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by Monet-AI-Editor · MCP Server · ★ 110
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Monet AI video editor and design canvas for coding agents Local-first video editing, a live design canvas, terminal-native agent workflows, real timeline operations, local transcription, and export in one macOS app. Monet is no longer just an AI video editor. Use Claude Code or Codex directly inside Monet Canvas to design vector graphics, animate, simulate physics, and drop the result straight into your video timeline. Built by Het Patel curl -fsSL https://raw.githubusercontent.com/Monet-AI-Editor/Monet/main/install.sh | bash Star History What Monet Is Monet is a desktop video editor designed for people who want coding agents to work on video directly, not through a fragile bridge into anot
| Stars | 110 |
| Forks | 8 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.4061388550615/100 |
| Last Updated | 2026-07-07 |
| Created | 2026-04-21 |
| Platforms | claude-code, cli, codex, mcp, node |
| Est. Tokens | ~17k |
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Monet is Edit Videos and Design Images with Claude code or Codex. It is categorized as a MCP Server with 110 GitHub stars.
Monet is primarily written in TypeScript. It covers topics such as agentic-ai, agentic-video-editor, ai-video-editor.
You can find installation instructions and usage details in the Monet GitHub repository at github.com/Monet-AI-Editor/Monet. The project has 110 stars and 8 forks, indicating an active community.
Monet is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Monet on Agent Skills Hub include octocode, sudocode, cutcli-cookbook. 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.
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