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 Nanako0129 · Codex Skill · ★ 367
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TokenBar safe to install? View the security audit →
TokenBar AI token usage & quota monitor for the macOS menu bar — native Swift, Liquid Glass. TokenBar sits in your menu bar and shows what you're spending across 25+ AI coding agents — Claude Code, Codex, Cursor, OpenCode, Gemini CLI and more — read on-device from your local session logs. No Dock icon, no telemetry, no account. The menu-bar title shows today's tokens, cost, live tokens/min, or how much subscription quota is left — as signal bars, a ring, or a popsicle that melts as your window drains. And
| Stars | 367 |
| Forks | 37 |
| Language | Swift |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.4832356429953/100 |
| Open Issues | 42 |
| Last Updated | 2026-09-25 |
| Created | 2026-06-10 |
| Platforms | claude-code, codex |
| Est. Tokens | ~16k |
These tools work well together with TokenBar for enhanced workflows:
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TokenBar is AI token usage & quota monitor for the macOS menu bar — native Swift, Liquid Glass, 3D contribution graph. Tracks Claude Code, Codex, Cursor, OpenCode & 25+ agents locally.. It is categorized as a Codex Skill with 367 GitHub stars.
TokenBar is primarily written in Swift. It covers topics such as ai-usage, claude-code, codex.
You can find installation instructions and usage details in the TokenBar GitHub repository at github.com/Nanako0129/TokenBar. The project has 367 stars and 37 forks, indicating an active community.
TokenBar is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to TokenBar on Agent Skills Hub include TokenEater, poirot, swiftui-design-skill. 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: