# 🚀 Future Development Roadmap

> **⚠️ IMPORTANT**: This document describes **FUTURE PLANS** and **ASPIRATIONAL FEATURES** that do NOT currently exist. The current implementation is a simple Python script that monitors Claude token usage. This roadmap represents potential future enhancements.

## 📌 Current State

The Claude Code Usage Monitor is currently a **single Python script** (`ccusage_monitor.py`) that:
- ✅ Monitors real-time token usage
- ✅ Shows visual progress bars
- ✅ Predicts token depletion
- ✅ Supports multiple Claude plans
- ✅ Works cross-platform

## 🔮 Future Vision

Below are ideas for potential future development. These features are NOT implemented yet:


## 🎯 Current Development Status

### 🧠 ML-Powered Auto Mode
**Status**: 🔶 In Active Development  

#### Overview
Intelligent Auto Mode with machine learning will actively learn your actual token limits and usage patterns.

#### How It Will Work

**📊 Data Collection Pipeline**:
- Monitors and stores token usage patterns in local DuckDB database
- Tracks session starts, consumption rates, and limit boundaries
- Builds comprehensive dataset of YOUR specific usage patterns
- No data leaves your machine - 100% local processing

**🤖 Machine Learning Features**:
- **Pattern Recognition**: Identifies recurring usage patterns and peak times
- **Anomaly Detection**: Spots when your token allocation changes
- **Regression Models**: Predicts future token consumption based on historical data
- **Classification**: Automatically categorizes your usage tier (Pro/Max5/Max20/Custom)

**💾 DuckDB Integration**:
- Lightweight, embedded analytical database
- No external server required - all data stays local
- Efficient SQL queries for real-time analysis
- Automatic data optimization and compression

**🎯 Dynamic Adaptation**:
- Learns your actual limits, not predefined ones
- Adapts when Claude changes your allocation
- Improves predictions with each session
- No manual plan selection needed

#### Benefits Over Static Limits

| Current Approach | ML-Powered Approach |
|-----------------|---------------------|
| Fixed 7K, 35K, 140K limits | Learns YOUR actual limits |
| Manual plan selection | Automatic detection |
| Basic linear predictions | Advanced ML predictions |
| No historical learning | Improves over time |
| Can't adapt to changes | Dynamic adaptation |

#### Data Privacy & Security

- **🔒 100% Local**: All ML processing happens on your machine
- **🚫 No Cloud**: Your usage data never leaves your computer
- **💾 Local Database**: DuckDB stores data in `~/.claude_monitor/usage.db`
- **🗑️ Easy Cleanup**: Delete the database file to reset ML learning
- **🔐 Your Data, Your Control**: No telemetry, no tracking, no sharing

#### Development Tasks

- [ ] **Database Schema Design** - Design DuckDB tables for usage data
- [ ] **Data Collection Module** - Implement usage pattern tracking
- [ ] **ML Pipeline** - Create model training and prediction system
- [ ] **Pattern Analysis** - Develop usage pattern recognition
- [ ] **Auto-Detection Engine** - Smart plan switching based on ML
- [ ] **Performance Optimization** - Efficient real-time ML processing
- [ ] **Testing Framework** - Comprehensive ML model testing

---

### 📦 PyPI Package
**Status**: 🔶 In Planning Phase

#### Overview
Publish Claude Code Usage Monitor as an easy-to-install pip package for system-wide availability.

#### Planned Features

**🚀 Easy Installation**:
```bash
# Future installation method
pip install claude-usage-monitor

# Run from anywhere
claude-monitor --plan max5 --reset-hour 9
```

**⚙️ System Integration**:
- Global configuration files (`~/.claude-monitor/config.yaml`)
- User preference management
- Cross-platform compatibility (Windows, macOS, Linux)

**📋 Command Aliases**:
- `claude-monitor` - Main command
- `cmonitor` - Short alias
- `ccm` - Ultra-short alias

**🔄 Auto-Updates**:
```bash
# Easy version management
pip install --upgrade claude-usage-monitor
claude-monitor --version
claude-monitor --check-updates
```

#### Development Tasks

- [ ] **Package Structure** - Create proper Python package structure
- [ ] **Setup.py Configuration** - Define dependencies and metadata
- [ ] **Entry Points** - Configure command-line entry points
- [ ] **Configuration System** - Implement global config management
- [ ] **Cross-Platform Testing** - Test on Windows, macOS, Linux
- [ ] **Documentation** - Create PyPI documentation
- [ ] **CI/CD Pipeline** - Automated testing and publishing
- [ ] **Version Management** - Semantic versioning and changelog

#### Package Architecture

```
claude-usage-monitor/
├── claude_monitor/
│   ├── __init__.py
│   ├── cli.py          # Command-line interface
│   ├── monitor.py      # Core monitoring logic
│   ├── config.py       # Configuration management
│   ├── ml/            # ML components (future)
│   └── utils.py       # Utilities
├── setup.py
├── requirements.txt
├── README.md
└── tests/
```

---

### 🐳 Docker Image
**Status**: 🔶 In Planning Phase  

#### Overview
Docker containerization for easy deployment, consistent environments, and optional web dashboard.

#### Planned Features

**🚀 One-Command Setup**:
```bash
# Future Docker usage
docker run -e PLAN=max5 -e RESET_HOUR=9 maciek/claude-usage-monitor

# With persistent data
docker run -v ~/.claude_monitor:/data maciek/claude-usage-monitor

# Web dashboard mode
docker run -p 8080:8080 maciek/claude-usage-monitor --web-mode
```

**🔧 Environment Configuration**:
- `CLAUDE_PLAN` - Set monitoring plan
- `RESET_HOUR` - Configure reset time
- `TIMEZONE` - Set timezone
- `WEB_MODE` - Enable web dashboard
- `ML_ENABLED` - Enable ML features

**📊 Web Dashboard**:
- Real-time token usage visualization
- Historical usage charts
- Session timeline view
- Mobile-responsive interface
- REST API for integrations

**⚡ Lightweight Design**:
- Alpine Linux base image
- Multi-stage build optimization
- Minimal resource footprint
- Fast startup time

#### Development Tasks

- [ ] **Dockerfile Creation** - Multi-stage build optimization
- [ ] **Web Interface** - React-based dashboard development
- [ ] **API Design** - REST API for data access
- [ ] **Volume Management** - Persistent data handling
- [ ] **Environment Variables** - Configuration via env vars
- [ ] **Docker Compose** - Easy orchestration
- [ ] **Security Hardening** - Non-root user, minimal attack surface
- [ ] **Documentation** - Docker deployment guide

#### Docker Architecture

```dockerfile
# Multi-stage build example
FROM node:alpine AS web-builder
# Build web dashboard

FROM python:alpine AS app
# Install Python dependencies
# Copy web assets
# Configure entry point
```

---

## 🌟 Future Features

### 📈 Advanced Analytics (v2.5)
- Historical usage tracking and insights
- Weekly/monthly usage reports
- Usage pattern visualization
- Trend analysis and forecasting

### 🔔 Smart Notifications (v2.2)
- Desktop notifications for token warnings
- Email alerts for usage milestones
- Slack/Discord integration
- Webhook support for custom integrations

### 📊 Enhanced Visualizations (v2.3)
- Real-time ML prediction graphs
- Confidence intervals for predictions
- Interactive usage charts
- Session timeline visualization

### 🌐 Multi-user Support (v3.0)
- Team usage coordination
- Shared usage insights (anonymized)
- Organization-level analytics
- Role-based access control

### 📱 Mobile App (v3.5)
- iOS/Android apps for remote monitoring
- Push notifications
- Mobile-optimized dashboard
- Offline usage tracking

### 🧩 Plugin System (v4.0)
- Custom notification plugins
- Third-party integrations
- User-developed extensions
- Plugin marketplace

---

## 🔬 Research & Experimentation

### 🧠 ML Algorithm Research
**Current Focus**: Evaluating different ML approaches for token prediction

**Algorithms Under Consideration**:
- **LSTM Networks**: For sequential pattern recognition
- **Prophet**: For time series forecasting with seasonality
- **Isolation Forest**: For anomaly detection in usage patterns
- **DBSCAN**: For clustering similar usage sessions
- **XGBoost**: For feature-based limit prediction

**Research Questions**:
- How accurately can we predict individual user token limits?
- What usage patterns indicate subscription tier changes?
- Can we detect and adapt to Claude API changes automatically?
- How much historical data is needed for accurate predictions?

### 📊 Usage Pattern Studies
**Data Collection** (anonymized and voluntary):
- Token consumption patterns across different subscription tiers
- Session duration and frequency analysis
- Geographic and timezone usage variations
- Correlation between coding tasks and token consumption

### 🔧 Performance Optimization
**Areas of Focus**:
- Real-time ML inference optimization
- Memory usage minimization
- Battery life impact on mobile devices
- Network usage optimization for web features

---

## 🤝 Community Contributions Needed

### 🧠 ML Development
**Skills Needed**: Python, Machine Learning, DuckDB, Time Series Analysis

**Open Tasks**:
- Implement ARIMA models for token prediction
- Create anomaly detection for usage pattern changes
- Design efficient data storage schema
- Develop model validation frameworks

### 🌐 Web Development
**Skills Needed**: React, TypeScript, REST APIs, Responsive Design

**Open Tasks**:
- Build real-time dashboard interface
- Create mobile-responsive layouts
- Implement WebSocket for live updates
- Design intuitive user experience

### 🐳 DevOps & Infrastructure
**Skills Needed**: Docker, CI/CD, GitHub Actions, Package Management

**Open Tasks**:
- Create efficient Docker builds
- Set up automated testing pipelines
- Configure PyPI publishing workflow
- Implement cross-platform testing

### 📱 Mobile Development
**Skills Needed**: React Native, iOS/Android, Push Notifications

**Open Tasks**:
- Design mobile app architecture
- Implement offline functionality
- Create push notification system
- Optimize for battery life

---

## 📋 Development Guidelines

### 🔄 Development Workflow

1. **Feature Planning**
   - Create GitHub issue with detailed requirements
   - Discuss implementation approach in issue comments
   - Get feedback from maintainers before starting

2. **Development Process**
   - Fork repository and create feature branch
   - Follow code style guidelines (PEP 8 for Python)
   - Write tests for new functionality
   - Update documentation

3. **Testing Requirements**
   - Unit tests for core functionality
   - Integration tests for ML components
   - Cross-platform testing for packaging
   - Performance benchmarks for optimization

4. **Review Process**
   - Submit pull request with clear description
   - Respond to code review feedback
   - Ensure all tests pass
   - Update changelog and documentation

### 🎯 Contribution Priorities

**High Priority**:
- ML algorithm implementation
- PyPI package structure
- Cross-platform compatibility
- Performance optimization

**Medium Priority**:
- Web dashboard development
- Docker containerization
- Advanced analytics features
- Mobile app planning

**Low Priority**:
- Plugin system architecture
- Multi-user features
- Enterprise features
- Advanced integrations

---

## 📞 Developer Contact

For technical discussions about development:

**📧 Email**: [maciek@roboblog.eu](mailto:maciek@roboblog.eu)  
**💬 GitHub**: Open issues for feature discussions  
**🔧 Technical Questions**: Include code examples and specific requirements  

---

*Ready to contribute? Check out [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines!*
