This is a submission for the GitHub Copilot CLI Challenge
What I Built
Disk Octopus - Intelligent Storage Analytics powered by AI
I built a professional-grade terminal application that transforms how users analyze and understand their disk storage. Disk Octopus combines sophisticated file system visualization with AI-powered analysis to provide deep insights into storage usage patterns.
Key Features Built:
🖥️ Interactive Terminal UI
- Split-panel interface with resizable sections using Textual framework
- Beautiful braille octopus ASCII art for visual appeal
- Real-time file tree with intelligent lazy-loading
- Status bar with progress tracking
📊 Advanced File Analysis
- Recognition and categorization of 40+ file types (code, documents, media, config, specialized formats)
- Real-time file type statistics and storage distribution analysis
- File path indexing and searchable results
- Intelligent file complexity classification
🤖 AI-Powered Deep Analysis
- Integration with GitHub Copilot CLI for enhanced file analysis
- Intelligent fallback analyzer using pattern-based analysis when Copilot unavailable
- Analysis of file purpose, usage context, creator information, and practical assessments
- Support for files up to 5MB with streaming analysis
⚡ Performance Optimizations
- Lazy-loading of directory trees for instant responsiveness
- Intelligent caching of analysis results
- Efficient terminal rendering with minimal CPU usage
- Async file scanning with real-time progress
Technical Architecture:
- Core: Python 3.10+ with Textual TUI framework
- UI: Rich terminal formatting with CSS-based styling
- Analysis: Pattern-based intelligent analyzer + Copilot CLI integration
- File System: Recursive directory scanning with size tracking
- Data Processing: Real-time statistics aggregation
Why I Built This:
Traditional disk analysis tools like WizTree are GUI-only and resource-intensive. Disk Octopus brings powerful storage analytics directly to the terminal - where developers work. The AI integration transforms generic file analysis into actionable insights about file purposes, storage patterns, and disk organization opportunities.
Demo
Live Repository: https://github.com/n-92/Disk-Octopus
Screenshots:
Splash Screen - Welcoming interface with octopus branding
Main Analysis Interface - Comprehensive disk visualization
File Statistics - Real-time storage distribution analysis
Copilot-Powered Analysis - AI-generated file insights
Deep Analysis Results - Intelligent file categorization and assessment
File Paths Display - Complete file indexing and organization
Quick Start:
# Clone and setup
git clone https://github.com/n-92/Disk-Octopus.git
cd Disk-Octopus
pip install -r requirements.txt
# Run
python main.py
# Navigate with arrow keys, press 'd' for deep analysis, 'q' to quit
Key Keyboard Shortcuts:
-
q- Quit -
h- Help menu -
d- Deep file analysis with Copilot -
a- Quick file type analysis -
s- Show statistics -
↑/↓- Navigate
My Experience with GitHub Copilot CLI
How I Used Copilot CLI:
1. Deep File Analysis Integration
I integrated GitHub Copilot CLI to analyze file contents and generate intelligent commentary. The challenge was that subprocess calls with complex prompts would hang on Windows. I solved this by implementing an intelligent fallback analyzer that uses pattern-based analysis, while still leveraging Copilot when available.
2. Problem-Solving for Complex Issues
When deep analysis wasn't working (files showing "Uploading... Please Wait" indefinitely), I used Copilot to help debug the subprocess hanging issue. This led me to discover PowerShell argument parsing limitations and implement a robust fallback mechanism.
3. Development Acceleration
Copilot CLI helped me:
- Generate analysis frameworks for file categorization
- Create intelligent fallback logic for when Copilot is unavailable
- Develop comprehensive file type recognition across 40+ formats
- Debug threading and async issues with terminal UI updates
Impact on Development Experience:
Positive Impacts:
- ✅ Reduced debugging time for complex subprocess issues
- ✅ Accelerated implementation of file type detection system
- ✅ Helped design the intelligent fallback analyzer architecture
- ✅ Provided pattern suggestions for file analysis frameworks
- ✅ Enhanced code quality through intelligent suggestions
Technical Achievements:
- Built a production-ready terminal application with advanced features
- Implemented robust error handling and fallback mechanisms
- Created 40+ file type recognition system
- Achieved seamless Copilot integration with graceful degradation
- Delivered comprehensive documentation and user guides
Key Learning:
The biggest insight was designing systems that gracefully degrade when external services (like Copilot CLI) are unavailable. This approach made the application more robust and user-friendly, as it works perfectly even without Copilot while providing enhanced features when available.
Copilot CLI Integration Details:
The application automatically:
- Detects if Copilot CLI is installed
- Attempts analysis via Copilot when available
- Falls back to intelligent pattern-based analysis if not
- Shows users which analysis method was used
- Provides meaningful insights either way
This design philosophy made Disk Octopus inclusive for all users, regardless of their GitHub Copilot subscription status.
Project Highlights
- Professional Quality: Production-ready code with clean architecture
- Cross-Platform: Works on Windows, macOS, and Linux
- Well-Documented: Comprehensive README with screenshots and setup guides
- User-Friendly: Intuitive keyboard navigation and clear help system
- Open Source: MIT licensed, ready for community contributions
- Robust: Handles large directories and files gracefully
Version: 1.0.0
Author: N-92
Technologies: Python 3.10+, Textual, Rich, GitHub Copilot CLI
Repository: https://github.com/n-92/Disk-Octopus
Special Thanks: GitHub Copilot CLI for intelligent code assistance and analysis acceleration.
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