openJev-verdict-2.0 — security grade SAFE, quality 60/100

Security audit verdict: SAFE · quality 60/100

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 Heman10x-NGU · Agent Tool · ★ 133

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is openJev-verdict-2.0 safe to install? View the security audit →

About openJev-verdict-2.0

openJev-verdict-2.0: Non-Autoregressive System 1 Decision Engine -brightgreen) -success) openJev-verdict-2.0 is an open-source, non-autoregressive foundational decision model engineered for deterministic software automation, inspired by TypeSafe AI's Jev and Reinforcement Learning for Calibrated Decisions (RLCD). While standard Large Language Models waste compute generating text strings that deterministic software must parse and validate, openJev-verdict-2.0 evaluates typed decision schemas (, , ) in non-autoregressive forward passes (20–25 ms per decision) with actuarial-grade c

agentic-aibrier-scorecalibrationdecision-enginedeep-learningencoderjevmachine-learningmodernbertnon-autoregressive

Quick Facts

Stars133
Forks17
LanguagePython
CategoryAgent Tool
Quality Score59.9815173442794/100
Last Updated2026-09-20
Created2026-09-19
Platformsbrowser, python
Est. Tokens~15k

Compatible Skills

These tools work well together with openJev-verdict-2.0 for enhanced workflows:

  • von — semantic(0.59)+complementary+rare_topics+same_lang+similar_pop+shared_platform (79%)
  • awesome-jev — semantic(0.35)+complementary+rare_topics+same_lang+similar_pop+shared_platform (71%)

openJev-verdict-2.0 alternative? Top 6 similar tools

Looking for a openJev-verdict-2.0 alternative? If you're comparing openJev-verdict-2.0 with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • awesome-jev-gallery by OmniJev · ⭐ 76

    🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.

  • awesome-jev by OmniJev · ⭐ 65

    🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.

  • von by wfzyx · ⭐ 78

    The open-source System One decision model. Sub-15ms, non-autoregressive, local drop-in alternative to TypeSafe

  • awesome-jev-projects by logicrw · ⭐ 177

    Awesome Jev: source-backed open-source ecosystem radar, plain-language project discovery, and automatic GitHub

  • awesome-jev by hellogumbo · ⭐ 86

    A community directory of projects built on Jev, TypeSafe AI's System One model.

  • awesome-jev by AppitStudio · ⭐ 58

    Curated Jev resources and runnable examples for typed AI decisions.

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Frequently Asked Questions

What is openJev-verdict-2.0?

openJev-verdict-2.0 is Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE). It is categorized as a Agent Tool with 133 GitHub stars.

What programming language is openJev-verdict-2.0 written in?

openJev-verdict-2.0 is primarily written in Python. It covers topics such as agentic-ai, brier-score, calibration.

How do I install or use openJev-verdict-2.0?

You can find installation instructions and usage details in the openJev-verdict-2.0 GitHub repository at github.com/Heman10x-NGU/openJev-verdict-2.0. The project has 133 stars and 17 forks, indicating an active community.

What are the best alternatives to openJev-verdict-2.0?

The top alternatives to openJev-verdict-2.0 on Agent Skills Hub include awesome-jev-gallery, awesome-jev, von. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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