by smixs · Claude Skill · ★ 172
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npx skills add smixs/humanizer-ru
npx skills add smixs/humanizer-ru -g
Taken from the project's README on GitHub.
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 | 172 |
| Forks | 33 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 63/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-23 |
| Created | 2026-01-27 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
Humanizer RU Skill для AI-агентов: Claude Code, Codex, OpenClaw, Hermes и любых других, читающих формат SKILL.md. Удаляет признаки AI-генерации из русского текста: канцелярит, штампы ChatGPT и Claude, длинные тире, «не просто… а», рубленый драматизм, техножаргон. 34 паттерна, жёсткие запреты и защита от переусердствования.
These tools work well together with humanizer-ru for enhanced workflows:
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AI-writing humanizer for KO/EN/ZH/JA
微信公众号 AI 运营助手 | 选题、写稿、审稿、排版、配图、发布全流程 Skill,支持 OpenClaw / Claude Code / Cursor / Codex
Правит русский текст после нейросети и сверяет цифры, даты и имена. Навык для Claude Code, Codex, Cursor и Cla
humanizer-ru: редактура русского текста для AI-агентов. Убирает канцелярит и шаблонные обороты, сверяет смысл
Free, open-source AI writing humanizer and detector. 55 patterns, 5 voices, a 0-100 AI-tell score, and nothing
Russian text quality for AI agents — neuroslop cleanup, typography, information style, editorial standards, UX
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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: