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 montevive · MCP Server · ★ 237
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is penpot-mcp safe to install? View the security audit →
Penpot MCP Server 🎨🤖 AI-Powered Design Workflow Automation Connect Claude AI and other LLMs to Penpot designs via Model Context Protocol 🚀 What is Penpot MCP? Penpot MCP is a revolutionary Model Context Protocol (MCP) server that bridges the gap between AI language models and Penpot, the open-source design and prototyping platform. This integration enables AI assistants like Claude (in both Claude Desktop and Cursor IDE) to understand, analyze, and interact with your design files programmatically. 🎯 Key Benefits 🤖 AI-Native Design Analysis: Let Claude AI analyze your UI/UX designs, provide feedback, and suggest improvements ⚡ Automated Design Workflows
| Stars | 237 |
| Forks | 28 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 70.6644338773918/100 |
| Open Issues | 15 |
| Last Updated | 2025-11-01 |
| Created | 2025-05-26 |
| Platforms | mcp, python |
| Est. Tokens | ~69k |
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penpot-mcp is Penpot MCP server. It is categorized as a MCP Server with 237 GitHub stars.
penpot-mcp is primarily written in Python. It covers topics such as ai, api-integration, cursor.
You can find installation instructions and usage details in the penpot-mcp GitHub repository at github.com/montevive/penpot-mcp. The project has 237 stars and 28 forks, indicating an active community.
The top alternatives to penpot-mcp on Agent Skills Hub include youtube-connector-mcp, atlassian-mcp-server, mcp-fusion. 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: