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by mikeoller82 · Agent Tool · ★ 69
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VideoGraphAI 🎬 An open-source AI-powered YouTube Shorts automation tool that revolutionizes content creation using graph-based agents and state-of-the-art AI technologies. Features • Installation • Usage • Contributing • License 📚 Table of Contents 🌟 Overview ✨ Key Features 🔄 Workflow 📋 Prerequisites 🚀 Installation ⚙️ Configuration 📝 Usage 🔧 Troubleshooting 👥 Contributing 🙏 Acknowledgements 📄 License 🌟 Overview VideoGraphAI streamlines the creation of YouTube Shorts using advanced AI technologies. Built with Streamlit, it offers end-to-end video production capabilities from content research to final rendering. The system leverages various AI models and API
| Stars | 69 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 62.5038385287826/100 |
| Open Issues | 1 |
| Last Updated | 2026-02-11 |
| Created | 2024-09-10 |
| Platforms | python |
| Est. Tokens | ~1063k |
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VideoGraphAI is 🎬 AI-powered YouTube Shorts automation tool using LLMs, real-time search, and text-to-speech. Create engaging short-form videos with automated research, voiceovers, and subtitles.. It is categorized as a Agent Tool with 69 GitHub stars.
VideoGraphAI is primarily written in Python. It covers topics such as ai-tools, ai-video-generation, artificial-intelligence.
You can find installation instructions and usage details in the VideoGraphAI GitHub repository at github.com/mikeoller82/VideoGraphAI. The project has 69 stars and 12 forks, indicating an active community.
The top alternatives to VideoGraphAI on Agent Skills Hub include LLM-Tools, bottube, claudepro-directory. 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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