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 chaterm · Codex Skill · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Chaterm safe to install? View the security audit →
中文 / English / 日本語 <img src="https://img.shields.io/twitter/follow/chatermai?style
| Stars | 3,069 |
| Forks | 293 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 65.1265289870472/100 |
| Open Issues | 55 |
| Last Updated | 2026-09-09 |
| Created | 2025-04-14 |
| Platforms | cli, codex, node |
| Est. Tokens | ~16k |
These tools work well together with Chaterm for enhanced workflows:
Looking for a Chaterm alternative? If you're comparing Chaterm with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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Chaterm is Open source AI terminal for cloud and infrastructure management, enabling you to deploy, troubleshoot, and automate services using natural language and intelligent agents.. It is categorized as a Codex Skill with 3.1k GitHub stars.
Chaterm is primarily written in TypeScript. It covers topics such as aiagent, bastion, codex-cli.
You can find installation instructions and usage details in the Chaterm GitHub repository at github.com/chaterm/Chaterm. The project has 3.1k stars and 293 forks, indicating an active community.
The top alternatives to Chaterm on Agent Skills Hub include ruler, agent-of-empires, DevDocs. 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: