87 lines
No EOL
2.5 KiB
Markdown
87 lines
No EOL
2.5 KiB
Markdown
# Cursor Platform Implementation
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This directory contains the system prompts and implementation details for the Cursor platform.
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## Overview
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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.
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## System Prompts
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### Core System Prompt
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```
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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.
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```
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### Specialized Prompts
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- Code Completion Expert
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- Code Generation Assistant
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- Debugging Specialist
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- Code Explanation Guide
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- Documentation Generator
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## Implementation Details
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### Architecture
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- Code understanding techniques
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- Context management
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- Response generation
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- Error handling
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- Editor integration
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### Features
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- Intelligent code completion
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- Code generation
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- Code explanation
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- Debugging assistance
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- Documentation generation
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## Usage Examples
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```python
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# Example: Cursor API Integration
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import requests
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API_KEY = "your_api_key"
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ENDPOINT = "https://api.cursor.sh/v1/chat"
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json"
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}
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data = {
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"messages": [
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{"role": "system", "content": "You are Cursor, a helpful coding assistant."},
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{"role": "user", "content": "Can you explain how this React component works?"}
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],
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"temperature": 0.3,
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"max_tokens": 1000,
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"code_context": {
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"file_path": "src/components/Button.jsx",
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"code_snippet": "const Button = ({ onClick, children }) => {\n return (\n <button onClick={onClick} className=\"btn\">\n {children}\n </button>\n );\n};"
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}
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}
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response = requests.post(ENDPOINT, headers=headers, json=data)
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result = response.json()
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print(result["choices"][0]["message"]["content"])
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```
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## Best Practices
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1. Use appropriate system prompts for different tasks
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2. Implement proper error handling
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3. Manage context effectively
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4. Optimize token usage
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5. Cache responses when appropriate
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6. Provide relevant code context
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7. Maintain editor integration
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## Contributing
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Please follow these guidelines:
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1. Document any new system prompts
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2. Include usage examples
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3. Add performance benchmarks
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4. Document API changes |