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🚧 Development Roadmap

Features currently in development and planned for future releases of Claude Code Usage Monitor.

🎯 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:

# 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:

# 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:

# 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

# 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

🛠️ Code Quality Tools

Ruff Integration: This project uses Ruff for fast Python linting and formatting.

# Install pre-commit for automatic code quality checks
uv tool install pre-commit --with pre-commit-uv

# Install pre-commit hooks
pre-commit install

# Run ruff manually
ruff check .          # Lint code
ruff format .         # Format code
ruff check --fix .    # Auto-fix issues

Pre-commit Hooks: Automatic code quality checks run before each commit:

  • Ruff linting and formatting
  • Import sorting
  • Trailing whitespace removal
  • YAML and TOML validation

VS Code Integration: The project includes VS Code settings for:

  • Auto-format on save with Ruff
  • Real-time linting feedback
  • Import organization
  • Consistent code style

🔄 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
    • Code is automatically formatted and linted via pre-commit hooks
    • 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 💬 GitHub: Open issues for feature discussions 🔧 Technical Questions: Include code examples and specific requirements

Ready to contribute? Check out CONTRIBUTING.md for detailed guidelines!