by alberto-solano · AI Tool · ★ 75
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
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 | 75 |
| Forks | 5 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 65/100 |
| Open Issues | 4 |
| Last Updated | 2023-11-02 |
| Created | 2023-10-08 |
| Platforms | python |
| Est. Tokens | ~15k |
Open Source Translation Tool This project use the Meta NLLB-200 translation model through the Hugging Face transformers library. The Meta NLLB-200 is a powerful language model designed for translation which has 54 billion parameters. However, this application built in Django uses a much smaller version (600M parameters) so that it can be used in its simplest form.
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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: