CodeRL leverages deep reinforcement learning and pretrained models to enhance code generation capabilities. This innovative approach allows developers to automate and optimize programming tasks effectively.
claude install salesforce/CodeRLhttps://github.com/salesforce/CodeRL
Automate the generation of repetitive code snippets to save time during development.
Create boilerplate code for new projects, allowing developers to focus on core functionalities.
Enhance code quality by providing real-time suggestions based on best practices and learned patterns.
Optimize algorithm performance through reinforcement learning techniques that adapt to user feedback.
claude install salesforce/CodeRLgit clone https://github.com/salesforce/CodeRLCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate a Python function to process [DATA] using CodeRL. The function should include error handling and logging. Optimize the code for performance and readability. Use the following context: [CONTEXT].
# Python Function to Process Data Using CodeRL
```python
import logging
from typing import List, Dict
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def process_data(data: List[Dict]) -> List[Dict]:
"""
Process a list of dictionaries containing data using CodeRL.
Args:
data: A list of dictionaries containing the data to process.
Returns:
A list of dictionaries with processed data.
"""
try:
processed_data = []
for item in data:
# Simulate processing with CodeRL
processed_item = {
'id': item['id'],
'processed_value': item['value'] * 2,
'status': 'processed'
}
processed_data.append(processed_item)
logger.info(f"Processed item with id: {item['id']}")
return processed_data
except Exception as e:
logger.error(f"Error processing data: {e}")
raise
# Example usage
if __name__ == "__main__":
sample_data = [
{'id': 1, 'value': 10},
{'id': 2, 'value': 20},
{'id': 3, 'value': 30}
]
result = process_data(sample_data)
print(result)
```
## Output
```
[
{'id': 1, 'processed_value': 20, 'status': 'processed'},
{'id': 2, 'processed_value': 40, 'status': 'processed'},
{'id': 3, 'processed_value': 60, 'status': 'processed'}
]
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