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 Zane-0x5a · Claude Skill · ★ 33
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is remotion-director safe to install? View the security audit →
A Claude Code plugin that designs and builds a finished motion piece from a one-line brief — via the 甲乙环 (critic-loop) pipeline, judged on its real rendered frames.
| Stars | 33 |
| Forks | 7 |
| Language | JavaScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 59.9969231671083/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-25 |
| Created | 2026-06-17 |
| Platforms | claude-code, node |
| Est. Tokens | ~18k |
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remotion-director is A Claude Code plugin that designs and builds a finished motion piece from a one-line brief — via the 甲乙环 (critic-loop) pipeline, judged on its real rendered frames.. It is categorized as a Claude Skill with 33 GitHub stars.
remotion-director is primarily written in JavaScript. It covers topics such as ai-design, claude-code, generative-design.
You can find installation instructions and usage details in the remotion-director GitHub repository at github.com/Zane-0x5a/remotion-director. The project has 33 stars and 7 forks, indicating an active community.
remotion-director is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to remotion-director on Agent Skills Hub include wonda, AIEV, Monet. 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: