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 L-Forster · Codex Skill · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-jet safe to install? View the security audit →
OpenJet The local agent for your own GPU Files - tools - shell approval - workflows - local model. OpenJet runs the agent loop locally. No API calls. No code or data upload. RTX 3090 + Qwen 27B: 33 tok/s - 70 tok/s with MTP. Open source. Discord Someone built a local AI agent that runs on your own GPU. OpenJet runs the agent loop locally: No API calls. No code or data upload. RTX 3090 + Qwen 27B: 33 tok/s - 70 tok/s with MTP. Open source. If you are new to local LLMs, OpenJet is the fastest way to get started without spending hours figuring out models, runtimes, and config. If you have already tried local LLMs and got frustrated piecing together a model backend, a frontend, and an actual agent workflow, OpenJet removes that setup tax. Install Recommended bash pipx install open-jet
| Stars | 50 |
| Forks | 4 |
| Language | Python |
| Category | Codex Skill |
| License | AGPL-3.0 |
| Quality Score | 69.7664463127525/100 |
| Last Updated | 2026-08-26 |
| Created | 2026-02-16 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
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open-jet is A terminal coding agent, and a Python SDK for embedding on-device models in your own apps.. It is categorized as a Codex Skill with 50 GitHub stars.
open-jet is primarily written in Python. It covers topics such as agent, ai, ai-agents.
You can find installation instructions and usage details in the open-jet GitHub repository at github.com/L-Forster/open-jet. The project has 50 stars and 4 forks, indicating an active community.
open-jet is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to open-jet on Agent Skills Hub include captain-claw, Deskdrop, kea-research. 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: