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 tokentopapp · Agent Tool · ★ 70
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tokentop safe to install? View the security audit →
Early release — tokentop is functional and actively developed. Please report issues! tokentop htop for your AI costs Real-time terminal monitoring of LLM token usage and spending across providers and coding agents. Features · Install · Quick Start · Agents · Plugins · Docs tokentop.app
| Stars | 70 |
| Forks | 6 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 74.4607435975091/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-12 |
| Created | 2026-01-23 |
| Platforms | cli, node |
| Est. Tokens | ~17k |
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tokentop is htop for your AI costs — real-time terminal monitoring of LLM token usage and spending across providers and coding agents. It is categorized as a Agent Tool with 70 GitHub stars.
tokentop is primarily written in TypeScript. It covers topics such as ai, anthropic, bun.
You can find installation instructions and usage details in the tokentop GitHub repository at github.com/tokentopapp/tokentop. The project has 70 stars and 6 forks, indicating an active community.
tokentop is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tokentop on Agent Skills Hub include toktrack, gptree, claude-code-open. 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: