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 browser-use · AI Tool · ★ 10.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jev-ultrafast safe to install? View the security audit →
Featured in: TypeSafe Jev
Jev Ultrafast ⚡ A browser agent with a dynamic, indexed action space. Give it one goal. TypeSafe's Jev picks an operation and an element. A small LLM writes text only when the operation is . Zürich → London on Google Flights in 7.1 seconds. One natural-language goal, actual text generation, and loading waits included. Watch the MP4 · Measurements · Read the loop The action space Every observation produces a new element table: The operations are , , , , , , , and . Only supported operations and targets are offered. text one TypeSafe request ┌───────────────────────────┐ page → element table → operation │ │ clicktarget │ │ typetexttarget │ │ selecttarget, if present
| Stars | 10,555 |
| Forks | 638 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 55.8859296816462/100 |
| Open Issues | 64 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-16 |
| Platforms | python |
| Est. Tokens | ~15k |
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jev-ultrafast is i. am. speed.. It is categorized as a AI Tool with 10.6k GitHub stars.
jev-ultrafast is primarily written in Python.
You can find installation instructions and usage details in the jev-ultrafast GitHub repository at github.com/browser-use/jev-ultrafast. The project has 10.6k stars and 638 forks, indicating an active community.
jev-ultrafast is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jev-ultrafast on Agent Skills Hub include claude-code-infrastructure-showcase, vision-agent, axton-obsidian-visual-skills. 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: