humanize

LLM-agnostic skills that make AI writing sound human and relatable.
Grounded in 50+ peer-reviewed sources through April 2026.

Works in any LLM agent: Claude Code, Codex CLI, ChatGPT, Gemini, Cursor, Aider, OpenCode, Continue, Copilot. The install paths differ; the skill content is identical.

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Contents

Get started

• What's inside — the two skills, at a glance
• Installation — plugin marketplace for Claude Code and Codex, one command for everything else
• Usage — how to invoke each skill
• Benchmark — 25 inputs, two independent scorers

Why the skills work

• Background — the gap between AI and human writing
• The detection literature — perplexity, burstiness, stylometry, discourse
• Detection methodology taxonomy — zero-shot, classifier, watermarking, hybrid
• What the research shows about surface rewriting — why the same edits that read human also move the numbers
• The nine humanization levers — the rules the skill applies

Limits and what closes them

• Accuracy benchmarks from the literature — detector accuracy numbers from published research
• Known limitations — what this skill does not guarantee
• What this rule-based approach cannot do (the ceiling) — the learned-classifier ceiling and why it exists
• Complementary techniques — what closes the gap (cross-model paraphrase, base-model rewriting, manual edits)

Maintenance & references

• Updating · Uninstalling · Contributing · License · Citation · References

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What's inside

Skill	What it does	Trigger
humanize	Rewrites or generates text so it reads the way a person actually writes — natural rhythm, real specifics, a voice — by applying nine humanization levers drawn from the detection literature.	"humanize this", "make this sound more human", "make this less robotic", "write this like a person"
ai-check	Forensic analysis of text for AI-generation signals. Scores 9 signal categories, cites every flag with evidence, returns a verdict + confidence + AI-edited fraction estimate.	"does this sound AI?", "run ai-check on this", "score this text"

Both are static rule-based skills (single SKILL.md per skill, zero runtime dependencies). Six advanced hybrid techniques are documented in the humanize skill for high-stakes use.

What "static" means here. No models trained, no API calls, no detector-in-the-loop. The skill is a rulebook the host LLM follows. The detection research is the measuring instrument, not the target: it tells us precisely where AI prose diverges from human prose, and closing those gaps is what makes the writing read as human. That the same edits also lower perplexity-based scores (ZeroGPT, QuillBot) is a consequence, not the goal. The "What this approach cannot do" section below is honest about the learned-classifier ceiling (Grammarly, GPTZero) and what additional steps close that gap.

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Installation