GDACollab/Well-Witches
Feb 2025 – Apr 2025
2 Months active
Languages Used
C#UnityUnity Meta
Technical Skills
AI ProgrammingEnemy AIGame DevelopmentObject PoolingState MachinesUnity
ConnorYH
PROFILE
Connoryh
Connor Y. Hua contributed to the GDACollab/Well-Witches repository by developing a new Tank Enemy AI featuring patrol and attack behaviors, including an acid pool trail mechanic that enhances gameplay strategy. He implemented these features using C# and Unity, integrating new prefabs and updating scene and state management scripts for maintainability and asset reuse. Connor also refined melee enemy collision logic and overhauled the tagging system to ensure accurate targeting and reduce erroneous interactions. His work addressed both feature delivery and bug resolution, demonstrating depth in AI programming, physics engine integration, and disciplined version control practices over a focused two-month period.
Overall Statistics
Feature vs Bugs
40%Features
Repository Contributions
6Total
Bugs
3
Commits
6
Features
2
Lines of code
3,045
Activity Months2
Work History
April 2025
4 Commits • 1 Features
Apr 1, 2025April 2025 — GDACollab/Well-Witches: Focused on refining melee combat interactions, player tagging, and target logic to improve gameplay fidelity and maintainability. Key feature attempt: added melee enemy collision via BoxCollider2D on the Melee Enemy prefab with a dedicated test scene to validate interactions; this change was reverted in the same cycle to revert unintended side effects and simplify integration. Tagging and targeting cleanup across Gatherer, Warden, and player sprites to ensure a single source of truth for the Player tag and reduce erroneous interactions. These efforts reduced friction for QA, clarified tagging rules, and prepared the codebase for stable physics-based enhancements in future iterations. Technologies/skills demonstrated include Unity 2D physics (BoxCollider2D), prefab-level changes, tag management, test scene creation, and disciplined commit hygiene.
4 Commits • 1 Features
Apr 1, 2025April 2025 — GDACollab/Well-Witches: Focused on refining melee combat interactions, player tagging, and target logic to improve gameplay fidelity and maintainability. Key feature attempt: added melee enemy collision via BoxCollider2D on the Melee Enemy prefab with a dedicated test scene to validate interactions; this change was reverted in the same cycle to revert unintended side effects and simplify integration. Tagging and targeting cleanup across Gatherer, Warden, and player sprites to ensure a single source of truth for the Player tag and reduce erroneous interactions. These efforts reduced friction for QA, clarified tagging rules, and prepared the codebase for stable physics-based enhancements in future iterations. Technologies/skills demonstrated include Unity 2D physics (BoxCollider2D), prefab-level changes, tag management, test scene creation, and disciplined commit hygiene.
April 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for GDACollab/Well-Witches. Delivered a new Tank Enemy AI with patrol and attack behaviors, including an acid pool trail, plus new prefabs for acid pool and tank enemy. Updated scene and state management scripts to support the new enemy. Resolved pre-merge bugs to ensure stable integration. This work adds strategic enemy variety, enhances player risk-reward, and demonstrates end-to-end feature delivery from AI behavior to asset integration, with maintainable code organization and asset reuse.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for GDACollab/Well-Witches. Delivered a new Tank Enemy AI with patrol and attack behaviors, including an acid pool trail, plus new prefabs for acid pool and tank enemy. Updated scene and state management scripts to support the new enemy. Resolved pre-merge bugs to ensure stable integration. This work adds strategic enemy variety, enhances player risk-reward, and demonstrates end-to-end feature delivery from AI behavior to asset integration, with maintainable code organization and asset reuse.
Activity
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Quality Metrics
Correctness81.8%
Maintainability83.4%
Architecture76.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C#UnityUnity Meta
Technical Skills
AI ProgrammingEnemy AIGame DevelopmentObject PoolingPhysics EnginePrefab ManagementState MachinesTagging SystemUnityUnity EngineVersion Control
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