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 Alex314618-create · LLM Plugin · ★ 703
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is JevRev safe to install? View the security audit →
JevRev The decision layer beside your LLM. Shortlist the options. Score in a loop. Watch the run. Your LLM can imagine, write, test, and revise. It should not have to make every cheap routing decision by itself. JevRev puts Jev beside the LLM: a semantic layer that filters plans, checks progress, and keeps attention on the work worth continuing. The LLM supplies breadth and implementation power. JevRev supplies the second look before more time and tokens are spent. That is JevRev: not another coding agent, but the decision system around one. See the idea The included case asks for a faster CSV parser. The LLM proposes a tempting regex shortcut and a more careful state machine. The shortcut wins the paper ranking, then fails the correctness check. T
| Stars | 703 |
| Forks | 30 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 66.2399490590712/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-02 |
| Created | 2026-09-21 |
| Platforms | node |
| Est. Tokens | ~15k |
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JevRev is An LLM + Jev workflow that changes EVERYTHING. Boost your vertebrate brain with a spine inside.. It is categorized as a LLM Plugin with 703 GitHub stars.
JevRev is primarily written in TypeScript. It covers topics such as jev, llm, workflow.
You can find installation instructions and usage details in the JevRev GitHub repository at github.com/Alex314618-create/JevRev. The project has 703 stars and 30 forks, indicating an active community.
JevRev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to JevRev on Agent Skills Hub include py-gpt, flock, flow-like. 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: