# Cursor Platform Implementation

This directory contains the system prompts and implementation details for the Cursor platform.

## Overview

Cursor is an AI-powered code editor that integrates advanced language models to help developers write, understand, and debug code more efficiently. It provides intelligent code completion, code generation, and code explanation capabilities.

## System Prompts

### Core System Prompt
```
You are Cursor, an advanced AI assistant integrated into a code editor to help with software development tasks. You can help with code generation, code explanation, debugging, and answering programming questions. You have been trained on a diverse dataset of programming languages and software engineering concepts.
```

### Specialized Prompts
- Code Completion Expert
- Code Generation Assistant
- Debugging Specialist
- Code Explanation Guide
- Documentation Generator

## Implementation Details

### Architecture
- Code understanding techniques
- Context management
- Response generation
- Error handling
- Editor integration

### Features
- Intelligent code completion
- Code generation
- Code explanation
- Debugging assistance
- Documentation generation

## Usage Examples

```python
# Example: Cursor API Integration
import requests

API_KEY = "your_api_key"
ENDPOINT = "https://api.cursor.sh/v1/chat"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

data = {
    "messages": [
        {"role": "system", "content": "You are Cursor, a helpful coding assistant."},
        {"role": "user", "content": "Can you explain how this React component works?"}
    ],
    "temperature": 0.3,
    "max_tokens": 1000,
    "code_context": {
        "file_path": "src/components/Button.jsx",
        "code_snippet": "const Button = ({ onClick, children }) => {\n  return (\n    <button onClick={onClick} className=\"btn\">\n      {children}\n    </button>\n  );\n};"
    }
}

response = requests.post(ENDPOINT, headers=headers, json=data)
result = response.json()
print(result["choices"][0]["message"]["content"])
```

## Best Practices

1. Use appropriate system prompts for different tasks
2. Implement proper error handling
3. Manage context effectively
4. Optimize token usage
5. Cache responses when appropriate
6. Provide relevant code context
7. Maintain editor integration

## Contributing

Please follow these guidelines:
1. Document any new system prompts
2. Include usage examples
3. Add performance benchmarks
4. Document API changes