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 cobusgreyling · AI Tool · ★ 131
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Jev safe to install? View the security audit →
Jev Showcase — TypeSafe System One, not chat Unofficial operator-level companion for TypeSafe Jev Choice · Score · Noul · parallel fan-out · confidence as a second axis Jev 1.13 · released 15 September 2026 What people miss · Announcement · Docs · Models · Article · Showcase framework Most posts stop at “fast structured output.” This repo surfaces the docs-only details that change how you build: Jev is a function call, questions run in parallel, output is free, confidence is not probability, and code owns the workflow. | Noul
| Stars | 131 |
| Forks | 30 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 64.058954851374/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-19 |
| Platforms | python |
| Est. Tokens | ~15k |
Looking for a Jev alternative? If you're comparing Jev with other ai tool 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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Jev is Unofficial TypeSafe Jev showcase — System One decisions, not chat.. It is categorized as a AI Tool with 131 GitHub stars.
Jev is primarily written in Python. It covers topics such as calibrated-decisions, jev, showcase.
You can find installation instructions and usage details in the Jev GitHub repository at github.com/cobusgreyling/Jev. The project has 131 stars and 30 forks, indicating an active community.
Jev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Jev on Agent Skills Hub include awesome-jev, awesome-jev, awesome-jev-typesafe. 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: