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 jnMetaCode · Claude Skill · ★ 433
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-shortfilm-prompts safe to install? View the security audit →
ai-shortfilm-prompts ) ═══ -- ▶ 15s demo — generated from a prompt this repo's Skill wrote (prompt) -- The complete methodology + prompt library + Claude Code Skill behind Zombie Scavenger by Mx-Shell — the AI short that Hollywood director PJ Ace called "one of the best short fi
| Stars | 433 |
| Forks | 82 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 68.9749762762628/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-21 |
| Created | 2026-05-18 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
Looking for a ai-shortfilm-prompts alternative? If you're comparing ai-shortfilm-prompts with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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ai-shortfilm-prompts is Claude Code Skill that turns any idea into a cinematic, model-ready video prompt — Sora · Kling · Veo · Seedance. 21 genre templates, 5-stage structure, eval-tested. Distilled from the AI short Hollyw. It is categorized as a Claude Skill with 433 GitHub stars.
ai-shortfilm-prompts is primarily written in Python. It covers topics such as ai-video, chinese, cinematic.
You can find installation instructions and usage details in the ai-shortfilm-prompts GitHub repository at github.com/jnMetaCode/ai-shortfilm-prompts. The project has 433 stars and 82 forks, indicating an active community.
ai-shortfilm-prompts is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-shortfilm-prompts on Agent Skills Hub include lanshu-awesome-ai-video-kit, ai-media-generator, higgsfield-ai-prompt-skill. 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: