Auto-Deployment System using Claude Code

A fully automated GitHub project deployment, testing, and documentation generation system that supports batch processing of multiple projects with automatic deployment documentation and RAG data upload.

📊 Performance Statistics

🎯 Deployment Success Rate: 92.3% (60/65 projects)

💰 Average Cost: $0.71 per deployment

⏱️ Average Time: 8.7 minutes per project

These statistics demonstrate the system's reliability, cost-effectiveness, and efficiency in handling diverse GitHub projects.

🚀 Features

• Automated Deployment: Uses Claude AI to automatically analyze projects and execute deployments
• Batch Processing: Supports deploying multiple GitHub projects simultaneously
• Smart Environment Detection: Automatically detects system environment and adapts deployment strategies
• Documentation Generation: Automatically generates deployment guides and issue resolution documentation
• RAG Integration: Automatically uploads deployment issues to RAG knowledge base
• Conda Support: Automatically creates and manages conda environments

📁 Project Structure

testCC/
├── auto_deploy_all.py          # Main deployment script (Python)
├── autodeploy.js              # Node.js deployment executor
├── rag.py                     # RAG data upload script
├── deploy.sh                  # Quick deployment launcher script
├── prompt.md                  # Deployment prompt template
├── projects/                  # Cloned project code directory
├── wechat/                    # Generated documentation and JSON files
│   └── {project_name}/        # Documentation directory for each project
│       ├── {project_name}_deployment.md
│       ├── {project_name}_deployment_guide.json
│       └── {project_name}_deployment_issues_solution.json
└── package.json               # Node.js dependency configuration

🛠️ Requirements

System Requirements

• Operating System: macOS, Linux, Windows
• Python: 3.7+
• Node.js: 14+
• Conda: For environment management

Dependencies

• Python: requests
• Node.js: @anthropic-ai/claude-code, diskusage, systeminformation, dotenv, tsx

📦 Installation

1. Clone Repository

git clone <repository-url>
cd testCC

2. Install Node.js Dependencies

npm install

3. Install Python Dependencies

pip install requests

4. Configure Environment Variables Create .env file:

ANTHROPIC_AUTH_TOKEN=your_anthropic_token
ANTHROPIC_BASE_URL=your_anthropic_base_url

5. Set Execution Permissions

chmod +x deploy.sh
chmod +x auto_deploy_all.py

🚀 Usage

Quick Start

Using shell script (recommended):

./deploy.sh https://github.com/username/project-name

Batch Deployment

./deploy.sh \
  https://github.com/black-forest-labs/flux \
  https://github.com/Stability-AI/generative-models \
  https://github.com/username/another-project

Direct Python Script Usage

python3 auto_deploy_all.py https://github.com/username/project-name

🔧 Core Components

1. AutoDeployManager (auto_deploy_all.py)

• Function: Batch project manager
• Features:
  • Automatically extracts project names
  • Modifies URLs in configuration files
  • Executes deployment processes
  • Generates deployment documentation
  • Runs RAG upload

2. Node.js Deployment Executor (autodeploy.js)

• Function: Interacts with Claude AI to execute deployments
• Features:
  • System environment detection
  • Disk space checking
  • Real-time deployment progress display
  • Error handling and logging

3. RAG Data Uploader (rag.py)

• Function: Uploads deployment issues to RAG knowledge base
• Features:
  • Parses JSON issue files
  • Batch uploads to server
  • Error retry mechanism
  • Progress statistics

📋 Deployment Process

1. Project Analysis: Extract project information from GitHub URL
2. Environment Detection: Analyze system environment and resources
3. Configuration Update: Modify relevant configuration files
4. Code Cloning: Clone project to local directory
5. Environment Creation: Create isolated environment using conda
6. Dependency Installation: Install project dependencies
7. Project Building: Execute build and test processes
8. Documentation Generation: Create deployment guides and issue solutions
9. RAG Upload: Upload issue data to knowledge base

📊 Output Files

Each successfully deployed project generates:

Deployment Documentation

• {project_name}_deployment.md: Complete deployment process documentation
• {project_name}_deployment_guide.json: Structured deployment steps
• {project_name}_deployment_issues_solution.json: Issue resolution solutions

Project Code

• projects/{project_name}/: Complete project source code

🔍 Troubleshooting

Common Issues

1. Environment Variables Not Set

   Error: ANTHROPIC_AUTH_TOKEN not set
   Solution: Check .env file configuration
   

2. Conda Environment Creation Failed

   Error: conda create failed
   Solution: Ensure conda is properly installed and configured
   

3. Network Connection Issues

   Error: Request timeout
   Solution: Check network connection and proxy settings
   

Log Viewing

Detailed logs during deployment are displayed in real-time in the terminal, including:

• System environment information
• Deployment step progress
• Error messages and solutions
• Final statistics

🤝 Contributing

Issues and Pull Requests are welcome to improve this project!

📄 License

ISC License

🔗 Related Links

• Claude AI
• Conda Documentation
• Node.js Documentation

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Generated by TestCC Auto-Deploy System