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 wanshuiyin · Agent Tool · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ARIS-Movie-Director safe to install? View the security audit →
ARIS-Movie-Director 🌐 中文 README · English Hand a fuzzy story to your agent, wake up to a cross-model-audited movie 🎬 — no forgotten facts, no frame signing off on itself. 🎞️ Image-based today, video next. 🤖 Agentic by design — planning · gating · cross-model review on the agent; rendering on the diffusion model. 📚 Jump to — ▶ Watch the movie · ⚡ Quick Start · 🔄 Workflows · 📝 Make your own · 🧩 Layout · 💬 Community · 📖 Cite · 🤝 Contributing · · · · [](http
| Stars | 60 |
| Forks | 3 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 60.468339687196/100 |
| Last Updated | 2026-09-09 |
| Created | 2026-06-09 |
| Platforms | claude-code, python |
| Est. Tokens | ~20k |
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ARIS-Movie-Director is Agentic, long-horizon visual generation: a fuzzy story → a cross-model-audited image-based movie. Brings ARIS's research-wiki + multi-agent debate to multimodal generation (intelligence lives in the a. It is categorized as a Agent Tool with 60 GitHub stars.
ARIS-Movie-Director is primarily written in Python. It covers topics such as agent-skills, ai-agents, ai-filmmaking.
You can find installation instructions and usage details in the ARIS-Movie-Director GitHub repository at github.com/wanshuiyin/ARIS-Movie-Director. The project has 60 stars and 3 forks, indicating an active community.
ARIS-Movie-Director is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ARIS-Movie-Director on Agent Skills Hub include claudepro-directory, c4-genai-suite, repopack-py. 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: