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 dlazy-ai · Codex Skill · ★ 14
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-product-video safe to install? View the security audit →
AI agent skill (Claude Code / Codex) that turns your product into a cinematic promo video: 152 shot recipe cards, 209 style variants, tuned Remotion implementations, 149 SFX, a validated 10-shot template, plus music, voiceover, captions and B-roll via the dLazy CLI.
| Stars | 14 |
| Forks | 0 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 59.8359126492103/100 |
| Last Updated | 2026-08-31 |
| Created | 2026-08-17 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~16k |
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ai-product-video is AI agent skill (Claude Code / Codex) that turns your product into a cinematic promo video: 152 shot recipe cards, 209 style variants, tuned Remotion implementations, 149 SFX, a validated 10-shot templ. It is categorized as a Codex Skill with 14 GitHub stars.
ai-product-video is primarily written in TypeScript. It covers topics such as agent-skill, ai-agent, animation.
You can find installation instructions and usage details in the ai-product-video GitHub repository at github.com/dlazy-ai/ai-product-video. The project has 14 stars and 0 forks, indicating an active community.
ai-product-video is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ai-product-video on Agent Skills Hub include deterministic-agent-control-protocol, three.ws, kaggle-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: