November 2024 performance summary for gmuGADIG/FetchQuest. Focused on feature delivery for Target Practice and code quality improvements to support maintainability and faster future iterations. Key outcomes include the introduction of a new Target Practice Scene tarB903.tmp with multiple targets, pressure plates, and player elements; a refactor of target.gd to improve animation control and ensure the visibility of target markers reflects the current game state; and a follow-up refactor to standardize variable naming and references for long-term maintainability. These changes expand training capabilities, improve player feedback, and reduce future debugging effort. No user-facing regressions observed; commits prioritized correctness and code cleanliness. Technologies demonstrated include Godot Engine, GDScript, scene management, state-driven logic, and maintainability practices. Business value includes expanded training scenarios, clearer gameplay feedback, and faster, safer iteration cycles for future features.