by byadityaraj · LLM Plugin · ★ 73
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx @designmdcc/cli stripe.com > DESIGN.md # human-readable spec
npx @designmdcc/cli stripe.com --skill > SKILL.md # agent-ready skill
Taken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 73 |
| Forks | 7 |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 66/100 |
| Last Updated | 2026-10-20 |
| Created | 2026-05-12 |
| Platforms | browser, claude-code, cli |
| Est. Tokens | ~18k |
DesignMD Production-grade design context for AI coding workflows. Extract a real design system from any production URL — colors, typography, spacing, breakpoints, motion, interaction states — as a portable (human-readable) or an agent-ready . Use it from the web, a CLI, or an MCP server your coding agent calls directly.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: