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 onllm-dev · Codex Skill · ★ 743
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is onWatch safe to install? View the security audit →
onWatch Free, open-source AI API quota monitoring for developers. Track usage across Synthetic, Z.ai, Anthropic, Codex, GitHub Copilot, MiniMax, Gemini CLI, Cursor, and Antigravity in one place. See history, get alerts, and open a local web dashboard before you hit throttling or run over budget. Additionally, you can ingest local telemetry from your own API-driven workflows with API Integrations, keeping track of token use and spending across multiple providers. Links: Website | Buy Me a Coffee Trust & Quality [](https://codecov.io/gh/o
| Stars | 743 |
| Forks | 62 |
| Language | Go |
| Category | Codex Skill |
| License | GPL-3.0 |
| Quality Score | 69.3887301484483/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-19 |
| Created | 2026-02-06 |
| Platforms | claude-code, codex, go |
| Est. Tokens | ~24k |
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onWatch is Track AI API quotas across Synthetic, Z.ai, Anthropic (Claude Code), Codex, GitHub Copilot & Antigravity in real time. Lightweight background daemon (<50MB RAM), SQLite storage, Material Design 3 dash. It is categorized as a Codex Skill with 743 GitHub stars.
onWatch is primarily written in Go. It covers topics such as ai-api-monitoring, antigravity, api-monitoring.
You can find installation instructions and usage details in the onWatch GitHub repository at github.com/onllm-dev/onWatch. The project has 743 stars and 62 forks, indicating an active community.
onWatch is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to onWatch on Agent Skills Hub include agent-sessions, octopus, pilot-shell. 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: