This commit implements iOS/Android mobile app support using Capacitor
and adds a comprehensive opening training feature with LLM-powered
explanations.
## Mobile App Infrastructure
- Add Capacitor configuration for iOS/Android builds
- Create mobile build script that excludes API routes
- Update Next.js config for conditional static export
- Add layout components with generateStaticParams for static builds
- Generate 500+ static pages for offline mobile use
## Chess Engine Abstraction
- Create ChessEngine interface for pluggable implementations
- Add LocalEngine (GPL - uses stockfish.js in browser)
- Add RemoteEngine (proprietary - calls API server)
- Factory pattern selects engine based on environment
- Enables GPL compliance for web, proprietary for mobile
## Opening Training Feature
- Interactive opening repertoire training
- Move validation with engine-backed feedback
- LLM explanations using Gemini API
- Wikipedia integration for opening context
- Opening family grouping (e4, d4, c4, etc.)
- Session state management
- Real-time move feedback with evaluation
Components:
- OpeningSelector: Browse and select openings by family
- OpeningTrainer: Main training interface with chessboard
- MoveFeedback: Display move quality and LLM explanations
- WikipediaSummary: Show opening history and context
- ErrorBoundary: Graceful error handling
Services:
- openingLoader: Load and filter opening database
- engineService: Engine evaluation wrapper
- moveValidator: Validate moves against repertoire
- feedbackGenerator: Generate contextual feedback
- wikipediaService: Fetch and cache Wikipedia data
- sessionManager: Track training session state
## Wikipedia Integration
- Automatic Wikipedia article fetching for openings
- Client-side and server-side caching
- Sanitized summaries with proper formatting
- Link opening database to Wikipedia slugs
- API endpoints for on-demand fetching
## Docker Improvements
- Add entrypoint script for automatic data setup
- Fetch Wikipedia data on first container startup
- Generate opening move index automatically
- Remove generated data from git (public/openings/*.json, public/wikipedia/*.json)
- Add READMEs explaining data requirements
- Update .gitignore for generated files
## Dual Licensing Strategy
- Add LICENSING.md explaining dual licensing approach
- GPL-3.0 for web builds (includes Stockfish)
- Proprietary option for mobile builds (no GPL code)
- Single codebase, multiple licensing models
- Legal compliance documented
## API Endpoints
- POST /api/v1/llm/opening-explanation - Get LLM move explanations
- GET /api/v1/wikipedia/summary - Fetch Wikipedia summaries
## Type Updates
- Add openingTraining types
- Update Tutor component to use ChessEngine interface
- Add Gemini error handling types
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Create chessFormatDetector utility for automatic format detection
- Update StartScreen with textarea supporting both FEN and PGN input
- Add real-time format detection with visual feedback indicators
- Update translations for all 4 languages (EN, DE, FR, IT)
- Add markdown rendering for Tutor chat messages
- Add comprehensive tests for format detection (20 tests)
- Update game initialization to handle both FEN and PGN formats
This completes the fix/stale-analysis-data branch with:
- Fixed stale evaluation data in hint/best move requests
- Clarified AI's dual role (opponent + tutor) to prevent hint rejection
- Stored complete evaluation history (P0, P1, P2) for all moves
- Improved end-game analysis with better mistake detection
- Fixed duplicate analysis runs with useRef flag
- Added markdown rendering for formatted analysis output
- Added unified FEN/PGN import with auto-detection
This commit fixes a bug where the AI tutor and end-game analysis would use stale data from previous evaluations.
The `Tutor` component now receives the live `game` instance and has a new function, `evaluateCurrentPosition`, which is called on-demand when a user requests a hint or the best move. This ensures the LLM receives up-to-date information.
The end-game analysis was also corrected to use the proper evaluation data when constructing the move history, preventing incorrect analysis of mistakes and blunders.
The test suite was improved by restoring deleted tests, adding a new test to verify the fix, and making existing tests more robust.
Stefan