awesome-jev: Papers, open reproductions and independent evaluations behind System One models and Jev

by OmniJev · LLM Plugin · ★ 65

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

Security audit verdict: SAFE · quality 52/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

awesomeawesome-listcalibrated-probabilitiescalibrationdecision-modelsjevllmllm-evaluationrlcdstructured-outputs

Quick Facts

Stars65
Forks5
LanguageJavaScript
CategoryLLM Plugin
Quality Score52/100
Open Issues2
Last Updated2026-09-19
Created2026-09-17
Platformsnode
Est. Tokens~24k

About awesome-jev

Awesome JEV Papers, open models and evaluations behind System One models, the class of AI that answers typed questions with calibrated probabilities in one pass. [![License](https://i

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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