Libraries.dev: Libraries and models for next-gen UI

by Jakubantalik · AI Tool · ★ 4.6k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 65/100

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.

beamdesigneffectglowmotionproduct

Quick Facts

Stars4,594
Forks275
LanguageTypeScript
CategoryAI Tool
LicenseMIT
Quality Score65/100
Open Issues21
Last Updated2026-11-03
Created2026-04-12
Platformsnode
Est. Tokens~15k

About Libraries.dev

Libraries.dev High-crafted UI libraries for AI agents. Seven React libraries for the effects that make an interface feel alive: a beam of light travelling a card's border, orbs that think while a model works, bot avatars with living faces, shapes that merge like liquid, a glow that rises with your voice, brushed metal that catches the light, and an image loader that dissolves a pixel mosaic into...

Compatible Skills

These tools work well together with Libraries.dev for enhanced workflows:

  • video-shotcraft — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)
  • typeui — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • doop — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)
  • stylekit — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (55%)
  • dotflowy — semantic(0.20)+complementary+rare_topics+same_lang+shared_platform (52%)

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How this security grade is produced

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:

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