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 levi-qiao · Codex Skill · ★ 128
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is herdr-agent-quota safe to install? View the security audit →
herdr-agent-quota Live, credential-scoped AI quota and context in Herdr's agent sidebar. 中文文档：README.zh-CN.md That packed layout is the default. puts provider, model, and each quota field on its own row so a narrow sidebar does not truncate , , or : packed (default) stacked The plugin shows only data it can attribute to the pane's exact session and credential scope. Missing fields collapse automatically; failed refreshes keep the last good quota; confirmed pay-as-you-go sessions clear stale subscription numbers.
| Stars | 128 |
| Forks | 30 |
| Language | Rust |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.4628823663989/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-08-15 |
| Platforms | claude-code, codex, rust |
| Est. Tokens | ~25k |
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herdr-agent-quota is Credential-scoped AI quota, context, and cache in Herdr for Claude, Codex, Grok, Agy, OpenCode, Pi, omp, Devin, Muse, and Cursor.. It is categorized as a Codex Skill with 128 GitHub stars.
herdr-agent-quota is primarily written in Rust. It covers topics such as agent-usage, ai-agents, antigravity.
You can find installation instructions and usage details in the herdr-agent-quota GitHub repository at github.com/levi-qiao/herdr-agent-quota. The project has 128 stars and 30 forks, indicating an active community.
herdr-agent-quota is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to herdr-agent-quota on Agent Skills Hub include sub-agents-mcp, sub-agents-skills, skillsaw. 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: