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 ZLHad · Codex Skill · ★ 14
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenVideoHarness safe to install? View the security audit →
A video harness work platform for Agents such as Claude or Codex: the agent writes the code that renders the film, checks its own frames, storyboard and cut. 给 Claude Code 和 Codex 用的视频Harness工作台：agent 写代码渲染成片、自己检查分镜，立意、成片由你拍板；众多风格预设、引擎丰富，配音配乐也用代码做。
| Stars | 14 |
| Forks | 1 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.6358679599113/100 |
| Last Updated | 2026-10-12 |
| Created | 2026-09-28 |
| Platforms | claude-code, codex, gemini, python |
| Est. Tokens | ~23k |
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OpenVideoHarness is A video harness work platform for Agents such as Claude or Codex: the agent writes the code that renders the film, checks its own frames, storyboard and cut. 给 Claude Code 和 Codex 用的视频Harness工作台：agen. It is categorized as a Codex Skill with 14 GitHub stars.
OpenVideoHarness is primarily written in Python. It covers topics such as agent-skills, ai-video, claude-code.
You can find installation instructions and usage details in the OpenVideoHarness GitHub repository at github.com/ZLHad/OpenVideoHarness. The project has 14 stars and 1 forks, indicating an active community.
OpenVideoHarness is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenVideoHarness on Agent Skills Hub include hypernovum, mcp-video, ai-agent-session-center. 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: