MindFlowInteractive/quest-service

AI Summary: This issue outlines the development of a comprehensive puzzle editor designed to empower content creators and the community to build engaging puzzles. Key features include an intuitive drag-and-drop interface, integrated validation and preview tools, template systems, and robust capabilities for sharing, collaboration, version control, and community moderation. The goal is to facilitate efficient creation of complex puzzles that seamlessly integrate with the game engine.

AI Summary: This issue outlines the creation of a comprehensive, application-wide input validation and error handling framework. It involves implementing `class-validator` with custom decorators, global exception filters, and custom exception classes to ensure consistent error responses and prevent security vulnerabilities. Key tasks include request sanitization, file upload validation, and setting up error tracking, all while providing user-friendly and secure handling of all application inputs.

AI Summary: Design and implement the core game engine architecture for logic puzzle mechanics, including abstract puzzle classes, state management, cause-and-effect engine, validation/solution checking, dynamic sequence generation, difficulty scaling, puzzle progression/unlocking, save/load functionality, hint system, and analytics tracking. The engine must support multiple puzzle types, ensure consistent state management, accurate validation, adaptive difficulty, and persistent game state.

AI Summary: Implement a multi-layer caching strategy using Redis to improve the performance of the quest-service application. This involves setting up Redis, creating a cache service with get/set/delete operations, implementing caching decorators, handling database query caching and session caching, defining invalidation strategies, warming the cache, monitoring performance, configuring for different environments, and establishing backup/recovery procedures.

AI Summary: Implement a comprehensive logging and monitoring system for the quest-service application, including Winston logger configuration with multiple transports, structured logging with correlation IDs, request/response logging middleware, error tracking and alerting, performance monitoring and metrics collection, log rotation and retention, health check endpoints, a monitoring dashboard, automated alerting, and log aggregation for production. The system must ensure all application events are logged, errors are tracked and trigger alerts, performance metrics are collected and visualized, health checks are accurate, and log management doesn't impact application performance.

AI Summary: Implement comprehensive API documentation for the quest-service using Swagger/OpenAPI and NestJS. This includes installing and configuring the necessary packages, setting up Swagger UI with custom styling, creating documentation for all endpoints (including authentication and versioning), generating schemas from DTOs, adding example requests and responses, configuring deployment, and integrating API testing. The documentation should be accessible, functional, and maintainable.

AI Summary: Implement a robust JWT-based authentication and authorization system for the quest-service project, including user registration, password management, role-based access control, OAuth2 integration (optional), rate limiting, and session management. This involves creating user entities, implementing JWT strategies, setting up password hashing, creating authorization guards and decorators, and handling password resets and token blacklisting.

AI Summary: Develop a comprehensive leaderboard system for the quest-service project, including data structures, ranking algorithms (time, score, efficiency), real-time updates, various categories and time periods, rank calculation with tie-breaking, privacy controls, achievement integration, reset/archival, analytics, and social features. The system must accurately reflect player performance, handle high-volume updates efficiently, and promote healthy competition.

AI Summary: Implement a robust game session management system for the quest-service project, including session creation, lifecycle management, state persistence, recovery, timeout handling, analytics, multi-device synchronization, sharing/spectator modes, puzzle recommendations, and export/replay functionality. The system must ensure seamless game resumption, reliable multi-device synchronization, and minimal performance impact.

AI Summary: Design and implement a dynamic difficulty scaling system for puzzles within a game, encompassing player skill assessment, adaptive difficulty adjustment, optimization, prediction modeling, A/B testing, feedback collection, analytics, reporting, and content recommendation. The system must maintain optimal challenge and support diverse player skill levels and preferences.

AI Summary: Develop a comprehensive achievement system with diverse unlock conditions (progress, skills, events), rewards (rarity, special items), notifications, tracking, social sharing, retroactive unlocking, analytics, and content unlocking features. The system must be flexible and easily extensible for future achievements.

AI Summary: Implement a core logic puzzle game engine supporting multiple puzzle types, including features like cause-and-effect processing, state transition validation, puzzle generation, solution verification, hint generation, difficulty adjustment, randomization, undo/redo, timing, scoring, and rewards. The engine must have a consistent interface for different puzzle types and ensure accurate and efficient solution verification.

AI Summary: Implement a comprehensive user progress tracking system, including achievements, statistics, leaderboards, streak tracking, progress visualization, puzzle recommendations, and backup/restore functionality. The system must accurately track progress across all game aspects without impacting performance.

AI Summary: Develop a comprehensive puzzle management system including puzzle creation, categorization, difficulty rating, versioning, search, analytics, sharing, export/import, and moderation features. The system should handle puzzle metadata and ensure data integrity through validation and constraints.

AI Summary: Design and implement a comprehensive database schema for a game, including entities for users, puzzles, progress, achievements, and game sessions. This involves defining relationships between entities, creating migration scripts, optimizing database performance with indexes, and setting up constraints and validation rules. Seed data for initial puzzles and achievements should also be implemented.

AI Summary: Set up and configure a PostgreSQL database for the quest-service project using TypeORM, including Docker containerization, connection pooling, migrations, logging, health checks, testing, backups, and performance monitoring.

AI Summary: Set up a new NestJS project with a modular architecture for a puzzle game, including user management, puzzle handling, achievement tracking, and game logic. This involves configuring environment variables, setting up linting and formatting tools, implementing logging and security middleware, and creating a comprehensive README.