Defining Agent Capabilities

Skylar lets you specify what tasks your AI agent will perform. The platform provides an easy-to-use interface for defining specific capabilities.

Custom Task Examples

Skylar allows you to define and customize your AI agent's capabilities to suit a wide variety of use cases. Some examples of tasks your AI agent can perform include:

Trading Bot: Build an AI agent that monitors cryptocurrency or stock markets, analyzes trends, and makes trading recommendations or even executes trades based on predefined strategies.
Coin Analysis Bot: Create an AI agent that performs in-depth analysis of cryptocurrencies, stocks, or other assets, providing real-time market insights, price forecasts, and technical analysis.
Conversational Bot: Design a chatbot that engages users in natural conversations, answering queries, offering advice, and handling interactions in a way that matches the agent’s personalized personality (e.g., friendly, professional, or playful).

These tasks can be fine-tuned to fit your specific goals, whether you're automating trading strategies, providing market insights, or enhancing customer interaction with a conversational AI.

def deploy_agent_to_pump_fun(agent_id, user_input=None):
    try:
        # Fetch agent data from Skylar
        agent_data = get_skylar_agent(agent_id)
        
        # Optionally, handle user input (for token analysis, trading, or conversation)
        if user_input:
            if "analyze" in user_input and "token" in user_input:
                token_symbol = user_input.split()[-1].upper()
                result = analyze_token(token_symbol)
                print(f"Token Analysis for {token_symbol}: {json.dumps(result)}")
            
            elif "trade" in user_input:
                parts = user_input.split()
                action = parts[0].lower()  # 'buy' or 'sell'
                amount = float(parts[1])
                token_symbol = parts[2].upper()
                result = execute_trade(action, amount, token_symbol)
                print(f"Trade Execution: {json.dumps(result)}")
            
            else:
                result = get_conversational_response(user_input)
                print(f"AI Response: {json.dumps(result)}")
        
        # Deploy agent data to pump.fun
        deployment_info = deploy_to_pump_fun(agent_data)
        print(f"Agent deployed to pump.fun: {deployment_info}")
    
    except Exception as e:
        print(f"Deployment failed: {e}")

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Last updated 2 months ago

def deploy_agent_to_pump_fun(agent_id, user_input=None): try: # Fetch agent data from Skylar agent_data = get_skylar_agent(agent_id) # Optionally, handle user input (for token analysis, trading, or conversation) if user_input: if "analyze" in user_input and "token" in user_input: token_symbol = user_input.split()[-1].upper() result = analyze_token(token_symbol) print(f"Token Analysis for {token_symbol}: {json.dumps(result)}") elif "trade" in user_input: parts = user_input.split() action = parts[0].lower() # 'buy' or 'sell' amount = float(parts[1]) token_symbol = parts[2].upper() result = execute_trade(action, amount, token_symbol) print(f"Trade Execution: {json.dumps(result)}") else: result = get_conversational_response(user_input) print(f"AI Response: {json.dumps(result)}") # Deploy agent data to pump.fun deployment_info = deploy_to_pump_fun(agent_data) print(f"Agent deployed to pump.fun: {deployment_info}") except Exception as e: print(f"Deployment failed: {e}")