humanizer-stack

A two-pass pipeline for removing the signs of AI writing from outward-facing text, packaged as Claude Code Skills.

Most humanizers only fix words. That is the easy half, and it is the half that is decaying fastest. This repo pairs a surface pass with a structural pass, because the research says structure is where the durable fingerprint lives.

Free community. I build tools like this in the open inside the Jens AI Community, a free Skool group for putting AI to work in your business. If this repo is useful to you, come join us: https://www.skool.com/jens-ai-community-1306

Why two passes

The StoryScope study (Russell et al., 2026) classified 61,608 stories from humans and five LLMs using only discourse-level features, with every style feature withheld. It detected AI text at 93.2% F1.

Then the authors ran AI text through LAMP, a professional span-level rewriting system that strips cliche, purple prose, and redundant exposition. Functionally, a very good surface humanizer.

Detection dropped 1.6 points.

Meanwhile the surface layer is eroding on its own. GPT 5.4 already cut its em-dash usage sharply, and fine-tuning drops stylistic detection from 97% to 3%. Word-level tells are a moving target. Structural tells require structural rewrites.

So: pass 1 fixes the words. Pass 2 fixes the shape. Run them in that order.

What is in here

skills/
  humanizer/                     Pass 1: words and phrasing
    SKILL.md
    references/copy-tells.md     Copy-specific tells (em dash, hype vocab, antithesis)
  structural-humanizer/          Pass 2: discourse structure
    SKILL.md
    references/
      storyscope-findings.md     The study distilled: 30 core features with rates
      genre-calibration.md       Which audits apply per genre
    scripts/structural_scan.py   Deterministic scanner for grep-able structural tells
scripts/
  copy_scan.py                   Deterministic scanner for mechanical copy tells
docs/
  PIPELINE.md                    How the passes chain, and what each one owns

Pass 1: humanizer

Vocabulary, punctuation, and phrasing. Inflated symbolism, promotional language, superficial "-ing" analyses, vague attributions, em dash overuse, rule of three, AI vocabulary, negative parallelism. Built from Wikipedia's Signs of AI writing.

The copy-tells.md reference adds the tells that show up specifically in public copy, ranked by a 3.2M-post Reddit analysis of what people actually flag.

Pass 2: structural-humanizer

Six audits run one at a time, because aspect-based checking found 95% of issues in the study's own pipeline against 68% for a single combined pass: