.cursorrules

# Tool-Use Repository Development Guidelines

## Creating a New Script

To add a new script to the tool-use repository, you need to modify several files:

1. Create a new script file in `src/tool_use/scripts/`: 

Example: src/tool_use/scripts/my_script.py
from ..utils.ai_service import AIService
from ..config_manager import config_manager
def main(args):
    config_values = config_manager.get_tool_config("script_name")
    # Your script implementation
    pass
if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        console.print("\n[yellow]Operation cancelled by user.[/yellow]")
        sys.exit(0)
    except Exception as e:
        console.print(f"\n[red]An unexpected error occurred: {e}[/red]")
        sys.exit(1)

2. Add dependencies to `src/tool_use/scripts/_script_dependencies.py`:
    
SCRIPT_DEPENDENCIES = {
# ... existing dependencies ...
"my-script": ["required-package1", "required-package2"]
}

3. Add configuration in `src/tool_use/utils/config_wizard.py`:
    SCRIPT_INFO = {
# ... existing scripts ...
"my-script": {
    "name": "My Script",
    "description": "What your script does",
    "config_keys": [
            {
            "key": "my_config_key",
            "prompt": "Enter configuration value:",
            "description": "What this config value does",
            "required": True
            }
        ]
    }
}
    
4. Update `src/tool_use/cli.py`:
- Add to `all_scripts` dictionary
- Add to `script_modules` mapping

5. Add script to `README.md`

## Using AIService

The AIService utility (`src/tool_use/utils/ai_service.py`) provides a standardized way to interact with AI models. Example usage from prioritize.py:

from ..utils.ai_service import AIService
# Initialize the service
ai = AIService()
# Simple completion
response = ai.query("Your prompt here")


#Structured output

class Example(BaseModel):
    example: str = Field(description="example")
    
class StructuredOutput(BaseModel):
    """Example description for the structured output, keys can be any pydantic supported field"""
    field1: str = Field(description="First field for the output")
    field2: List[Example] = Field(description="List of examples")
    
# Structured outputs only supported by openai
ai_service = AIService(service_type="openai")
response = ai_service.query_structured(prompt, StructuredOutput, system_prompt)

field1, field2 = response

## Script Best Practices

1. Use config_manager for settings:
    
from ..config_manager import config_manager
config_values = config_manager.get_tool_config("script_name")

2. Handle arguments properly:
def main(args):
    print("Usage: tool-use my-script [args]")
    return

3. Provide clear error messages and user feedback

4. Follow existing patterns for consistency:
- Use relative imports
- Handle dependencies through SCRIPT_DEPENDENCIES
- Provide configuration through config_wizard
- Document usage in script's docstring


# Error Handling
Make sure to handle errors gracefully and provide clear error messages to the user. 


# Formatting
- Use Rich when possible to display print statements in a visually appealing way

## Common Patterns

1. AI Integration:
   - Use AIService for AI interactions
   - Structure prompts clearly
   - Handle API errors gracefully

2. Configuration:
   - Use config_wizard for user settings
   - Validate config values
   - Provide clear configuration prompts

3. CLI Integration:
   - Follow existing argument patterns
   - Provide help text
   - Handle invalid inputs gracefully