: Discovers feeds, batches all assets into a single price call (up to 100 feeds), then computes value, allocation, and optionally compares against historical reference prices.

What you can ask:

• "What's my portfolio worth? I hold 2 BTC, 50 ETH, and 1000 SOL"
• "Show gold, silver, and platinum prices"
• "How has my portfolio performed since last month?"
• "What's the allocation breakdown of my crypto holdings?"

Try it:

"What's my portfolio worth? I hold 2 BTC, 50 ETH, and 1000 SOL"

The assistant fetches all three prices in one call and returns a table with each asset's value and your total portfolio value with allocation percentages.

Funding Rate Monitor

Monitors perpetual futures funding rates to gauge market sentiment. Positive rates indicate long-biased (bullish) markets, negative rates indicate short-biased (bearish) markets.

How it works: Discovers funding rate feeds using the funding-rate asset type, fetches current rates, and can analyze historical rate trends via candlestick data.

What you can ask:

• "What are the current BTC and ETH funding rates?"
• "Show me all funding rates sorted by magnitude"
• "How has the BTC funding rate changed this week?"
• "Which assets have the highest funding rates right now?"

Try it:

"What are the current BTC and ETH funding rates?"

The assistant fetches both rates and presents them with sentiment labels — for example, BTC at +0.012% (slightly long-biased) and ETH at +0.035% (moderately long-biased).

Data & Integration

Data Export

Exports Pyth price data in CSV, JSON, or Markdown table format. Handles OHLC exports, feed catalog dumps, historical snapshots, and current price tables.

How it works: Fetches all requested data first (with pagination if needed), then formats the complete dataset. Caps exports at 1000 rows.

What you can ask:

• "Export BTC daily OHLC for the last 30 days as CSV"
• "Give me all crypto feeds as JSON"
• "Show current prices for BTC, ETH, and gold as a Markdown table"
• "Export the full feed catalog"

Try it:

"Export BTC daily OHLC for the last 30 days as CSV"

The assistant fetches daily candlestick data and formats it with headers: timestamp,open,high,low,close,volume followed by one row per day.

Time-Series Snapshots

Fetches price snapshots at specific historical timestamps. Generates timestamp series (monthly, weekly, quarterly) and retrieves prices for each date.

How it works: Generates Unix timestamps for your requested dates, then calls the historical price API once per timestamp with up to 50 feeds per call. For continuous data (more than ~20 points), suggests candlestick data instead.

What you can ask:

• "What was BTC at the start of each month this year?"
• "Show me ETH and SOL prices every Monday for the last 4 weeks"
• "Get quarterly gold prices since April 2025"

Try it:

"What was BTC at the start of each month this year?"

The assistant generates timestamps for April 1, May 1, June 1, etc. (data starts April 2025), fetches each snapshot, and presents a table of monthly prices.

Integration Helper

Guides developers on integrating Pyth price feeds. Explains feed discovery, symbol formats, ID types, price exponents, and data channels.

How it works: Understands your integration context (language, chain, use case), uses feed discovery to find specific feeds, and explains the symbol/ID/exponent/channel system.

What you can ask:

• "How do I use Pyth in my trading bot?"
• "What's the feed ID for AAPL?"
• "What asset types does Pyth support?"
• "How does Pyth Pro work?"
• "How do pricing channels work?"

Try it:

"What's the feed ID for AAPL?"

The assistant searches for AAPL and returns the symbol (Equity.US.AAPL), the Pyth Pro numeric feed ID, and the exponent for price conversion.