by naveen-annam · Claude Skill · ★ 83
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Creativly.ai Brand Video — Built with Remotion + Claude Code This project demonstrates what's possible when you combine Claude Code (Anthropic's AI coding agent) with Remotion (React-based video rendering). Every scene, transition, component, and animation was built through iterative AI-assisted development — from V1 to V26 across multiple sessions. Watch the full video If you're looking for a real-world example of: Building complex Remotion videos with AI assistance Advanced Remotion animation patterns and transitions Frame-driven animation architecture (no CSS transitions) Glass morphism, kinetic typography, and cinematic UI recreation in React ...this is it. Built from scratch across 26 versions using Claude Code with the Remotion Best Practices skill. Getting Started Prerequisites Node.js 18+ pnpm (or npm/yarn) Install Preview Opens Remotion Studio at where you can scrub thr
| Stars | 83 |
| Forks | 24 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 65.615207530689/100 |
| Open Issues | 1 |
| Last Updated | 2026-02-21 |
| Created | 2026-02-21 |
| Platforms | claude-code, node |
| Est. Tokens | ~16k |
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creativly.ai-brand-video-remotion is Creativly.ai brand video — 17 cinematic scenes, 13 transitions, 12,000+ lines of React. Built with Remotion + Claude Code + Remotion Best Practices Skill.. It is categorized as a Claude Skill with 83 GitHub stars.
creativly.ai-brand-video-remotion is primarily written in TypeScript. It covers topics such as animation, brand-video, claude-code.
You can find installation instructions and usage details in the creativly.ai-brand-video-remotion GitHub repository at github.com/naveen-annam/creativly.ai-brand-video-remotion. The project has 83 stars and 24 forks, indicating an active community.
creativly.ai-brand-video-remotion is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to creativly.ai-brand-video-remotion on Agent Skills Hub include claude-remotion-kickstart, lovcode, orchestkit. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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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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