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 daseinlabs · AI Tool · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-jev safe to install? View the security audit →
openjev Disclaimer: This project was inspired by vinnylarouge/jevlike. It is an independent reimplementation and is not affiliated with or endorsed by the original author. Live demo: the local model scores the available actions each frame. Full-resolution video (.mov). One-pass option scoring with a local Gemma 3 4B on Apple silicon via MLX. Design notes: docs/one-pass-option-scoring.md. Given a context and a list of pre-written options, the model prefills the context once, expands that KV cache across the option batch, and scores every option in a single padded forward pass. No decoding. The score is the log-probability of the option tokens given the context; a softmax over the option scores gives a probability per option, like . Setup also in
| Stars | 63 |
| Forks | 13 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 61.4374048122182/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~15k |
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open-jev is Open Jev implementation with custom finetuning. It is categorized as a AI Tool with 63 GitHub stars.
open-jev is primarily written in Python.
You can find installation instructions and usage details in the open-jev GitHub repository at github.com/daseinlabs/open-jev. The project has 63 stars and 13 forks, indicating an active community.
The top alternatives to open-jev on Agent Skills Hub include redesigned-pancake, claude-code-voice-skill, maui-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: