TextHumanize: Transform AI-generated text into natural human-like content

by ksanyok · AI Tool · ★ 85

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install TextHumanize

pip install texthumanize
git clone https://github.com/ksanyok/TextHumanize.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 55/100

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.

ai-textai-text-detectorenglishhumanizehumanize-aihumanizerlibrarymultilingualnlpnlp-library

Quick Facts

Stars85
Forks15
LanguagePython
CategoryAI Tool
Quality Score55/100
Open Issues9
Last Updated2026-10-08
Created2026-02-16
Platformspython
Est. Tokens~25k

About TextHumanize

TextHumanize The most advanced open-source text naturalization engine Transform AI-generated text into clearer, more natural prose — with proprietary PHANTOM™, ASH™, and SentenceValidator™ technologies Reduce built-in AI-like style signals · 25 languages · 38-stage adaptive pipeline · 100% offline · Zero dependencies External AI detector results are not guaranteed.

TextHumanize alternative? Top 6 similar tools

Looking for a TextHumanize alternative? If you're comparing TextHumanize with other ai tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

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:

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