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 kgoedecke · MCP Server · ★ 716
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is doop safe to install? View the security audit →
Doop is the open-source alternative to Paper.design — a multiplayer design canvas for humans and AI agents. Every design lives on a shareable Canvas () holding Frames — artboards that render real HTML in sandboxed iframes. People edit in the browser; AI agents edit through the built-in MCP server, streaming their designs in live. Everyone sees everything as it happens: cursors, presence, frame edits, agent status, and an activity feed. Design with agents, not prompts-and-refresh — connect Claude Code (or any MCP client) once, then wat
| Stars | 716 |
| Forks | 87 |
| Language | TypeScript |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 63.6517880776767/100 |
| Open Issues | 14 |
| Last Updated | 2026-09-19 |
| Created | 2026-08-22 |
| Platforms | claude-code, codex, mcp, node |
| Est. Tokens | ~19k |
These tools work well together with doop for enhanced workflows:
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doop is The open-source alternative to Paper.design. A multiplayer design canvas where humans and AI agents design together, live. MCP built in.. It is categorized as a MCP Server with 716 GitHub stars.
doop is primarily written in TypeScript. It covers topics such as ai-agents, canvas, claude.
You can find installation instructions and usage details in the doop GitHub repository at github.com/kgoedecke/doop. The project has 716 stars and 87 forks, indicating an active community.
doop is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to doop on Agent Skills Hub include agor, deepcontext-mcp, linkedin-mcp-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: