Add mobile app support and opening training feature

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>

public/openings/README.md

@@ -0,0 +1,64 @@
+# Opening Database
+
+This directory contains the ECO (Encyclopedia of Chess Openings) database files.
+
+## Required Files
+
+The following files are required for opening training:
+
+- `ecoA.json` - ECO codes A00-A99
+- `ecoB.json` - ECO codes B00-B99
+- `ecoC.json` - ECO codes C00-C99
+- `ecoD.json` - ECO codes D00-D99
+- `ecoE.json` - ECO codes E00-E99
+- `moveIndex.json` - Generated move sequence index
+
+## File Format
+
+Each ECO file (ecoA-E.json) should be a JSON object mapping FEN positions to opening metadata:
+
+```json
+{
+  "fen_position": {
+    "eco": "A00",
+    "name": "Opening Name",
+    "moves": "e4 e5 Nf3 Nc6",
+    "wikipediaSlug": "opening-name" // optional
+  }
+}
+```
+
+## Setup
+
+### Option 1: Docker (Automatic)
+
+When running via Docker, these files should be provided as a volume mount:
+
+```bash
+docker run -v ./openings:/app/public/openings ghcr.io/stefan-kp/chess-tutor
+```
+
+### Option 2: Local Development
+
+1. Obtain ECO database files (ecoA-E.json)
+2. Place them in this directory
+3. Generate the move index:
+
+```bash
+npm run build:opening-index
+```
+
+This will create `moveIndex.json` from the ECO files.
+
+### Option 3: Generate from PGN
+
+If you have a PGN database, you can extract ECO codes using chess tools like:
+- `pgn-extract`
+- Custom scripts
+
+## Notes
+
+- These files are **not included in git** (too large, ~4MB total)
+- Users must provide their own opening database
+- Wikipedia integration is optional (see `public/wikipedia/README.md`)
+- The move index is automatically generated during Docker startup