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 Supervertaler · LLM Plugin · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Supervertaler-Workbench safe to install? View the security audit →
Supervertaler Workbench Professional AI-enhanced translation workbench with multi-LLM support (GPT-4, Claude, Gemini, Mistral, Ollama), translation memory, glossary management, and seamless CAT tool integration (memoQ, Trados, CafeTran, Phrase, Déjà Vu). See the latest release notes for what changed in the most recent build. Download Three ways to install. Pick whichever fits your setup. [Download from the latest release](https://github.com/Superve
| Stars | 57 |
| Forks | 12 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 62.6025817130021/100 |
| Open Issues | 63 |
| Last Updated | 2026-09-09 |
| Created | 2025-09-04 |
| Platforms | browser, claude-code, gemini, python |
| Est. Tokens | ~14k |
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Supervertaler-Workbench is Open-source, AI-enhanced CAT tool with multi-LLM support, translation memory, glossary management, 'Superlookup' concordance across TMs/glossaries/web resources, voice commands, Okapi sidecar for file. It is categorized as a LLM Plugin with 57 GitHub stars.
Supervertaler-Workbench is primarily written in Python. It covers topics such as ahk, ai, cafetran.
You can find installation instructions and usage details in the Supervertaler-Workbench GitHub repository at github.com/Supervertaler/Supervertaler-Workbench. The project has 57 stars and 12 forks, indicating an active community.
Supervertaler-Workbench is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Supervertaler-Workbench on Agent Skills Hub include jta, ai-microcore, prompt-engineering-skills. 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: