No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by darksider-9 · Codex Skill · ★ 119
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is master-thesis-studio-skill safe to install? View the security audit →
Master Thesis Studio Skill 面向东南大学的中文硕士论文写作与 Word 自动生成的 Codex Skill，默认适配东南大学风格硕士论文 Word 模板。它把论文创作流程拆成两层：先通过对话确认题目、章节、资产和写作边界，再通过内置 Python 脚本把 Markdown 内容安全写入 Word 模板，生成新的 文件，打开时出现任何弹窗都点击是即可。 模板来源 模板修改自https://seuthesis-word.github.io/ ，目前在图表目录和封面上尚未完全完成，请勿直接使用模板带封面的word，修改后的模板文件已存放在examples/中，可以在完成后再额外添加封面等摘要之前的内容。 主要能力 初始化论文项目工作区，创建章节、图表、代码、数据、参考文献、状态文件和输出目录。 引导用户确认题目、研究方向、当前完成度、已有资产、章节框架和写作模式。 支持从零生成初稿、边做边写、续写已有论文、根据代码和数据转写论文、格式整理与 Word 输出。 使用 manifest 管理参考文献、图、表、代码、数据、公式和待补材料。 将确认后的 Markdown 写入 Flat OPC XML，再构建新的 Word 文档，不覆盖原始模板。 支持图题、表题、三线表、公式编号、参考文献、交叉引用、页眉页脚和目录字段刷新。 适用场景 只有论文题目或方向，希望先生成一个可修改的完整初稿。 已经写了一部分论文，希望继续补写或重写薄弱章节。 已有代码、数据、实验记录、图表，希望转写成论文方法和实验章节。 已有 Word 模板，希望自动化写入章节、图表、公式和参考文献。 需要基于东南大学风格模板生成安全、可追溯的新 Word 副本。 目录结构 环境要求 -
| Stars | 119 |
| Forks | 7 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 53.480240380066/100 |
| Last Updated | 2026-04-21 |
| Created | 2026-04-20 |
| Platforms | codex, python |
| Est. Tokens | ~9k |
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master-thesis-studio-skill is 面向东南大学的中文硕士论文写作与 Word 自动生成的 Codex Skill. It is categorized as a Codex Skill with 119 GitHub stars.
master-thesis-studio-skill is primarily written in Python.
You can find installation instructions and usage details in the master-thesis-studio-skill GitHub repository at github.com/darksider-9/master-thesis-studio-skill. The project has 119 stars and 7 forks, indicating an active community.
master-thesis-studio-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to master-thesis-studio-skill on Agent Skills Hub include solana-dev-skill, theorist. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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