structural-humanizer
Remove the discourse-level (structural) signs of AI writing that survive surface embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, o
npx skills add NulightJens/humanizer-stack --skill structural-humanizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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,
- The trap (same trap as unslop-ui)
- The six audits
- 1. Theme explicitness (the biggest tell)
- 2. Structural tidiness
- 3. Emotion mode (inverts "show don't tell")
- 4. Reference specificity
- 5. Reader engagement
- 6. Shape convergence
- Workflow
- Intervention menu (rotate, never all at once)
- Model fingerprints (know what drafted the text)
- What this skill does not do
- References
What does the structural-humanizer skill do?
Remove the discourse-level (structural) signs of AI writing that survive surface embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, o
How do I install it?
Run `npx skills add NulightJens/humanizer-stack --skill structural-humanizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NulightJens/humanizer-stack, a repository with 170 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.