context-cleaner 0.1.0

Advanced productivity tracking and context optimization for AI-assisted development

Context Cleaner

Advanced productivity tracking and context optimization for AI-assisted development

Context Cleaner is a comprehensive productivity tracking tool designed specifically for developers using AI coding assistants like Claude Code. It provides intelligent context health monitoring, performance analytics, and optimization recommendations to maximize development productivity.

🎯 Key Features

📊 Productivity Tracking

• Development session monitoring with automatic boundary detection
• Context health scoring (0-100 scale) based on size, structure, and complexity
• Performance correlation analysis between context optimization and productivity
• Git integration for commit pattern analysis and productivity metrics

🔍 Advanced Analytics

• Trend analysis across days, weeks, and months
• Pattern recognition for optimal work schedules and session lengths
• A/B testing framework for different context optimization strategies
• Productivity forecasting based on historical data and patterns

📈 Interactive Dashboard

• Web-based visualization of productivity trends and session analytics
• Real-time metrics with automatic refresh and live updates
• Actionable insights with specific optimization recommendations
• Mobile-responsive design for monitoring on any device

🛡️ Privacy-First Architecture

• Local-only processing - all data stays on your machine
• No external requests - zero network dependencies
• Complete data ownership with easy export and deletion
• Transparent operation with open-source codebase

🔧 Developer-Friendly CLI

• Simple installation via pip with zero configuration required
• Flexible commands for all productivity tracking and analysis needs
• Multiple output formats (JSON, YAML, text) for integration
• Comprehensive help and troubleshooting built-in

🚀 Quick Start

Installation

# Install from PyPI
pip install context-cleaner

# Or install from source
git clone https://github.com/context-cleaner/context-cleaner.git
cd context-cleaner
pip install -e .

Basic Usage

# Start productivity tracking
context-cleaner start

# Launch interactive dashboard
context-cleaner dashboard

# Analyze recent productivity
context-cleaner analyze --days 7

# Export your data
context-cleaner export --output my-data.json

Dashboard Access

After running context-cleaner dashboard, visit http://localhost:8548 to access your productivity insights through an intuitive web interface.

📊 Usage Examples

Development Workflow Integration

# Start tracking at beginning of work session
context-cleaner start

# View real-time productivity dashboard
context-cleaner dashboard --port 8080

# Get daily productivity summary
context-cleaner analyze --days 1 --format text

# Weekly productivity report in JSON
context-cleaner analyze --days 7 --format json --output weekly-report.json

Productivity Analysis

# Comprehensive 30-day analysis
context-cleaner analyze --days 30

# Example output:
# 📊 PRODUCTIVITY ANALYSIS REPORT
# ================================
# 📅 Analysis Period: Last 30 days
# 🎯 Average Productivity Score: 87.3/100
# 📈 Total Sessions: 156
# ⚡ Optimization Events: 45
# 🌟 Most Productive Day: Tuesday
# 
# 💡 RECOMMENDATIONS:
#    1. Your productivity peaks at 3-4 PM - schedule complex tasks then
#    2. Context optimization events correlate with 18% productivity increase
#    3. Consider shorter sessions (< 90 minutes) for sustained performance

Data Management

# Export all data for backup
context-cleaner export --format json --output backup-$(date +%Y%m%d).json

# Show privacy information
context-cleaner privacy show-info

# Delete all collected data
context-cleaner privacy delete-all

📈 Dashboard Features

Productivity Metrics Overview

• Current productivity score with 7-day trend
• Session statistics including count, duration, and quality
• Optimization events tracking context improvements
• Health trend indicators showing improvement/decline patterns

Advanced Visualizations

• Time-series charts showing productivity trends over time
• Heatmaps indicating peak performance hours and days
• Session type analysis (debugging, coding, exploration, optimization)
• Correlation charts between context health and productivity

Actionable Insights

• Personalized recommendations based on individual productivity patterns
• Optimal scheduling suggestions for different types of development tasks
• Context optimization timing recommendations for maximum impact
• Performance alerts when productivity patterns change significantly

🔧 Configuration

Default Configuration

Context Cleaner works out-of-the-box with sensible defaults, but can be customized via configuration files.

Configuration File (Optional)

Create ~/.context_cleaner/config.yaml:

dashboard:
  port: 8548
  host: localhost
  auto_refresh: true
  cache_duration: 300

tracking:
  enabled: true
  sampling_rate: 1.0
  session_timeout_minutes: 30
  data_retention_days: 90

privacy:
  local_only: true
  encrypt_storage: true
  require_consent: true

analysis:
  health_thresholds:
    excellent: 90
    good: 70
    fair: 50
  max_context_size: 100000

Environment Variables

export CONTEXT_CLEANER_PORT=8080
export CONTEXT_CLEANER_DATA_DIR=~/my-productivity-data
export CONTEXT_CLEANER_LOG_LEVEL=DEBUG

🧪 Advanced Features

Custom Analytics

from context_cleaner import ProductivityAnalyzer, ContextCleanerConfig

# Initialize with custom configuration
config = ContextCleanerConfig.default()
analyzer = ProductivityAnalyzer(config)

# Analyze context health
result = await analyzer.analyze_context_health(context_data)
print(f"Health Score: {result.health_score}/100")

# Analyze productivity session
metrics = analyzer.analyze_productivity_session(session_data)
print(f"Productivity Score: {metrics.productivity_score}/100")

Integration with Development Tools

# Git hook integration (post-commit)
#!/bin/bash
context-cleaner analyze --days 1 --format json | jq '.avg_productivity_score'

# CI/CD integration
context-cleaner analyze --days 7 --format json > productivity-report.json

Custom Dashboard Themes

The dashboard supports custom CSS themes and can be extended with additional charts and metrics.

🔒 Privacy & Security

Data Protection

• Local Storage: All data is stored locally in ~/.context_cleaner/data/
• No Telemetry: Zero external network requests or data transmission
• Encryption: Optional at-rest encryption for sensitive productivity data
• Data Retention: Configurable automatic cleanup after specified period

Privacy Controls

• Granular Tracking: Choose exactly what metrics to collect
• Easy Deletion: One-command complete data removal
• Data Export: Full data portability in standard formats
• Transparent Operation: Open-source code for complete auditability

Security Features

• Input Validation: Comprehensive sanitization of all data
• Resource Limits: Built-in protection against resource exhaustion
• Safe Defaults: Conservative configuration prioritizing privacy
• Error Isolation: System continues working even with component failures

🏗️ Architecture

Core Components

Context Cleaner Architecture
├── 📊 Analytics Engine
│   ├── ProductivityAnalyzer - Core analysis algorithms
│   ├── TrendCalculator - Time-series analysis  
│   └── ImpactEvaluator - Optimization effectiveness
├── 📈 Dashboard System
│   ├── Web Server - FastAPI-based interface
│   ├── Data Visualization - Interactive charts
│   └── Real-time Updates - Live metric streaming
├── 🗃️ Data Management
│   ├── Session Tracking - Development session boundaries
│   ├── Storage System - Local SQLite database
│   └── Privacy Controls - Data export/deletion
└── 🔧 CLI Interface
    ├── Command Processing - Argument parsing and validation
    ├── Output Formatting - Text/JSON/YAML formats
    └── Configuration - Settings and preferences

Data Flow

1. Data Collection - Monitor development sessions and context changes
2. Analysis Processing - Calculate health scores and productivity metrics
3. Pattern Recognition - Identify trends and optimization opportunities
4. Insight Generation - Create actionable recommendations
5. Visualization - Present insights through dashboard or CLI

📚 API Reference

Command Line Interface

context-cleaner [OPTIONS] COMMAND [ARGS]...

Commands:
  start              Start productivity tracking
  dashboard          Launch web dashboard
  analyze            Analyze productivity trends  
  export             Export all data
  privacy            Privacy and data management
  config-show        Show current configuration

Python API

from context_cleaner import ContextCleanerConfig, ProductivityAnalyzer

# Configuration
config = ContextCleanerConfig.from_file('config.yaml')
config = ContextCleanerConfig.from_env()  # Environment variables
config = ContextCleanerConfig.default()   # Sensible defaults

# Analytics
analyzer = ProductivityAnalyzer(config)
result = await analyzer.analyze_context_health(data)
metrics = analyzer.analyze_productivity_session(session_data)

# Dashboard
from context_cleaner import ProductivityDashboard
dashboard = ProductivityDashboard(config)
dashboard.start_server(host="localhost", port=8548)

🧪 Development

Development Setup

# Clone repository
git clone https://github.com/context-cleaner/context-cleaner.git
cd context-cleaner

# Install development dependencies
pip install -e .[dev]

# Run tests
pytest

# Code formatting
black src/
flake8 src/

# Type checking
mypy src/

Testing

# Run all tests
pytest

# Run specific test categories  
pytest -m unit      # Unit tests only
pytest -m integration  # Integration tests only

# Coverage report
pytest --cov=context_cleaner --cov-report=html

Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch: git checkout -b feature/awesome-feature
3. Make changes and add tests
4. Run test suite: pytest
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Support

Documentation

• Installation Guide
• Usage Examples
• API Reference
• Privacy Policy

Community

• GitHub Issues - Bug reports and feature requests
• GitHub Discussions - Questions and community support
• Documentation - Comprehensive guides and tutorials

Commercial Support

For enterprise support, custom integrations, and consulting services, please contact us at [e-mail].

🎯 Roadmap

Version 0.2.0 (Next Release)

• Advanced ML Analytics: Machine learning-powered productivity insights
• Team Features: Aggregated (anonymized) team productivity metrics
• IDE Integration: Direct integration with popular development environments
• Extended Git Integration: Branch-based productivity tracking

Version 0.3.0 (Future)

• Cross-Project Analytics: Multi-repository productivity tracking
• Advanced Forecasting: Predictive productivity modeling
• Custom Metrics: User-defined productivity indicators
• API Integration: Webhooks and external service integration

Long-term Vision

• AI-Powered Coaching: Personalized productivity improvement recommendations
• Collaboration Analytics: Pair programming and code review productivity
• Performance Benchmarking: Industry-wide anonymous productivity comparisons
• Ecosystem Integration: Seamless integration with development tool ecosystems

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Context Cleaner - Transforming AI-assisted development through intelligent productivity tracking and optimization.

Built with ❤️ for developers who want to understand and improve their coding productivity.