from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field

class WeatherInput(BaseModel):
    """Input schema for weather skill."""
    location: str = Field(..., description="City name")
    unit: str = Field("celsius", description="Temperature unit")

class WeatherSkill(BaseTool):
    name = "get_weather"
    description = "Get current weather for a location"
    args_schema = WeatherInput
    
    def _run(self, location: str, unit: str = "celsius") -> str:
        """Get weather data."""
        return f"Weather in {location}: 20°{unit[0].upper()}"

from .weather import WeatherSkill

def get_crestal_skill(name: str) -> BaseTool:
    skills = {
        "weather": WeatherSkill()
    }
    return skills.get(name)

{
  "cdp": {
    "monitor_prices": {
      "pairs": ["BTC-USD", "ETH-USD"],
      "interval": 300
    }
  },
  "social": {
    "twitter": {
      "auto_reply": true,
      "sentiment_analysis": true
    }
  }
}

class PriceMonitorInput(BaseModel):
    pair: str = Field(..., description="Trading pair (e.g., BTC-USD)")
    threshold: float = Field(5.0, description="Alert threshold percentage")

class PriceMonitorSkill(BaseTool):
    name = "monitor_prices"
    description = "Monitor crypto price movements"
    args_schema = PriceMonitorInput
    
    def _run(self, pair: str, threshold: float = 5.0) -> str:
        # Implementation
        return f"Monitoring {pair} for {threshold}% changes"

    async def _arun(self, pair: str, threshold: float = 5.0) -> str:
        # Async implementation
        pass

class TwitterSkill(BaseTool):
    name = "twitter_post"
    description = "Post updates to Twitter"
    
    def _run(self, text: str) -> str:
        # Post to Twitter
        return f"Posted: {text}"

    def validate_length(self, text: str) -> bool:
        return len(text) <= 280

class TransactionInput(BaseModel):
    amount: float = Field(..., gt=0, description="Transaction amount")
    pair: str = Field(..., pattern="^[A-Z]+-[A-Z]+$")

try:
    response = await self.api_call()
except RateLimitError:
    return "Rate limit exceeded. Try again in 60 seconds."
except ApiError as e:
    return f"API Error: {str(e)}"

async def _arun(self, **kwargs):
    async with aiohttp.ClientSession() as session:
        result = await self.process(session, **kwargs)
    return result

def test_skill():
    skill = CustomSkill()
    result = skill.run(input="test")
    assert result.status == "success"

# skill_sets/trading.py
from typing import List
from pydantic import BaseModel, Field
from langchain_core.tools import BaseTool

class Order(BaseModel):
    pair: str
    side: str
    amount: float
    type: str = "market"

class TradingSkill(BaseTool):
    name = "execute_trade"
    description = "Execute crypto trades"
    
    def __init__(self, api_key: str, pairs: List[str]):
        self.api_key = api_key
        self.allowed_pairs = pairs
        super().__init__()
    
    def _run(self, order: Order) -> str:
        if order.pair not in self.allowed_pairs:
            return f"Unsupported pair: {order.pair}"
        # Execute trade
        return f"Executed {order.side} {order.amount} {order.pair}"

{
  "skill_sets": {
    "trading": {
      "pairs": ["BTC-USD", "ETH-USD"],
      "api_key": "your-api-key"
    }
  }
}