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 jaredpalmer · AI Tool · ★ 8.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is kev safe to install? View the security audit →
kev Jev-inspired decision model. Typed questions in, calibrated probabilities out, one forward pass. kev is a LoRA adapter and a small readout head on top of Qwen2.5-0.5B. It reads a document once and answers many typed questions about it in parallel, in a single prefill pass with no decoding. The document and every question are packed into one sequence; a block-causal mask lets each question see the document but never another question. A pointer head then scores each question's options against its decision token and applies softmax. Those probabilities are the output. The head is trained with cross-entropy against labelled outcomes, so the probabilities are l
| Stars | 8,595 |
| Forks | 562 |
| Language | Python |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 71.0712930645072/100 |
| Open Issues | 35 |
| Last Updated | 2026-10-06 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~17k |
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kev is Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own. It is categorized as a AI Tool with 8.6k GitHub stars.
kev is primarily written in Python. It covers topics such as decision-model, jev, qwen3.
You can find installation instructions and usage details in the kev GitHub repository at github.com/jaredpalmer/kev. The project has 8.6k stars and 562 forks, indicating an active community.
kev is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to kev on Agent Skills Hub include laya, MaxKB, QuantDinger. 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: