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 Kuddev · Codex Skill · ★ 1.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pebrel safe to install? View the security audit →
Pebrel A GPU-accelerated terminal, SSH workspace, and home for your AI CLI sessions. Built with Rust and GPUI for Windows, macOS, and Linux. Split terminals · SSH & SFTP · Claude Code & Codex workflows · Native document reader <img src="https://img.shields.io/github/stars/Kuddev/pebrel?sty
| Stars | 1,948 |
| Forks | 99 |
| Language | Rust |
| Category | Codex Skill |
| License | GPL-3.0 |
| Quality Score | 63.6733400767743/100 |
| Open Issues | 57 |
| Last Updated | 2026-09-20 |
| Created | 2026-07-05 |
| Platforms | claude-code, cli, codex, rust |
| Est. Tokens | ~24k |
These tools work well together with pebrel for enhanced workflows:
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pebrel is AI-native, GPU-accelerated terminal emulator for Windows with SSH, persistent sessions, split panes, and first-class AI CLI workflows.. It is categorized as a Codex Skill with 1.9k GitHub stars.
pebrel is primarily written in Rust. It covers topics such as ai-cli, ai-terminal, claude-code.
You can find installation instructions and usage details in the pebrel GitHub repository at github.com/Kuddev/pebrel. The project has 1.9k stars and 99 forks, indicating an active community.
pebrel is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to pebrel on Agent Skills Hub include tty7, dinotty, clsh. 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: