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 · Codex Skill · ★ 441
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-text-detector safe to install? View the security audit →
Best AI Text Detector An explainable, cautious AI-generated text risk analyzer for coding agents and local workflows. 简体中文 · Install · Evaluation · Use Cases Other Quality Projects AI Humanize Text:https://github.com/lynote-ai/humanize-text AI Image Detector:https://github.com/lynote-ai/ai-image-detector Why This Exists Most AI text detectors are either overconfident, opaque, or awkward to embed inside agent workflows. takes the opposite approach: explainable weighted signals instead of hidden model claims a local CLI and Python API that are easy to script a short-text guardrail that refuses to overstate weak evidence skill-ready packaging for Codex, Claude Code, and other repo-aware agents repr
| Stars | 441 |
| Forks | 17 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 74.3830119606976/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-05 |
| Created | 2026-05-25 |
| Platforms | codex, python |
| Est. Tokens | ~7k |
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ai-text-detector is A cautious, explainable AI-like text risk analyzer for local workflows and coding agents.. It is categorized as a Codex Skill with 441 GitHub stars.
ai-text-detector is primarily written in Python. It covers topics such as agent-tools, ai-content-detection, ai-text-detector.
You can find installation instructions and usage details in the ai-text-detector GitHub repository at github.com/lynote-ai/ai-text-detector. The project has 441 stars and 17 forks, indicating an active community.
ai-text-detector is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-text-detector on Agent Skills Hub include TrustGraph, medusa, humanize. 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: