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 yibie · LLM Plugin · ★ 496
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev safe to install? View the security audit →
awesome-jev 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. Jev is not a chat model. It takes unstructured state plus a typed question and returns a typed decision — a choice, a score, or a boolean, each with a confidence. That makes it a drop-in decision layer for software: classification, routing, rubric scoring, verification, and agent guardrails. This list tracks who is actually building with it, and which patterns transfer across industries. The repository treats all categories equally — each entry lives in exactly one category, chosen by its direct Jev application domain. A dedicated Related Practices / Discussions category captures credible public practice signals — X threads, Reddit discussions, and interviews — that describe real Jev usage even when no strong standalone case page exists yet. Why this list Most Jev discussion is scattered across launch threads, model-gateway listings, and one-off prototypes.
| Stars | 496 |
| Forks | 68 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 59.19469346046/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~18k |
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awesome-jev is A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model for typed decisions.. It is categorized as a LLM Plugin with 496 GitHub stars.
awesome-jev is primarily written in Python. It covers topics such as awesome, awesome-list, jev.
You can find installation instructions and usage details in the awesome-jev GitHub repository at github.com/yibie/awesome-jev. The project has 496 stars and 68 forks, indicating an active community.
The top alternatives to awesome-jev on Agent Skills Hub include awesome-LangGraph, awesome-llm-security, awesome-gpt-prompt-engineering. 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: