structural-humanizer

# structural-humanizer Read this first. The `humanizer` skill fixes words: "delve", em dashes, rule of three, negative parallelism. This skill fixes what survives that pass: the structure. The two are different jobs, run in sequence. Surface pass first, structural pass second. **Why this layer matters more.** StoryScope (Russell et al. 2026, arXiv:2604.03136) classified 61,608 stories from humans and 5 LLMs using only discourse-level features, with all style features withheld: 93.2% detection accuracy. Then they ran AI text through LAMP, a professional span-level rewriting framework that removes cliche, purple prose, and redundant exposition (functionally, a surface humanizer). Detection dropped 1.6 points. Meanwhile the surface layer is decaying on its own: GPT 5.4 already slashed em-dash usage, and fine-tuning drops stylistic detection from 97% to 3%. The durable fingerprint is structural, and fixing it requires structural rewrites, not word swaps. Full findings with numbers: [references/storyscope-findings.md](references/storyscope-findings.md). ## The trap (same trap as unslop-ui) Do not replace one default with another. If every piece now opens mid-scene, names three feelings, and ends unresolved, that is a new detectable cluster. The study's deepest finding is convergence: all five AI models occupy one tight region of structural space while humans are dispersed and rare. Rarity IS the human signal. So: **pick 1-2 structural interventions per piece, vary them across...