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 juliantanx · Codex Skill · ★ 129
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is aiusage safe to install? View the security audit →
AIUsage Local-first usage tracker for AI coding assistants. Website · Docs · Leaderboard · English | 中文 Track tokens, cost, sessions, models, projects, tool calls, and quota pressure across 20+ AI coding tools in one local dashboard. Local use needs no account, sends no telemetry, and does not require cloud sync. <img src="https://cdn.jsdelivr.net/gh/julianta
| Stars | 129 |
| Forks | 18 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.445695356339/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-20 |
| Created | 2026-05-12 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~16k |
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aiusage is Open-source AI usage tracker — tokens, cost, and sessions across Claude Code, Codex, Hermes, Qoder, and more. It is categorized as a Codex Skill with 129 GitHub stars.
aiusage is primarily written in TypeScript. It covers topics such as ai, ai-analytics, ai-coding-assistant.
You can find installation instructions and usage details in the aiusage GitHub repository at github.com/juliantanx/aiusage. The project has 129 stars and 18 forks, indicating an active community.
aiusage is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to aiusage on Agent Skills Hub include palot, claude-view, usage. 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: