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 haorantang97 · Codex Skill · ★ 288
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LabArt safe to install? View the security audit →
LabArt A small laboratory of visual-creation skills, packaged as a Claude / Codex plugin marketplace. Each plugin is a self-contained aesthetic system. Antibes Holiday Antibes Holiday is a portable visual-creation skill for original relaxed black-pen illustrations, narrative scenes, expressive marks, and early logo exploration. It focuses on physical stroke behavior, shorthand recognition, confident incompletion, causal story staging, active blank space, and non-equilibrium proportion. It is a general visual grammar, not an identity-bound imitation package. Examples These original outputs show the range of the system. They are release examples, not runtime style references. Precarious timing A tilted tray and airborne cups turn bodily counterforce into the event. <strong
| Stars | 288 |
| Forks | 16 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 67.208200616787/100 |
| Last Updated | 2026-09-26 |
| Created | 2026-07-25 |
| Platforms | codex, python |
| Est. Tokens | ~15k |
These tools work well together with LabArt for enhanced workflows:
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LabArt is A Codex marketplace of original visual and artistic creation skills.. It is categorized as a Codex Skill with 288 GitHub stars.
LabArt is primarily written in Python. It covers topics such as agent-skills, chinese-ink, codex.
You can find installation instructions and usage details in the LabArt GitHub repository at github.com/haorantang97/LabArt. The project has 288 stars and 16 forks, indicating an active community.
The top alternatives to LabArt on Agent Skills Hub include claude-workflow-v2, auteur, SkillForge. 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: