CodeGen is a comprehensive toolkit from Facebook AI Research for leveraging machine learning in code generation projects. It includes everything from dataset creation to model training and evaluation, along with pretrained models to accelerate development.
claude install facebookresearch/CodeGenhttps://github.com/facebookresearch/CodeGen
Automate the generation of boilerplate code for new software projects to save time.
Create and preprocess datasets for training custom machine learning models on code.
Evaluate the performance of machine learning models in various coding tasks, such as translation and deobfuscation.
Integrate AI-generated code snippets into existing applications to enhance functionality.
claude install facebookresearch/CodeGengit clone https://github.com/facebookresearch/CodeGenCopy 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] for [COMPANY] in the [INDUSTRY] sector. The function should include error handling, input validation, and return a structured output. Use CodeGen to suggest the most efficient implementation.
# Python Function for Data Processing
```python
import pandas as pd
from typing import Dict, List, Optional
def process_data(input_file: str, output_format: str = 'json') -> Optional[Dict[str, List]]:
"""
Processes data from a CSV file and returns it in the specified format.
Args:
input_file (str): Path to the input CSV file.
output_format (str): Desired output format ('json' or 'csv'). Default is 'json'.
Returns:
Optional[Dict[str, List]]: Processed data in the specified format or None if an error occurs.
"""
try:
# Read the CSV file
df = pd.read_csv(input_file)
# Validate input data
if df.empty:
raise ValueError("Input file is empty")
# Process data
processed_data = {
'columns': df.columns.tolist(),
'data': df.values.tolist()
}
# Return data in the specified format
if output_format.lower() == 'json':
return processed_data
elif output_format.lower() == 'csv':
return pd.DataFrame(processed_data['data'], columns=processed_data['columns']).to_csv(index=False)
else:
raise ValueError("Unsupported output format")
except Exception as e:
print(f"Error processing data: {e}")
return None
# Example usage
if __name__ == "__main__":
result = process_data('input_data.csv', 'json')
print(result)
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