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 avabbbb · Codex Skill · ★ 138
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Flovart safe to install? View the security audit →
Flovart Your coding agent, now with a visual production studio. Open-source, local-first workspace where you and your coding agent edit the same live Workflow — with your own image and video models, assets and API keys. English · 简体中文 Try live demo · Download preview · Get started · Compatibility <a href="https://github.com/avabbbb/Flovart"
| Stars | 138 |
| Forks | 34 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 63.6385233557783/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-26 |
| Created | 2025-10-21 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~18k |
These tools work well together with Flovart for enhanced workflows:
Looking for a Flovart alternative? If you're comparing Flovart with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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Flovart is Open-source agent-native visual production workspace where humans and coding agents edit the same live canvas — local-first, BYOK image/video models.. It is categorized as a Codex Skill with 138 GitHub stars.
Flovart is primarily written in TypeScript. It covers topics such as agentic-ai, ai-agent, ai-image.
You can find installation instructions and usage details in the Flovart GitHub repository at github.com/avabbbb/Flovart. The project has 138 stars and 34 forks, indicating an active community.
Flovart is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Flovart on Agent Skills Hub include Overture, Nomi, ai-media-generator. 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: