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 AKMessi · Agent Tool · ★ 85
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is vex safe to install? View the security audit →
Vex Terminal-first AI video editing, driven by plain English. Vex is a source-available, non-commercial AI video editing agent for the terminal. Launch , talk to it in plain English, point it at a video file, and it edits a safe working copy of your footage using FFmpeg, MoviePy, transcript intelligence, generated visuals, and an LLM-driven tool loop. It is built for creators and builders who want CLI speed without memorizing editing commands, FFmpeg flags, subtitle workflows, color-correction knobs, or export presets. Highlights | "Auto color grade this properly
| Stars | 85 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 53.8165494307233/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-23 |
| Created | 2026-03-09 |
| Platforms | claude-code, python |
| Est. Tokens | ~25k |
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vex is claude code for video editing. It is categorized as a Agent Tool with 85 GitHub stars.
vex is primarily written in Python. It covers topics such as agentic-harness, ai-agent, video.
You can find installation instructions and usage details in the vex GitHub repository at github.com/AKMessi/vex. The project has 85 stars and 12 forks, indicating an active community.
The top alternatives to vex on Agent Skills Hub include cutcli-cookbook, wonda, mcp-video. 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: