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 v-modal · LLM Plugin · ★ 740
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev-tools safe to install? View the security audit →
awesome-jev-tools A curated awesome list of public projects and practices built on Jev, TypeSafe AI's System One model for typed decisions. This README is the homepage aggregate of the current category files, so the latest accepted entries are visible here without drilling into subpages. A curated list of public projects and developer patterns built on Jev, TypeSafe AI's System One model for typed decisions. What is Jev? Jev is not a chat model. It does not write text or hold conversations. Instead, it takes unstructured state alongside a typed question and returns a typed decision—such as a choice, a score, or a boolean—accompanied by a confidence rating.By eliminating token-by token decoding, Jev acts as a fast, low-latency decision layer directly inside software. Developers use it to handle classification, infrastructure routing, rubric scoring, verification gates, and autonomous agent guardrails.Goal of this ListMost discussions about Jev are scattered across launch threads, social media, and one-off prototypes.
| Stars | 740 |
| Forks | 46 |
| Category | LLM Plugin |
| Quality Score | 53.4984483268/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-02 |
| Created | 2026-09-19 |
| Est. Tokens | ~28k |
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awesome-jev-tools is A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.. It is categorized as a LLM Plugin with 740 GitHub stars.
You can find installation instructions and usage details in the awesome-jev-tools GitHub repository at github.com/v-modal/awesome-jev-tools. The project has 740 stars and 46 forks, indicating an active community.
The top alternatives to awesome-jev-tools on Agent Skills Hub include awesome-jev-gallery, awesome-gpt-prompt-engineering, awesome-jev-use-cases. 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: