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 AbdelStark · Agent Tool · ★ 555
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-typesafe-jev safe to install? View the security audit →
Awesome Jev / TypeSafe The field guide to typed decisions. Official docs, working integrations, independent experiments, and the builders pushing Jev into new territory. Jev turns context into a probability, a choice, or a score; your code owns the threshold and the action. One call, three typed answers. In TypeSafe's documented support-ticket example, Jev chooses the technical team (0.85 probability), scores frustration at level 1 on a 0–2 rubric, and gives urgency a 1.0 Noul probability. The example response is from ;
| Stars | 555 |
| Forks | 140 |
| Language | HTML |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.4792303702447/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-02 |
| Created | 2026-09-17 |
| Est. Tokens | ~28k |
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awesome-typesafe-jev is Awesome Jev: a source-backed field guide to TypeSafe's System One model, with SDKs, live demos, agent tools, and independent evaluations.. It is categorized as a Agent Tool with 555 GitHub stars.
awesome-typesafe-jev is primarily written in HTML. It covers topics such as agent-skills, ai-agents, awesome.
You can find installation instructions and usage details in the awesome-typesafe-jev GitHub repository at github.com/AbdelStark/awesome-typesafe-jev. The project has 555 stars and 140 forks, indicating an active community.
awesome-typesafe-jev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-typesafe-jev on Agent Skills Hub include awesome-jev-gallery, awesome-jev-live, awesome-decision-models. 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: