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 hamed-elfayome · Claude Skill · ★ 3.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Claude-Usage-Tracker safe to install? View the security audit →
Claude Usage Tracker A native macOS menu bar application for real-time monitoring of Claude AI usage limits 🇬🇧 English • 🇪🇸 Español • 🇫🇷 Français • 🇩🇪 Deutsch • 🇮🇹 Italiano • 🇵🇹 Português • 🇯🇵 日本語 • 🇰🇷 한국어 • 🇨🇳 简体中文 ### Download Latest Release (v3.0.1) macOS 14.0+ (Sonoma) Officially Signed Overview Claude Usage Tracker is a lightweight, native macOS menu bar application that provides real-time monitoring of your Claude AI usage limits. Built entirely with Swift and SwiftUI, it offers a clean, intuitive
| Stars | 3,387 |
| Forks | 181 |
| Language | Swift |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 77.4248047729804/100 |
| Open Issues | 34 |
| Last Updated | 2026-08-31 |
| Created | 2025-10-25 |
| Platforms | claude-code |
| Est. Tokens | ~23k |
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Claude-Usage-Tracker is Native macOS menu bar app for tracking Claude AI usage limits in real-time. Built with Swift/SwiftUI.. It is categorized as a Claude Skill with 3.4k GitHub stars.
Claude-Usage-Tracker is primarily written in Swift. It covers topics such as claude, claude-ai, claude-code.
You can find installation instructions and usage details in the Claude-Usage-Tracker GitHub repository at github.com/hamed-elfayome/Claude-Usage-Tracker. The project has 3.4k stars and 181 forks, indicating an active community.
Claude-Usage-Tracker is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Claude-Usage-Tracker on Agent Skills Hub include claude-code-cheat-sheet, ios-simulator-skill, awesome-claude-skills. 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: