humanizer-ru
Removes machine-generation artifacts from Russian text while preserving authentic content, reducing detection risk and compliance violations tied to AI-gener…
Скилл для ИИ-агентов: находит и убирает следы машинной генерации из русского текста, не трогает живое и не дописывает факты. 56 паттернов, 40 regex-маркеров (реестр доказательств 38/40), ось «дельта детектируемости до/после» с FP-контролем, снятие меток C2PA/EXIF/XMP, пакет на PyPI и онлайн-демо
- Ask Claude to identify and remove AI-generated text patterns from Russian documentation.
- Generate cleaned Russian content by detecting machine writing artifacts and markers.
- Automate removal of AI detection metadata from Russian language outputs before publication.
Removes machine-generation artifacts from Russian text while preserving authentic content, reducing detection risk and compliance violations tied to AI-generated material in regulated markets.
Teams publishing Russian-language content who need to reduce AI-detection signatures without manual rewriting.
https://github.com/Vladimir-Human/humanizer-ru
By Vladimir-Human
How to Get It
claude plugins install Vladimir-Human/humanizer-ru
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Identify and remove AI-generated text patterns from Russian documentation
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 117 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 117 GitHub stars; contributors unknown; last commit 0d ago; license MIT.