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 bnsd55 · Agent Tool · ★ 69
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jevmlx safe to install? View the security audit →
jevmlx Typed decisions from a local model on Apple Silicon. One batched forward pass, every field at once, with a probability per field. Asking an LLM for JSON means parsing what it wrote, fixing what drifted, and retrying until it parses. jevmlx takes a schema of booleans, enums, and multi-selects, scores every allowed answer for every field in one forward pass, and assembles the JSON itself. Nothing is generated token by token: the output is valid by construction and every field carries a probability. Install bash library into your project pip install git+https://github.com/bnsd55/jevmlx CLI only uv tool install git+https://github.com/bnsd55/jevmlx from a clone (dev) git clone https://github.com/bnsd55/jevmlx && cd jevmlx && ./setup.s
| Stars | 69 |
| Forks | 8 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.8233791266676/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-25 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~15k |
These tools work well together with jevmlx for enhanced workflows:
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jevmlx is Jev-style parallel constrained decisions for any MLX model on Apple Silicon. Typed, schema-valid JSON in one forward pass.. It is categorized as a Agent Tool with 69 GitHub stars.
jevmlx is primarily written in Python. It covers topics such as apple-silicon, jev, local-llm.
You can find installation instructions and usage details in the jevmlx GitHub repository at github.com/bnsd55/jevmlx. The project has 69 stars and 8 forks, indicating an active community.
jevmlx is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jevmlx on Agent Skills Hub include maclocal-api, SAM, ovo-local-llm. 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: