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# Clover CLI Implementation Plan
## Overview
This document outlines the implementation plan for the Clover CLI tool, a terminal-based assistant that works with various AI models to help build and manage projects. The tool will support multiple models, provide a set of core tools, and have several key features for project management.
This document outlines the complete implementation plan for the Clover CLI tool, a terminal-based assistant that works with various AI models to help build and manage projects. The tool will support multiple models, provide a comprehensive set of core tools, and have advanced features for project management, code generation, and repository integration.
## Project Structure
```
@ -15,7 +15,21 @@ clover/
│ ├── __init__.py
│ ├── file_tools.py # File operations (read, create, update, delete)
│ ├── project_tools.py # Project operations (summarize, structure)
│ └── commandline_tool.py # Command line execution tool
│ ├── commandline_tool.py # Command line execution tool
│ ├── git_tools.py # Git operations (status, diff, commit, push)
│ ├── lint_format_tools.py # Linting and formatting tools
│ ├── test_generation.py # Test generation tools
│ ├── docstring_tools.py # Docstring generation tools
│ ├── dependency_tools.py # Dependency management tools
│ ├── security_tools.py # Security scanning tools
│ ├── model_orchestration.py # Multi-model orchestration
│ ├── incremental_summary.py # Incremental project summarization
│ ├── sandbox_execution.py # Sandbox execution tools
│ ├── cost_tracking.py # Cost reporting mechanism
│ ├── profiling_tools.py # Performance profiling tools
│ ├── workflow_tools.py # Workflow and issue tracking tools
│ ├── language_detection.py # Language detection for tool selection
│ └── ide_integration.py # IDE integration support
├── models/ # Model interaction modules
│ ├── __init__.py
│ ├── model_manager.py # Manage available models
@ -39,16 +53,77 @@ clover/
- **create_file**: Create a new file with specified content
- **update_file**: Modify an existing file's content at specific lines or patterns
- **delete_file**: Remove a file from the project
- **list_files**: List all files in directory structure
### 2. Project Summary Tools
- **summarize_file**: Use another LLM to generate a summary of a specific file
- **get_project_structure**: Look for structure.md or generate it using LLM
- **aggregate_summaries**: Collect summaries from all files and create a combined project summary
- **incremental_summarization**: Re-summarize only changed files to save tokens and time
### 3. Command Line Tool
- **commandline**: Execute system commands with user permission
- **safe_execute**: Handle command execution safely with error handling and permissions
### 4. Git Integration Tools
- **git_status**: Query repository status and return changes
- **git_diff**: Generate JSON diff of changes for staged files
- **git_commit**: Commit changes with auto-generated commit message
- **git_push**: Push committed changes to remote repository
- **git_log**: Show commit history with structured output
### 5. Linting & Formatting Tools
- **lint_code**: Run linter (flake8, ESLint) on specified file(s)
- **format_code**: Run formatter (black, isort, prettier) on specified files
- **lint_format_report**: Return structured results of lint/format operations
### 6. Test Generation Tools
- **generate_tests**: Create unit tests for a file or module using LLM
- **test_coverage**: Analyze test coverage for given files
### 7. Documentation Tools
- **generate_docstring**: Auto-generate docstrings for functions/classes/modules
- **update_docstrings**: Update existing docstrings with current function purposes
### 8. Dependency Management Tools
- **scan_dependencies**: Parse requirements.txt, pyproject.toml, package.json etc.
- **add_dependency**: Add a package to project dependencies
- **remove_dependency**: Remove a package from project dependencies
- **dependency_report**: Generate structured dependency analysis
### 9. Security Tools
- **security_scan**: Run security audit (bandit) on project
- **vulnerability_report**: Return structured security findings
### 10. Multi-model Orchestration
- **model_selector**: Choose best LLM for specific sub-task based on cost/speed
- **task_orchestrator**: Schedule tools to appropriate model providers
- **cost_optimizer**: Track and optimize API costs across operations
### 11. Sandbox Execution Tools
- **sandbox_run**: Execute code in isolated container environment
- **container_manager**: Manage temporary containers for safe execution
### 12. Performance Analysis Tools
- **profile_execution**: Time how long a command or LLM request takes
- **cost_report**: Estimate token usage and API costs
- **performance_log**: Log execution timing for optimization
### 13. Workflow Management
- **create_issue**: Create GitHub/GitLab issue from model input
- **update_issue**: Update existing issue status
- **close_issue**: Close resolved issues
- **task_board**: Maintain task board with status tracking
### 14. Language Detection
- **detect_language**: Identify language of a file for tool selection
- **language_aware_tools**: Apply appropriate tools based on detected language
### 15. IDE Integration
- **ide_buffer_sync**: Send current buffer content to Clover assistant
- **vscode_ext**: Provide VSCode extension capabilities
- **neovim_integration**: Support Neovim integration
## Key Features Implementation
### 1. `/list` - List Available Models
@ -68,76 +143,175 @@ clover/
- Configure maximum threads used for concurrent LLM operations
- Manage thread pool for sub-processes like file summaries and command line calls
## Advanced Features Implementation
### Git Repository Management
1. **Git Operations Tools**:
- Git status: Query repository state for changes
- Git diff: Generate structured diffs for staging
- Git commit: Commit with auto-generating messages
- Git push: Push changes to remote repository
2. **Repository Sync Feature**:
- Single step commit-and-push workflow
- Automatic JSON diff generation and commit message
- Safe repository management with model interaction
### Code Quality Tools
1. **Linting & Formatting Suite**:
- Lint code with flake8, ESLint etc.
- Format code with black, isort, prettier
- Return structured results for quality analysis
2. **Testing Framework**:
- Generate unit tests from file/module content
- Bootstrap test coverage quickly
- Test generation with confidence-building approach
### Documentation Generation
1. **Docstring Tools**:
- Auto-generate docstrings
- Update existing docstrings with model understanding
- Improve code readability and typing
### Dependency Management
1. **Dependency Operations**:
- Scan project dependencies automatically
- Add/remove packages via pip/poetry/npm
- Generate dependency reports with conflicts detection
### Security Features
1. **Security Scanning**:
- Run security audits (bandit)
- Flag potential vulnerabilities early
- Return structured vulnerability reports
### Performance Optimization
1. **Cost Tracking**:
- Estimate token usage and API costs
- Multi-step task cost calculation
- Budget monitoring capabilities
2. **Profile Execution**:
- Time command or LLM request duration
- Log performance metrics for tuning
- Decorator-based timing tools
### Multi-model Orchestration
1. **Model Selection**:
- Choose best model for sub-task (cost/speed optimization)
- Scheduler for mapping tools to appropriate providers
- Cost optimization across multiple operations
## Implementation Details
### Model Integration
1. Abstract model interface that supports multiple providers (OpenAI, Anthropic, etc.)
2. Model manager to handle switching between different models
3. API client that handles authentication and requests
4. Multi-model orchestration capability for optimal performance/cost
### Configuration Management
- Environment variables for configuration
- Environment variables for configuration as specified in guidelines
- Settings file for persistent configuration storage
- Default fallback values for all settings
- Integration with containerized deployment via environment variables
### Thread Management
- Semaphore-based system for controlling concurrent LLM operations
- Thread pool implementation for managing sub-processes
- Thread pool implementation for managing sub-processes like file summaries and command line calls
- Safety limits to prevent resource exhaustion
### Security Considerations
- Permission prompts for command line execution
- Input validation for all user inputs
- Safe file paths to prevent directory traversal attacks
- Sandboxed execution for untrusted code snippets
## Dependencies to Install in Virtual Environment
- openai (for OpenAI API integration)
- requests (for HTTP requests)
- python-dotenv (for environment variable management)
- tqdm (for progress bars)
```
openai # For OpenAI API integration
requests # For HTTP requests
python-dotenv # For environment variable management
tqdm # For progress bars
GitPython # For Git operations
pylint # For linting quality checks
black # For Python code formatting
isort # For import sorting
bandit # For security scanning
docker # For sandbox execution (if using Docker)
pydantic # For data validation
```
## Development Approach
### Phase 1: Core Infrastructure
1. Start with basic CLI structure and command parsing
2. Implement core tools step by step
3. Add model integration capabilities
4. Implement configuration management
5. Add threading and timeout features
6. Implement permission prompts for command execution
7. Test all components thoroughly
8. Document functionality and usage
2. Implement core file tools (CRUD operations)
3. Add command line tool with permission prompts
### Phase 2: Model Integration
4. Add model interaction capabilities
5. Implement configuration management system
6. Add thread safety and timeout features
### Phase 3: Advanced Features
7. Implement Git integration tools for repository management
8. Add linting, formatting, and test generation tools
9. Include documentation and dependency management tools
10. Integrate security scanning capabilities
### Phase 4: Performance & Orchestration
11. Add sandbox execution and profiling tools
12. Implement multi-model orchestration system
13. Finalize all tool integrations and testing
## Testing Strategy
- Unit tests for individual tools and functions
- Integration tests for end-to-end CLI operations
- Security tests for file system access and command execution
- Configuration management tests
### Unit Tests for Tools
- Test individual file operations
- Validate Git command execution with mock repositories
- Ensure linter/formatter tools work correctly
- Test security scanning capabilities
### Integration Tests
- End-to-end CLI operation testing
- Multi-tool workflow testing (e.g., create file → lint → commit)
- Configuration management validation
- Security tests for execution environment
## Version Control Considerations
### File Management During Development
- Maintain .agent, .structure, and summary.md files during development
- Follow Git workflow for tracking changes
- Follow Git workflow for tracking changes through phases
- Keep requirements.txt updated with dependencies
```
### Documentation Updates
- Regularly update README.md during implementation
- Keep plan.md current with completed work
- Document new tools and features as they're developed
## Virtual Environment Setup Plan
1. Create a new virtual environment in the project directory:
1. Create virtual environment:
```bash
python -m venv clover_env
```
2. Activate the virtual environment:
2. Activate environment:
```bash
source clover_env/bin/activate # On macOS/Linux
# or
clover_env\Scripts\activate # On Windows
```
3. Install required packages:
3. Install dependencies:
```bash
pip install openai requests python-dotenv tqdm
pip install openai requests python-dotenv tqdm GitPython pylint black isort bandit docker pydantic
```
4. Initialize the project structure
4. Initialize the project structure and continue development
## Command Line Interface Design
@ -146,6 +320,8 @@ The CLI should support commands like:
- `clover /init` - Initialize project
- `clover /timeout 300` - Set timeout to 300 seconds
- `clover /threads 5` - Set thread limit to 5
- Direct prompts for AI assistance
- `clover /git_status` - Query git repository status
- `clover /lint_file main.py` - Lint a specific file
- `clover "Write a Python function to calculate factorial"` - Send prompt to AI assistant
Each command will be implemented in the commands.py file with proper error handling and validation.
Each command will be implemented in the commands.py file with proper error handling and validation, following modular design principles.

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# Clover CLI Progress Report
## Overview
This document tracks the implementation progress of the Clover CLI tool based on the comprehensive plan that includes both core and advanced features.
## Core Features Implemented
### 1. Basic CLI Infrastructure
- [x] Main entry point (main.py)
- [x] CLI argument parsing with argparse
- [x] Command handling module (cli/commands.py)
- [x] Configuration management system (config/settings.py)
- [x] Virtual environment setup and dependencies
### 2. Core File Operations
- [x] `read_file` - Read content from a file
- [x] `create_file` - Create a new file with specified content
- [x] `update_file` - Modify existing file content
- [x] `delete_file` - Remove files from project
- [x] `list_files` - List all files in directory structure
### 3. Command Line Execution
- [x] `commandline` - Execute system commands with permission prompts
- [x] `safe_execute` - Handle execution safely with error handling
### 4. Project Structure Management
- [x] `get_project_structure` - Look for structure.md or generate it using LLM interface
- [x] `aggregate_summaries` - Collect summaries from all files
- [x] `/init` command functionality to create clover.md file
- [x] `/list` command for listing models (simulated)
### 5. Configuration Management
- [x] Environment variable support (CLOVER_TIMEOUT, CLOVER_THREADS, CLOVER_MODEL, etc.)
- [x] Default configuration values
- [x] Settings loading and saving functions
## Advanced Features Implemented
### 1. Git Integration Tools
- [ ] `git_status` - Query repository status
- [ ] `git_diff` - Generate JSON diff of changes
- [ ] `git_commit` - Commit changes with auto-generated messages
- [ ] `git_push` - Push committed changes to remote repository
### 2. Linting & Formatting Tools
- [ ] `lint_code` - Run linter on specified files
- [ ] `format_code` - Run formatter on specified files
- [ ] `lint_format_report` - Return structured results
### 3. Test Generation Tools
- [ ] `generate_tests` - Create unit tests using LLM
- [ ] `test_coverage` - Analyze test coverage
### 4. Documentation Tools
- [ ] `generate_docstring` - Auto-generate docstrings
- [ ] `update_docstrings` - Update existing docstrings
### 5. Dependency Management
- [ ] `scan_dependencies` - Parse requirements files
- [ ] `add_dependency` - Add packages to project dependencies
- [ ] `remove_dependency` - Remove packages from project dependencies
### 6. Security Tools
- [ ] `security_scan` - Run security audit with bandit
- [ ] `vulnerability_report` - Return structured vulnerability reports
### 7. Multi-model Orchestration
- [ ] `model_selector` - Choose best LLM for sub-task
- [ ] `task_orchestrator` - Schedule tools to appropriate providers
- [ ] `cost_optimizer` - Track and optimize API costs
### 8. Sandbox Execution Tools
- [ ] `sandbox_run` - Execute code in isolated container
- [ ] `container_manager` - Manage temporary containers
### 9. Performance Analysis
- [ ] `profile_execution` - Time command/LLM requests
- [ ] `cost_report` - Estimate token usage and API costs
- [ ] `performance_log` - Log execution timing
### 10. Workflow Management
- [ ] `create_issue` - Create GitHub/GitLab issues
- [ ] `update_issue` - Update existing issue status
- [ ] `close_issue` - Close resolved issues
- [ ] `task_board` - Maintain task board with status tracking
### 11. Language Detection
- [ ] `detect_language` - Identify file language for tool selection
- [ ] `language_aware_tools` - Apply appropriate tools based on language
### 12. IDE Integration
- [ ] `ide_buffer_sync` - Send current buffer content to assistant
- [ ] `vscode_ext` - Provide VSCode extension capabilities
- [ ] `neovim_integration` - Support Neovim integration
## In Progress Features
### Project Structure Tools
- [x] Placeholder implementations for summarize_file and incremental_summarization
- [x] Basic framework for aggregate_summaries
### Configuration System
- [x] Environment variable handling
- [x] Default settings configuration
### Command Execution System
- [x] Permission-based command execution with safe_execute
- [ ] Full integration to work across all commands
## Next Implementation Steps
### Phase 1: Git & Code Quality Tools
1. Implement Git operations tools (git_status, git_diff, git_commit, git_push)
2. Add linting and formatting capabilities (lint_code, format_code)
### Phase 2: Testing & Documentation
3. Create test generation tools (generate_tests)
4. Implement documentation tools (generate_docstring)
### Phase 3: Dependency & Security
5. Build dependency management system (scan_dependencies, add/remove packages)
6. Add security scanning capabilities (security_scan)
### Phase 4: Advanced Orchestration
7. Multi-model selection and orchestration system
8. Performance profiling and cost tracking
### Phase 5: Workflow & IDE Integration
9. Issue tracking and workflow tools
10. IDE extension support
## Status Summary
- **Core Infrastructure**: 100% complete
- **Basic File Operations**: 100% complete
- **Command Line Execution**: 100% complete
- **Configuration Management**: 100% complete
- **Advanced Features**: 0% complete (in development)