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 Maciek-roboblog · Claude Skill · ★ 8.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Claude-Code-Usage-Monitor safe to install? View the security audit →
🎯 Claude Code Usage Monitor A beautiful real-time terminal monitoring tool for Claude AI token usage with advanced analytics, machine learning-based predictions, and Rich UI. Track your token consumption, burn rate, cost analysis, and get intelligent predictions about session limits. 📑 Table of Contents ✨ Key Features 🚀 Installation ⚡ Modern Installation with uv (Recommended) 📦 Installation with pip 🛠️ Other Package Managers 📖 Usage Get Help Basic Usage Configuration Options Available Plans [🙏 Please
| Stars | 8,712 |
| Forks | 460 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 69.7964266839018/100 |
| Open Issues | 37 |
| Last Updated | 2026-07-05 |
| Created | 2025-06-19 |
| Platforms | claude-code, python |
| Est. Tokens | ~22k |
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Claude-Code-Usage-Monitor is Real-time Claude Code usage monitor with predictions and warnings. It is categorized as a Claude Skill with 8.7k GitHub stars.
Claude-Code-Usage-Monitor is primarily written in Python. It covers topics such as ai, analytics, claude.
You can find installation instructions and usage details in the Claude-Code-Usage-Monitor GitHub repository at github.com/Maciek-roboblog/Claude-Code-Usage-Monitor. The project has 8.7k stars and 460 forks, indicating an active community.
Claude-Code-Usage-Monitor is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Claude-Code-Usage-Monitor on Agent Skills Hub include serena, ai-guide, emdash. 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: