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 0xwilliamortiz · Claude Skill · ★ 569
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is humanizer-cli safe to install? View the security audit →
humanizer 33 ways to spot AI-written text, right in your terminal. No network, no API key, no dependencies. One small C program. npm install -g . humanizer windows x64 · node ≥ 18 · github What this is A terminal reference for the humanizer skill, which collects the patterns catalogued in Wikipedia's Signs of AI writing. Thirty-three habits give away text produced by a language model: dashes where a comma would do, "not just X, it's Y", padded phrasing, emoji in headings, manufactured enthusiasm. Every pattern comes with before and after examples. Drafts can be checked on the spot. Nothi
| Stars | 569 |
| Forks | 72 |
| Language | JavaScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 73.4360456995854/100 |
| Last Updated | 2026-08-04 |
| Created | 2026-08-01 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~14k |
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humanizer-cli is 33 ways to spot AI-written text, right in your terminal. Before/after examples, draft checker, zero dependencies.. It is categorized as a Claude Skill with 569 GitHub stars.
humanizer-cli is primarily written in JavaScript. It covers topics such as ai-slop, ai-slop-detection, ai-slop-detector.
You can find installation instructions and usage details in the humanizer-cli GitHub repository at github.com/0xwilliamortiz/humanizer-cli. The project has 569 stars and 72 forks, indicating an active community.
humanizer-cli is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to humanizer-cli on Agent Skills Hub include humanize, humanizer-de, claude-codex-settings. 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: