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 knight6669 · Codex Skill · ★ 126
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is knight-imagetopptx-skill safe to install? View the security audit →
Knight ImageToPPTX Skill 把图片、图片型 PPTX、PDF 页面复刻成对象级可编辑的 PowerPoint。 Semantic slide rebuilding protocol for Codex · native PPT objects · generated transparent assets · page-by-page visual QA Skill 定位 是一个面向 Codex 的 图片页转可编辑 PPTX 复刻技能。 它不是“从零生成一份 PPT”的创作模板，也不是“把图片塞进幻灯片”的打包工具。它给 Codex 提供一套可复用的复刻协议：先拆解现有视觉页，再把文本、表格、卡片、箭头、按钮、图标和复杂视觉资产重建为可继续编辑的 PowerPoint 对象。 适合用在这些任务中： 将 PNG / JPG / WebP 幻灯片截图复刻为可编辑 PPTX 将图片版 PPTX 按页重建为文字、表格、图标可编辑的 PPTX 将 PDF 汇报页、扫描页或图片型 PDF 复刻为 PowerPoint 复刻中文商务汇报页、政务信息图、流程图、仪表盘和高密度 UI 截图 对图标裁切、CJK 字体、局部渲染差异和复杂资产进行强约束 QA 不适合用在这些任务中： 从空白内容创作新 deck 只需要整页截图作为背景展示 SVG-only 转换或纯矢量图标库建设 不需要对象级编辑能力的简单归档 效果展示 | <img src="assets/refer
| Stars | 126 |
| Forks | 17 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 59.2515737680474/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-24 |
| Created | 2026-05-09 |
| Platforms | codex, python |
| Est. Tokens | ~15k |
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开源了Codex Skills等集合，大部分是自己实际需要搞得，需要的自取。欢迎star
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knight-imagetopptx-skill is Semantic slide image to editable PPTX Codex skill. It is categorized as a Codex Skill with 126 GitHub stars.
knight-imagetopptx-skill is primarily written in Python.
You can find installation instructions and usage details in the knight-imagetopptx-skill GitHub repository at github.com/knight6669/knight-imagetopptx-skill. The project has 126 stars and 17 forks, indicating an active community.
knight-imagetopptx-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to knight-imagetopptx-skill on Agent Skills Hub include skills, 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.
Sources & who's responsible: