Spec: GitBey Core Feature Implementation

[jedwards78]

GitBey Core Feature Specification

Overview

GitBey is a mobile app that analyzes a GitHub user's commit history and returns 5 Beyoncé songs based on the "vibe" of their last 50 commits. Users should be able to play the songs in the app.

Product Overview

• Core value proposition: A mobile app that analyzes a GitHub user's commit history and recommends Beyoncé songs that match the mood or "vibe" of their recent development activity, with integrated music playback.
• Target audience: Software developers who use GitHub and enjoy music while coding, particularly those interested in novel tech-music crossovers and Beyoncé fans.

Functional Specifications

2.1 GitHub Handle Input

Allow users to enter any GitHub username to analyze.

2.2 Commit Retrieval

Access and fetch the last 50 commits of the specified GitHub user.

2.3 Sentiment Analysis

Analyze commit messages and patterns to determine the overall "vibe" or mood.

2.4 Song Matching

Select 5 Beyoncé songs that best match the determined mood from the commits.

2.5 Music Playback

Enable in-app playback of the recommended songs.

2.6 User Profiles

Save favorite GitHub handles and view history of past recommendations.

2.7 Social Sharing

Share song recommendations with others via social media.

Technical Specifications

• Platform: iOS (initial focus with potential for Android expansion)
• Frontend Framework: Swift with SwiftUI for native iOS development
• Backend Components:
  • GitHub API integration for commit data retrieval
  • Natural Language Processing for sentiment analysis of commits
  • Music service API (likely Spotify) for song metadata and playback
• Authentication: OAuth implementation for both GitHub and music service access
• Data Storage: Local storage for user preferences with optional cloud sync
• Analytics: Anonymous usage tracking to improve mood-matching algorithms

MVP Scope

• Simple user interface with GitHub handle input field
• Basic sentiment analysis of public commit messages
• Matching algorithm for Beyoncé songs based on detected mood
• Basic music playback functionality
• Ability to save favorite GitHub handles
• Minimal sharing capabilities

This MVP approach allows for quick validation of the core concept while establishing the foundation for future enhancements based on user feedback and engagement metrics.

Implementation Requirements

Core Features

1. GitHub API Integration

   • Fetch user commit history
   • Handle rate limiting and authentication
   • Support for public repositories initially

2. Sentiment Analysis Engine

   • Process commit messages for emotional sentiment
   • Categorize mood into predefined categories (Empowered, Confident, Focused, Reflective, Intense)
   • Handle technical language and common commit patterns

3. Beyoncé Song Database

   • Curated catalog of Beyoncé songs categorized by mood
   • Matching algorithm to select appropriate songs
   • Randomization to avoid repetitive recommendations

4. Music Integration

   • Spotify API integration for playback
   • OAuth authentication flow
   • Handle premium vs free account limitations

5. User Interface

   • SwiftUI-based iOS app
   • Clean, intuitive design
   • Loading states and error handling

Acceptance Criteria

• User can input any valid GitHub username
• App fetches and analyzes last 50 commits successfully
• Sentiment analysis produces consistent mood categorization
• 5 relevant Beyoncé songs are recommended based on commit mood
• Songs can be played through Spotify integration
• User can save favorite GitHub handles
• Basic sharing functionality works
• App handles errors gracefully (invalid usernames, network issues, etc.)

Technical Considerations

• GitHub API rate limiting (60 requests/hour unauthenticated, 5000/hour authenticated)
• Spotify API terms of service compliance
• iOS App Store guidelines compliance
• User privacy and data protection
• Performance optimization for sentiment analysis

Success Metrics

• Successful analysis completion rate (>90%)
• User retention after first use (>40%)
• Spotify authentication conversion (>60%)
• Social sharing engagement rate (>20%)

Dependencies

• GitHub Developer API access
• Spotify Developer API access
• iOS Developer account
• NLP library for sentiment analysis (VADER or similar)

Risks and Mitigation

• API Rate Limiting: Implement caching and efficient data fetching
• Sentiment Analysis Accuracy: Collect user feedback for continuous improvement
• Music Rights: Rely on Spotify API for legal playback compliance
• User Adoption: Focus on viral sharing features and developer community engagement