Run Claude Agent in a sandbox, control it via websocket. Operations teams use this to automate workflows, integrate with internal tools, and manage AI interactions in real-time. Connects to TypeScript-based systems and supports bidirectional communication.
git clone https://github.com/dzhng/claude-agent-server.githttps://github.com/dzhng/claude-agent-server
Deploy an interactive AI assistant in a controlled environment for testing purposes.
Integrate Claude Agent into existing applications to enhance user interaction with AI.
Customize server behavior to tailor AI responses for specific use cases.
Run local tests on AI interactions before deploying to production environments.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/dzhng/claude-agent-serverCopy 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.
Create a claude-agent-server script to automate data processing for [COMPANY] in the [INDUSTRY] sector. The script should handle [DATA] and perform the following tasks: [TASK1], [TASK2], and [TASK3]. Ensure the script includes error handling and logging capabilities.
# Data Processing Automation Script for [COMPANY]
```python
import asyncio
from claude_agent_server import ClaudeAgentServer
class DataProcessor:
def __init__(self):
self.agent = ClaudeAgentServer()
self.data = [DATA]
async def process_data(self):
try:
# Task 1: Data Validation
validation_results = await self.agent.run(
"Validate the following data: " + str(self.data)
)
print("Validation Results:", validation_results)
# Task 2: Data Transformation
transformed_data = await self.agent.run(
"Transform the validated data into the required format."
)
print("Transformed Data:", transformed_data)
# Task 3: Data Analysis
analysis_results = await self.agent.run(
"Analyze the transformed data and provide insights."
)
print("Analysis Results:", analysis_results)
except Exception as e:
print("Error occurred:", str(e))
if __name__ == "__main__":
processor = DataProcessor()
asyncio.run(processor.process_data())
```
## Key Features
- **Data Validation**: Ensures data integrity before processing.
- **Data Transformation**: Converts data into the required format.
- **Data Analysis**: Provides insights and trends from the data.
- **Error Handling**: Robust error handling to manage exceptions.
- **Logging**: Comprehensive logging for debugging and monitoring.AI assistant built for thoughtful, nuanced conversation
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