Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by op7418 · Claude Skill · ★ 335
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
🎬 Video Wrapper Add Variety Show Style Visual Effects to Interview/Podcast Videos AI-powered subtitle analysis, automatic effect suggestions, one-click professional visual rendering Quick Start • Features • Demo • Use Cases • Architecture 中文文档 ✨ Features 🎨 8 Visual Components Key Phrases - Short phrases highlighting core ideas Lower Thirds - Display guest name and title Chapter Titles - Topic transition title cards Term Cards - Professional terminology explanations Quote Callouts - Highlight memorable quotes Animated Stats - Dynamic number displays Bullet Points - Core takeaways summary Social Bars - Follow call-to-action 🎭 4 Visual Themes Notion 🟡 - Warm knowledge style Cyberpunk 💜 - Neon futuristic Apple ⚪ - Minimalist business Aurora 🌈 - Gradient flowing 🤖 Smart Workflow 📝 AI analyzes subtitle content 💡 Auto-generates effect suggestions ✅ User approves configuration 🎬
| Stars | 335 |
| Forks | 34 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 67.4543130373343/100 |
| Open Issues | 1 |
| Last Updated | 2026-02-10 |
| Created | 2026-01-28 |
| Platforms | claude-code, python |
| Est. Tokens | ~8k |
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Video-Wrapper-Skills is 为访谈视频添加综艺风格视觉特效的 Claude Skill。AI 分析字幕内容生成建议，用户审批后自动渲染。A Claude Skill for adding variety-show-style visual effects to interview videos. The AI analyzes subtitle content to generate suggestions. It is categorized as a Claude Skill with 335 GitHub stars.
Video-Wrapper-Skills is primarily written in Python.
You can find installation instructions and usage details in the Video-Wrapper-Skills GitHub repository at github.com/op7418/Video-Wrapper-Skills. The project has 335 stars and 34 forks, indicating an active community.
The top alternatives to Video-Wrapper-Skills on Agent Skills Hub include claude-codex-settings, digital-marketing-pro, life-sciences. 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: