by mozilla-ai · LLM Plugin · ★ 856
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git clone https://github.com/mozilla-ai/otariTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 856 |
| Forks | 113 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 70/100 |
| Open Issues | 252 |
| Last Updated | 2026-11-06 |
| Created | 2026-04-02 |
| Platforms | python |
| Est. Tokens | ~17k |
An OpenAI-compatible LLM gateway you own and run yourself. Put one endpoint in front of 40+ providers, then manage API keys, enforce budgets, and track usage in one place. 📖 Docs · 🚀 otari.ai · 📝 Launch blog · 💬 Discord Otari is the proxy server at the heart of otari.ai. Your apps talk to Otari, which routes to your providers.
These tools work well together with otari for enhanced workflows:
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: