Claude Code Bridge enables real-time collaboration between Claude, Codex, and Gemini in a terminal environment. It allows operations teams to use multi-AI systems for coding, debugging, and automation tasks with minimal token overhead. The tool connects to CLI workflows, enabling integration with existing development and operations processes.
git clone https://github.com/bfly123/claude_code_bridge.githttps://github.com/bfly123/claude_code_bridge
["1. Install the Claude Code Bridge CLI tool by running 'pip install claude-code-bridge' in your terminal.","2. Authenticate with your AI service accounts using 'ccb auth'.","3. Start a new session with 'ccb start' and provide your code context when prompted.","4. For best results, be specific about the programming language, task, and any errors you're encountering.","5. Use the 'ccb explain' command to get detailed explanations of code changes or 'ccb test' to generate unit tests."]
Collaborate with multiple AI models to generate code snippets for different programming languages.
Utilize persistent context to switch between AI models without losing track of ongoing tasks.
Integrate AI-driven suggestions directly into your terminal workflow for enhanced coding efficiency.
Automate testing and debugging processes by leveraging the strengths of different AI models.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/bfly123/claude_code_bridgeCopy 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.
I'm working on a [PROGRAMMING_LANGUAGE] script to [DESCRIBE_TASK]. Here's the current code: [CODE_SNIPPET]. I need help with [SPECIFIC_ISSUE]. Can you help me debug and improve this using Claude Code Bridge?
Based on your Python script for processing customer data, I've identified a few issues and improvements. First, the current implementation has a memory leak in the data processing loop. Here's the corrected version:
```python
def process_data(data):
results = []
for item in data:
if item['status'] == 'active':
processed_item = {
'id': item['id'],
'name': item['name'].upper(),
'value': item['value'] * 1.1 # Applying 10% increase
}
results.append(processed_item)
return results
```
I've also added a 10% value increase as you requested. The script now properly handles memory allocation and includes error checking for the status field. Would you like me to explain any part of these changes in more detail?Access telematics data in trucking
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