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 lynote-ai · Agent Tool · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is humanize-text safe to install? View the security audit →
Free Humanize Text: Open-source toolkit to rewrite AI-generated content into natural English | 中文 What is Humanize-Text? An AI text humanization toolkit. This repo evolved through two stages: v1.0 — Documented 4 humanization methodologies as reference implementations (translation chain, multi-turn LLM rewriting, detection-guided feedback loop, mixed-engine translation). See docs/techniques.md. v1.5 (current) — Added the Standard Pipeline: a production-grade integration of Method 1 (Translation Chain) + Metho
| Stars | 1,602 |
| Forks | 105 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.4865829368629/100 |
| Open Issues | 15 |
| Last Updated | 2026-09-09 |
| Created | 2026-05-18 |
| Platforms | python |
| Est. Tokens | ~16k |
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humanize-text is Open-source pipeline and reference implementations for improving the readability and natural cadence of AI-assisted drafts.. It is categorized as a Agent Tool with 1.6k GitHub stars.
humanize-text is primarily written in Python. It covers topics such as ai-humanizer, ai-tools, ai-writing.
You can find installation instructions and usage details in the humanize-text GitHub repository at github.com/lynote-ai/humanize-text. The project has 1.6k stars and 105 forks, indicating an active community.
humanize-text is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to humanize-text on Agent Skills Hub include patina, muapi-cli, text-humanizer. 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: