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 Yuuhann1999 · MCP Server · ★ 297
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codex-storyboard safe to install? View the security audit →
Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
Codex Storyboard / Codex 分镜台 安装插件后，Codex 可以直接启动本地分镜台、创建视频项目、生成图片/视频素材，并自动回填到分镜表。 快速开始 · 使用流程 · 界面展示 · 核心能力 · DESIGN.md · 开发 · 隐私说明 这是什么？ Codex Storyboard 是一个面向短视频和自媒体创作的本地分镜工作台。 你可以直接让 Codex 创建一个完整视频项目：镜头、台词、画面描述、A-ROLL / B-ROLL、时长、素材类型和生成方式都会写进本地分镜表。之后 Codex 还可以继续调用 Image Generation、HyperFrames 或 Remotion 生成图片 / 视频素材，并把结果回填到对应镜头。 可以把它理解成： 普通用户不需要理解 MCP、本地 API 或文件路径。安装插件后，说一句“打开分镜台”或“帮我创建一个分镜项目”就能开始。 快速开始 安装插件 需要： 支持插件的 Codex 本机可用的 Node.js 18 或更高版本 安装后重启 Codex，或新开一个对话，让插件和 MCP 工具重新加载。 ##
| Stars | 297 |
| Forks | 43 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.9643011660801/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-07 |
| Created | 2026-06-23 |
| Platforms | codex, mcp, node |
| Est. Tokens | ~14k |
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codex-storyboard is 本地多项目 Codex 视频分镜工作台，支持图片/视频生成任务、HyperFrames 与 Remotion 自动回填。Local multi-project storyboard workspace for Codex.. It is categorized as a MCP Server with 297 GitHub stars.
codex-storyboard is primarily written in JavaScript. It covers topics such as ai-video, codex, mcp.
You can find installation instructions and usage details in the codex-storyboard GitHub repository at github.com/Yuuhann1999/codex-storyboard. The project has 297 stars and 43 forks, indicating an active community.
codex-storyboard is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codex-storyboard on Agent Skills Hub include codexia, Nomi, open-responses-server. 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: