Open-world missions often rely on repeated formulas, yet designers lack systematic ways to examine pacing, variation, and experiential balance across large portfolios. We introduce the Mission Action Quality Vector (MAQV), a six-dimensional framework-covering combat, exploration, narrative, emotion, problem-solving, and uniqueness-paired with an action block grammar representing missions as gameplay sequences. Using about 2200 missions from 20 AAA titles, we apply LLM-assisted parsing to convert community walkthroughs into structured action sequences and score them with MAQV. An interactive dashboard enables designers to reveal underlying mission formulas. In a mixed-methods study with experienced players and designers, we validate the pipeline's fidelity and the tool's usability, and use thematic analysis to identify recurring design trade-offs, pacing grammars, and systematic differences by quest type and franchise evolution. Our work offers a reproducible analytical workflow, a data-driven visualization tool, and reflective insights to support more balanced, varied mission design at scale.

翻译：开放世界任务常依赖重复性设计公式，但设计师缺乏系统化方法审视大规模任务组合中的节奏感、多样性及体验平衡性。我们提出任务动作质量向量（MAQV），这是一个六维分析框架——涵盖战斗、探索、叙事、情感、问题解决与独特性——并配合动作块语法将任务表征为游戏序列。基于20款3A级游戏中约2200个任务，我们利用大语言模型辅助解析技术将社区攻略转化为结构化动作序列，并通过MAQV进行评分。交互式仪表盘使设计师能够揭示潜在的任务设计公式。在面向资深玩家与设计师的混合方法研究中，我们验证了分析管线的保真度与工具的可用性，通过主题分析归纳出反复出现的设计权衡、节奏语法、以及基于任务类型与系列演变的系统性差异。本研究提供了可复现的分析工作流、数据驱动的可视化工具及反思性见解，以支持大规模、更均衡且多样化的任务设计。