75 lines
3.0 KiB
Markdown
75 lines
3.0 KiB
Markdown
```# Clover CLI Development Summary
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## Project Setup and Planning
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1. **Project Analysis**: Analyzed the requirement for a Claude-like CLI tool that works with multiple AI models for code generation and project management.
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2. **Implementation Plan**: Created detailed plan in plan.md outlining:
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- Complete project structure
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- Core tools: file operations, project summary, command execution
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- Key features: /list, /init, /timeout, /threads commands
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- Model integration approach
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- Configuration management
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## Development Progress
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### 1. Project Structure Creation
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- Created modular directory structure with cli, tools, models, config, and utils
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- Setup main entry point (main.py)
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- Implemented CLI parser with argument handling for all required commands
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- Established configuration management system using environment variables
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### 2. Core Tool Implementation
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- **File Operations**: Implemented read_file, create_file, update_file, delete_file tools in tools/file_tools.py
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- **Command Execution**: Built commandline_tool.py with safe execution and permission prompts
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- **Project Tools**: Created placeholder structure for project operations in tools/project_tools.py
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### 3. Command Infrastructure
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- Developed CLI commands module (cli/commands.py) to handle:
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- /init command for initializing project files
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- /list command for listing models (simulated)
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- /timeout command for setting operation timeouts
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- /threads command for configuring thread limits
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- Regular prompts for AI assistant interaction
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### 4. Documentation and Setup
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- Created comprehensive README.md with usage instructions
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- Generated requirements.txt with dependencies
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- Created project structure diagram (.structure file)
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- Documented development process in .agent file
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## Technical Approach
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### Virtual Environment
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- Set up virtual environment (clover_env) to avoid global package installations
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- Installed required dependencies: openai, requests, python-dotenv, tqdm
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### Security Measures
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- Implemented permission prompts for command execution
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- Added input validation practices
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- Used safe file paths to prevent directory traversal
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### Design Principles
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- Modular architecture with separation of concerns
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- Configuration via environment variables as recommended
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- Extensible design ready for LLM integration
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## Next Steps
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1. Complete project structure generation functionality
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2. Add full LLM model integration capabilities
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3. Implement comprehensive /init functionality to create proper project summaries
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4. Develop actual file summarization using LLMs
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5. Integrate with OpenAI-compatible API endpoints
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6. Add unit and integration testing
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## Compliance with Guidelines
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- All development performed within virtual environment (clover_env)
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- No global package installations made
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- Environment variables used for configuration
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- Following Python best practices as specified in guidelines
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- Modular design suitable for future Docker deployment
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The foundation for a fully functional Clover CLI has been established, providing the core infrastructure needed for AI-powered terminal assistance.
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