by devmount · LLM Plugin · ★ 245
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. 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 | 245 |
| Forks | 51 |
| Language | Jupyter Notebook |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 65/100 |
| Last Updated | 2026-10-07 |
| Created | 2015-05-22 |
| Est. Tokens | ~19k |
GermanWordEmbeddings There has been a lot of research about the training of word embeddings on English corpora. This toolkit applies gensim's word2vec on German corpora to train and evaluate German word embeddings. An overview about the project, evaluation results and download links can be found on the project's website or directly in this repository.
These tools work well together with GermanWordEmbeddings 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: