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 arvindrk · MCP Server · ★ 170
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is extract-design-system safe to install? View the security audit →
extract-design-system — Extract Design Tokens from Any Website Reverse-engineer design tokens (colors, typography, spacing, border radius, shadows) from any public website and generate starter JSON and CSS custom properties for your local project. Available as an AI agent skill (Claude, Cursor, Codex) and standalone CLI. Skills follow Anthropic's Agent Skills overview and are installable through the skills.sh ecosystem. This repository is skills-first. It publishes an installable skill, and the bundled CLI is the executable workflow the agent runs under the hood. What It Extracts font fami
| Stars | 170 |
| Forks | 20 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 76.4306542351128/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-01 |
| Created | 2026-03-22 |
| Platforms | browser, claude-code, cli, codex, mcp, node |
| Est. Tokens | ~16k |
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extract-design-system is Extract design tokens (colors, typography, spacing, border radius, shadows) from any public website. Generates JSON and CSS custom properties for local projects. Available as an AI agent skill (Claude. It is categorized as a MCP Server with 170 GitHub stars.
extract-design-system is primarily written in TypeScript. It covers topics such as agent-skills, automation, brand-audit.
You can find installation instructions and usage details in the extract-design-system GitHub repository at github.com/arvindrk/extract-design-system. The project has 170 stars and 20 forks, indicating an active community.
extract-design-system is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to extract-design-system on Agent Skills Hub include awesome-ai-agent-skills, brandmd, awesome-claude-skills. 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: