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 Javis603 · Codex Skill · ★ 2.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is token-monitor safe to install? View the security audit →
EN 繁 Token Monitor One live dashboard for every AI coding tool, synced across every machine. <img src="https://img.shields.io/badge/license-MIT-A855F7?style=flat-square" alt="L
| Stars | 2,138 |
| Forks | 205 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 73.6446643165471/100 |
| Open Issues | 92 |
| Last Updated | 2026-09-14 |
| Created | 2026-05-19 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~16k |
These tools work well together with token-monitor for enhanced workflows:
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token-monitor is Local-first desktop widget for tracking token usage, costs, and limits across 36+ AI coding tools—including Claude Code, Codex, Cursor, OpenCode, and OpenClaw—with multi-device sync.. It is categorized as a Codex Skill with 2.1k GitHub stars.
token-monitor is primarily written in JavaScript. It covers topics such as ai, ai-tools, antigravity.
You can find installation instructions and usage details in the token-monitor GitHub repository at github.com/Javis603/token-monitor. The project has 2.1k stars and 205 forks, indicating an active community.
token-monitor is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to token-monitor on Agent Skills Hub include wesight, memorix, modsearch. 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: