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 tbhb · Claude Skill · ★ 118
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vale-ai-tells A Vale package for detecting linguistic patterns commonly associated with AI-generated prose. Based on 2024-2025 research into vocabulary fingerprints and structural tells. This package targets technical documentation, where clarity and directness matter more than style. Less useful for creative writing, marketing copy, or other contexts where some of these patterns may represent intentional choices. [!NOTE] The author created this package to help clean up AI-assisted technical documentation, not to disguise AI-generated content as human-written. Installation Add the package to your : Then run: Linting commit messages AI-generated commit messages carry the same fingerprints as AI-generated prose, plus a few tells of their own: self-referential preambles lik
| Stars | 118 |
| Forks | 12 |
| Language | Shell |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 67.8586244075067/100 |
| Open Issues | 10 |
| Last Updated | 2026-10-11 |
| Created | 2025-12-02 |
| Platforms | claude-code, cli, gemini |
| Est. Tokens | ~18k |
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vale-ai-tells is In today's rapidly evolving landscape, vale-ai-tells is a comprehensive, cutting-edge Vale style package that empowers writers to seamlessly delve into the rich tapestry of AI tells. It's not just a r. It is categorized as a Claude Skill with 118 GitHub stars.
vale-ai-tells is primarily written in Shell. It covers topics such as ai-detection, ai-slop, ai-text-detection.
You can find installation instructions and usage details in the vale-ai-tells GitHub repository at github.com/tbhb/vale-ai-tells. The project has 118 stars and 12 forks, indicating an active community.
vale-ai-tells is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to vale-ai-tells on Agent Skills Hub include anti-slop-writing, repopack-py, patina. 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: