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 openlayer-ai · LLM Plugin · ★ 102
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jevals safe to install? View the security audit →
jevals Evals and guardrails for agents, using Jev-style decision models instead of an LLM judge. All the evals for a trace go out as one request that costs a few thousandths of a cent and comes back in a few hundred milliseconds, so you can run them on every trace and inside the agent loop. Works with Jev through the TypeSafe or Vercel APIs, with Kev or Laya running locally on a Mac, or with a regular chat LLM if that's all you have (slower, costs more). That's one HTTP request for all eight, and those a
| Stars | 102 |
| Forks | 9 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 69.597479738213/100 |
| Last Updated | 2026-10-01 |
| Created | 2026-09-20 |
| Platforms | python |
| Est. Tokens | ~20k |
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jevals is Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.. It is categorized as a LLM Plugin with 102 GitHub stars.
jevals is primarily written in Python. It covers topics such as agents, evals, guardrails.
You can find installation instructions and usage details in the jevals GitHub repository at github.com/openlayer-ai/jevals. The project has 102 stars and 9 forks, indicating an active community.
jevals is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jevals on Agent Skills Hub include awesome-jev, awesome-typesafe, jev-codex-router. 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: