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 matsuikentaro1 · Claude Skill · ★ 181
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Featured in: Anti-Slop
Humanizer Academic A Claude Code skill that removes signs of AI-generated writing from academic medical papers, making them sound more natural and professionally written. Installation Recommended (clone directly into Claude Code skills directory) Manual install/update (only the skill file) If you already have this repo cloned (or you downloaded ), copy the skill file into Claude Code's skills directory: Usage In Claude Code, invoke the skill: Or ask Claude to humanize text directly: Overview Based on Wikipedia's "Signs of AI writing" guide, adapted for medical and scientific literature. Examples are derived from the EMPA-REG OUTCOME trial publications and from the author's (K. Matsui) observations during academic manuscript editing. Key Insight from Wikipedia "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases." 31 Patterns Detected (with Before/After Examples) Content Patterns |
| Stars | 181 |
| Forks | 29 |
| Category | Claude Skill |
| Quality Score | 63.2465414487322/100 |
| Last Updated | 2026-09-19 |
| Created | 2026-02-04 |
| Platforms | claude-code |
| Est. Tokens | ~19k |
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humanizer_academic is A Claude Code skill that removes signs of AI-generated writing from academic medical papers, making them sound more natural and professionally written.. It is categorized as a Claude Skill with 181 GitHub stars.
You can find installation instructions and usage details in the humanizer_academic GitHub repository at github.com/matsuikentaro1/humanizer_academic. The project has 181 stars and 29 forks, indicating an active community.
The top alternatives to humanizer_academic on Agent Skills Hub include digital-marketing-pro, life-sciences, claude-ai-mcp. 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.
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