by wiltonesten-web · Codex Skill · ★ 51
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codeximage-to-editable-ppt-v1 简体中文 | English 这是一个 Codex Skill，用于将图片型 PowerPoint 演示文稿和幻灯片截图转换为可编辑的 PPTX 文件。 本 Skill 面向栅格化幻灯片，包括整页截图、仅包含图片的 PPT/PPTX、扫描幻灯片， 以及大部分可见内容并非 PowerPoint 原生对象的演示文稿。它可以将幻灯片图片拆分为视觉元素， 导出检查产物，并重建经过精细调整的可编辑 PowerPoint；在适合的情况下，文字框和简单结构会转换为 PowerPoint 原生可编辑对象，而复杂科学图示可以保留为独立 PNG 元素以维持视觉保真度。 V1 与 V2 如何选择 V1：节省 Token 版本。 复杂图片以原图裁剪和复用为主，重点将文字与简单结构转换为可编辑对象， 能以较低的 Token 和时间成本完成可编辑化，适合一般组会汇报、内部交流和快速整理。 V2：精细版本。 对视觉质量和结构拆分要求更高；复杂图片会按需调用 进行修复、清理背景， 必要时甚至重绘相关视觉元素。它适合正式答辩、对外汇报和高保真交付，但通常需要更多 Token 和处理时间。 这是教学视频：点击前往抖音观看 案例展示 案例 1：高效神经进化势 本案例从一张扁平化幻灯片图片开始，将其重建为包含多个可独立选择对象的 PowerPoint 页面。 可识别文字和简单演示结构可以编辑；为了保持视觉保真度，复杂科学图示可以继续作为独立 PNG 元素存在。 原始扁平化幻灯片 可编辑 PowerPoint 结果 结果图中的选择框和旋转手柄表明，该页面由多个独立 PowerPoint 对象组成， 而不是一张覆盖整页的截图。 案例 2：精度、效率与尺度 该结构密集型幻灯片包含可编辑中文文字、编号内容面板、图标、分隔线和复杂科学图示。 精细重建结果在保持原始版式视觉一致性的同时，使文字、视觉元素和结构对象可以独立选择并继续编辑。 原始扁平化幻灯片 <th wi
pip install -r skills/codeximage-to-editable-ppt-v1/requirements.txtTaken from the project's README on GitHub. Check the security audit before running it.
Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 51 |
| Forks | 5 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 57.4795756475969/100 |
| Last Updated | 2026-09-09 |
| Created | 2026-07-07 |
| Platforms | codex, python |
| Est. Tokens | ~14k |
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codeximage-to-editable-ppt-v1 is 将基于图片的 PPT/PPTX 幻灯片重建为可编辑的 PPT演示文稿，分v1节省token版和v2高精度版。本skill属于v1节省token版. It is categorized as a Codex Skill with 51 GitHub stars.
codeximage-to-editable-ppt-v1 is primarily written in Python.
The README on GitHub (github.com/wiltonesten-web/codeximage-to-editable-ppt-v1) gives: pip install -r skills/codeximage-to-editable-ppt-v1/requirements.txt. Usage details are in the same README.
codeximage-to-editable-ppt-v1 is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codeximage-to-editable-ppt-v1 on Agent Skills Hub include 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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