by enmerasoftware · Agent Tool · ★ 231
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Featured in: AI Design Tools
npx @enmera/ai-design-check # check the project (or: ai-design-check src lang)
npx skills add enmerasoftware/ai-design-check # the rules and the fix skill for your agentTaken from the project's README on GitHub.
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.
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 | 231 |
| Forks | 12 |
| Language | HTML |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Last Updated | 2026-11-06 |
| Created | 2026-10-28 |
| Platforms | browser, claude-code |
| Est. Tokens | ~18k |
@enmera/ai-design-check Türkçe Finds and fixes what makes interfaces built with AI coding agents (Claude Code, Cursor, Codex) look generated: 74 design rules, a zero-dependency checker that marks every finding, and an agent skill that fixes the screen and proves it by checking again. For dashboards, admin panels, SaaS screens and landing pages.
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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: