Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by TopiCsarno · Codex Skill · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is yapcap safe to install? View the security audit →
YapCap A native COSMIC panel applet that tracks AI coding quota for Codex, Claude Code, Cursor, Gemini, and GitHub Copilot. Report a bug What it does YapCap lives in your COSMIC panel and shows how much of your AI coding quota you've used — without sending anything to a third party. All data is fetched directly from provider APIs using accounts you add in YapCap. No telemetry, no cloud sync, no separate account needed. Highlights 🤖 Providers Codex — 5h/weekly windows + credits Claude — session/weekly/extra usage Cursor — Auto/Composer and API usage Gemini — Pro / Flash / Lite quota bars (OAuth accounts only) GitHub Copilot — Free chat/completions or paid premium interactions 👥 Multi-account view — add, switch, and remove accounts per provider. Turn on Show all accounts to lay out each selected account side by side in the popup and show one usag
| Stars | 50 |
| Forks | 8 |
| Language | Rust |
| Category | Codex Skill |
| Quality Score | 69.1495178002746/100 |
| Open Issues | 6 |
| Last Updated | 2026-07-21 |
| Created | 2026-04-11 |
| Platforms | claude-code, codex, rust |
| Est. Tokens | ~24k |
These tools work well together with yapcap for enhanced workflows:
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yapcap is Native COSMIC panel applet showing local usage limits for Codex, Claude Code, and Cursor.. It is categorized as a Codex Skill with 50 GitHub stars.
yapcap is primarily written in Rust. It covers topics such as ai-usage, claude-code, codex.
You can find installation instructions and usage details in the yapcap GitHub repository at github.com/TopiCsarno/yapcap. The project has 50 stars and 8 forks, indicating an active community.
The top alternatives to yapcap on Agent Skills Hub include openusage, google-ai-mode-mcp, aiusage. 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: