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 SamurAIGPT · Agent Tool · ★ 815
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Text-To-Video-AI safe to install? View the security audit →
Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
Text To Video AI Generate engaging videos from text prompts using AI. Perfect for creating YouTube Shorts, Instagram Reels, TikTok videos, and more. Want to skip the setup? Use our Premium API to generate videos instantly - no installation required, production-ready, and scales with your needs. Demo https://github.com/user-attachments/assets/1e440ace-8560-4e12-850e-c532740711e7 Sample Videos Here are sample videos generated end-to-end directly by this project using the React/Remotion composition engine: "Wonders of Outer Space" — An educational video about the cosmos, galaxies, and black holes. Generated using local E
| Stars | 815 |
| Forks | 315 |
| Language | Jupyter Notebook |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 77.7526774842493/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-24 |
| Created | 2024-06-06 |
| Est. Tokens | ~18k |
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Text-To-Video-AI is Generate video from text using AI. It is categorized as a Agent Tool with 815 GitHub stars.
Text-To-Video-AI is primarily written in Jupyter Notebook. It covers topics such as ai-api, ai-tools, ai-video.
You can find installation instructions and usage details in the Text-To-Video-AI GitHub repository at github.com/SamurAIGPT/Text-To-Video-AI. The project has 815 stars and 315 forks, indicating an active community.
Text-To-Video-AI is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Text-To-Video-AI on Agent Skills Hub include awesome-generative-ai-apps, sdk, awesome-seedance-2.5-prompts-skills. 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: