GDACollab/Well-Witches
Jan 2025 – Mar 2025
3 Months active
Languages Used
C#UnityUnity C#YAMLUnity Scene
Technical Skills
AI ProgrammingAI PrototypingAsset IntegrationGame DevelopmentPrefab CreationState Machines
Derek Simpson
PROFILE
Derek Simpson
Over three months, Articuno Lies developed scalable enemy and boss AI systems for the GDACollab/Well-Witches repository, focusing on robust state-machine architectures in Unity using C#. They built a modular AIController and refactored enemy behaviors into extensible state machines, enabling rapid iteration and easier testing. Their work included prototyping enemy archetypes, implementing phased boss encounters with multiple attack patterns, and integrating visual feedback for player interactions. By emphasizing prefab creation, scene management, and asset integration, Articuno established a maintainable foundation for future content. The depth of their engineering enabled faster QA cycles, improved gameplay tuning, and supported ongoing expansion of combat features.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
4
Lines of code
9,461
Activity Months3
Your Network
32 people
Work History
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for GDACollab/Well-Witches: Focused on delivering a robust boss phase overhaul and preparing for subsequent content. Key accomplishments include a comprehensive Phase One state machine, new attack indicators, a lunge mechanic, and scene cleanup to accommodate the expanded system; these changes establish reliable attack timing, damage logic, and player feedback while enabling future phases. No major bug fixes recorded this period.
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for GDACollab/Well-Witches: Focused on delivering a robust boss phase overhaul and preparing for subsequent content. Key accomplishments include a comprehensive Phase One state machine, new attack indicators, a lunge mechanic, and scene cleanup to accommodate the expanded system; these changes establish reliable attack timing, damage logic, and player feedback while enabling future phases. No major bug fixes recorded this period.
March 2025
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 performance—GDACollab/Well-Witches delivered a robust AI and combat system overhaul, driving gameplay depth, maintainability, and faster iteration. The month focused on refactoring enemy AI into a scalable state-machine architecture, introducing new enemy archetypes, and building a boss encounter framework with multiple attack patterns and phases. These changes reduce merge conflicts, improve testability, and align with long-term roadmap for extensible enemy design and fight choreography.
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 performance—GDACollab/Well-Witches delivered a robust AI and combat system overhaul, driving gameplay depth, maintainability, and faster iteration. The month focused on refactoring enemy AI into a scalable state-machine architecture, introducing new enemy archetypes, and building a boss encounter framework with multiple attack patterns and phases. These changes reduce merge conflicts, improve testability, and align with long-term roadmap for extensible enemy design and fight choreography.
January 2025
5 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for GDACollab/Well-Witches: Focused on building a scalable AI testing framework using a state-machine architecture to accelerate enemy behavior development and evaluation. Delivered core AIController with State and Transition structures and provided test assets and scene configuration to enable rapid iteration. Implemented placeholder enemy PNG and prefab to bootstrap testing. Prototyped core behaviors with AttackState, PatrolState, and IdleState, establishing a repeatable pattern for adding new states. Completed a stability fix to the AI prototype to improve reliability in testing scenarios. Updated the AIPrototypeScene.unity to align with the new framework and testing workflow. These deliverables enable faster QA cycles, better tuning of enemy behavior, and a foundation for data-driven AI improvements.
5 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for GDACollab/Well-Witches: Focused on building a scalable AI testing framework using a state-machine architecture to accelerate enemy behavior development and evaluation. Delivered core AIController with State and Transition structures and provided test assets and scene configuration to enable rapid iteration. Implemented placeholder enemy PNG and prefab to bootstrap testing. Prototyped core behaviors with AttackState, PatrolState, and IdleState, establishing a repeatable pattern for adding new states. Completed a stability fix to the AI prototype to improve reliability in testing scenarios. Updated the AIPrototypeScene.unity to align with the new framework and testing workflow. These deliverables enable faster QA cycles, better tuning of enemy behavior, and a foundation for data-driven AI improvements.
January 2025
Activity
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Quality Metrics
Correctness82.2%
Maintainability81.6%
Architecture81.0%
Performance73.2%
AI Usage22.0%
Skills & Technologies
Programming Languages
C#UnityUnity C#Unity SceneYAML
Technical Skills
AIAI ProgrammingAI PrototypingAsset IntegrationBoss AIC# ScriptingCombat MechanicsCombat SystemsEnemy AIEnemy AI SetupGame DevelopmentObject-Oriented ProgrammingPrefab CreationScene ManagementState Machine
Repositories Contributed To
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