# Product Context

## Project Overview
ChessPrism is an advanced chess analysis tool that enhances the online chess experience by providing real-time analysis and move suggestions. It works by capturing and analyzing the chess board from popular chess websites and platforms.

## Core Problems Solved

### Chess Analysis Accessibility
- Makes professional-level chess analysis accessible during online play
- Provides real-time insights without manual position input
- Integrates seamlessly with existing chess platforms

### Visual Recognition
- Accurately detects chess board from screen content
- Recognizes board coordinates and boundaries
- Handles various board themes and orientations
- Maintains accuracy during game play
- Provides clean board snapshots for analysis

### Real-time Processing
- Captures and processes screen content in real-time
- Provides immediate feedback and analysis
- Maintains performance during long sessions
- Supports manual snapshot capture for detailed analysis

## User Experience Goals

### Seamless Integration
1. Non-intrusive Operation
   - Works with Chess.com desktop app
   - Minimal setup requirements
   - Automatic board detection and tracking
   - Clean snapshot capture without cursor interference

2. Intuitive Interface
   - Clear visualization of analysis
   - Easy-to-understand suggestions
   - Minimal user intervention required
   - Visual feedback for capture states
   - Manual snapshot control

### Reliable Detection
1. Board Recognition
   - Two-phase detection strategy:
     * Pattern recognition for known interfaces
     * Coordinate-based fallback for reliability
   - Proper coordinate system handling
   - Consistent board capture across sessions
   - High-quality snapshots for analysis

2. Position Analysis
   - Accurate piece recognition (planned)
   - Current position evaluation (planned)
   - Move suggestion visualization (planned)

## Target Users

### Chess Players
- Amateur to intermediate players
- Chess.com desktop app users
- Players seeking to improve

### Use Cases
1. Learning
   - Understanding position evaluation
   - Learning from mistakes
   - Exploring alternative moves
   - Analyzing specific positions via snapshots

2. Analysis
   - Real-time position assessment
   - Move validation
   - Strategic planning
   - Detailed position study

## Product Requirements

### Essential Features
1. Board Detection (Current Focus)
   - Accurate boundary recognition
   - Full board capture
   - Support for Chess.com desktop app
   - Reliable coordinate transformations
   - Clean snapshot capability

2. Position Analysis (Planned)
   - Real-time evaluation
   - Move suggestions
   - Tactical opportunities

3. User Interface
   - Analysis overlay
   - Control panel
   - Settings management
   - Snapshot controls
   - Visual status indicators

### Quality Standards
1. Accuracy
   - Reliable board detection
   - Complete board capture
   - Precise coordinate handling
   - Clean snapshots without artifacts

2. Performance
   - Real-time processing
   - Minimal resource usage
   - Stable operation
   - Efficient snapshot handling

3. Usability
   - Intuitive controls
   - Clear feedback
   - Minimal setup
   - Simple snapshot workflow

## Success Metrics

### Technical Metrics
- Board detection accuracy rate
- Full board capture success rate
- Processing speed per frame
- Error recovery rate
- Snapshot quality assessment

### User Metrics
- Setup success rate
- Analysis accuracy
- User engagement time
- Feature utilization
- Snapshot usage patterns

## Current Challenges

### Board Detection
1. Coordinate Systems
   - Vision framework (bottom-left origin)
   - NSImage/CGImage (bottom-left origin)
   - SwiftUI (top-left origin)
   - Proper transformations between systems

2. Detection Accuracy
   - Full board capture
   - Consistent positioning
   - Reliable boundaries
   - Clean snapshots

### Next Steps
1. Refine board detection
   - Improve coordinate handling
   - Ensure full board capture
   - Validate transformations
   - Optimize snapshot quality

2. Move to position analysis
   - Piece recognition
   - Position evaluation
   - Move suggestions

## Future Enhancements

### Planned Features
1. Advanced Analysis
   - Deep position evaluation
   - Opening recognition
   - Endgame tablebases
   - Position comparison from snapshots

2. Learning Tools
   - Mistake analysis
   - Improvement suggestions
   - Progress tracking
   - Position database from snapshots

3. Customization
   - Analysis depth control
   - Visual preference settings
   - Platform-specific optimizations
   - Snapshot management options

## Product Roadmap

### Current Phase
- Core board detection system
- Coordinate system handling
- Basic user interface
- Manual snapshot system

### Next Phase
- Position analysis
- Move suggestion system
- Visual overlay implementation
- Enhanced snapshot analysis

### Future Phase
- Advanced analysis features
- Learning tools integration
- Customization options
- Snapshot database and comparison tools