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by wshuyi · Claude Skill · ★ 360
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Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
remotion-video 中文文档 A Claude Code Skill for creating programmatic videos with Remotion framework. Features Create videos programmatically with React components AI-powered TTS audio generation (MiniMax or Edge TTS) Scene-based architecture with automatic timing Support for animations, subtitles, and music visualization Installation For Claude Code Users Copy this entire folder to your Claude Code skills directory: Then restart Claude Code or start a new session. TTS Setup (Choose One) Option A: Edge TTS (Free, Recommended for Quick Start) No setup required! Edge TTS is free and works out of the box. Just install the Python dependency: Option B: MiniMax TTS (Paid, Voice Cloning Support) Get your API key from MiniMax Platform Set environment variables: To get a Voice ID: Use MiniMax's built-in voices, or Clone your own voice on their platform Usage After installation, trigger the skill by saying: "用代码做视频" "编程视频" "Remotion" "/remotion-video" Example Prompts Tutorial Video: 帮我做一个讲解 Python 装饰器的教程视频，5分钟左右 Data Visualization: 用 Remotion 做一个展示2024年销售数据的动画视频 Music Visualization: 帮我做一个音乐可视化视频，配合这首歌的节奏
| Stars | 360 |
| Forks | 62 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 70.4762268739786/100 |
| Open Issues | 2 |
| Last Updated | 2026-01-25 |
| Created | 2026-01-25 |
| Platforms | claude-code, node, python |
| Est. Tokens | ~2k |
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remotion-video-skill is A Claude Code Skill for creating programmatic videos with Remotion framework. It is categorized as a Claude Skill with 360 GitHub stars.
remotion-video-skill is primarily written in Python.
You can find installation instructions and usage details in the remotion-video-skill GitHub repository at github.com/wshuyi/remotion-video-skill. The project has 360 stars and 62 forks, indicating an active community.
The top alternatives to remotion-video-skill 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.
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