humanizer-paper

name	humanizer-paper
description	Register-aware academic language polisher for English journal articles and Chinese doctoral dissertations: removes AI-writing tells while keeping academic norms. Use whenever the user wants to polish, 降AI味, 润色, or norm-check an academic draft, paper section, abstract, or 学位论文 paragraph. Polishes the author's own drafts only — not for laundering generated text past Turnitin or 知网 AIGC detection.
category	research-learning-knowledge
tags	["academic-writing","humanizer","ai-tells","journal","dissertation","zh","en","polishing"]
version	3.0.0
argument-hint	[text-or-file] [--target en-journal|zh-dissertation] [--section abstract|intro|methods|results|discussion|conclusion] [--style STYLE] [--check-only]
license	MIT
allowed-tools	Read, Write, Edit, Grep, Glob, AskUserQuestion, Bash(python *)

Humanizer (Academic): register-aware AI-tell removal

Polish an academic draft so it reads like a careful human author and conforms to its register's norms. Two modes: English journal articles (en-journal) and Chinese doctoral dissertations (zh-dissertation).

The analysis kernel is the "Signs of AI writing" taxonomy from Wikipedia (WikiProject AI Cleanup), re-gated for academic register: several general-prose defaults are backwards in a journal or dissertation, so each tell is kept, calibrated, or disabled rather than applied blindly. The heavy content lives in references/; this file is the router and the behavioral rules.

In the python command below, <skill-dir> is this skill's base directory, announced when the skill loads. Substitute that literal path; it is not an environment variable. The bundled script self-locates, so only the path needs to resolve.

诚信边界 (behavioral hard rule)

This skill polishes the author's own draft for clarity and for compliance with academic norms. It is not a detector-evasion tool.

• The legitimate, transferable techniques here — varying cadence, calibrating hedging, tightening argumentation, enforcing terminology consistency, protecting citations — apply only to text the author actually wrote or substantively authored.
• If the request is framed as "rewrite this generated text so it passes Turnitin / 知网 AIGC / an AI detector", refuse that framing. Redirect to the legitimate goal: improving real originality (add real data and citations, deepen analysis, fix norms). High AIGC rate is treated by many institutions as academic misconduct; do not position this skill as a way around that.

When to use

• Polish an English journal/conference manuscript or section to remove AI tells while keeping formal register, hedging, and section-appropriate passive.
• Polish a Chinese 学位论文 (摘要/引言/方法/结果/讨论/结论/致谢) for AI tells and GB punctuation, terminology unification, and AIGC quantitative self-check.
• Norm-check a draft (--check-only) and report mechanical tells without rewriting.

When NOT to use

• Generic / non-academic prose (blog posts, essays, marketing). This skill's defaults are tuned for academic register; for general "make it sound human" use an upstream generic humanizer, not this one.
• Paper intake, reading, synthesis, or literature review (skim, deep-read, card, compare, gap map, review outline, normalize a DOI/arXiv source). That is paper-workbench, not this skill. This skill edits language; paper-workbench analyzes content.
• Implementing a paper's method into code. Out of scope.

Routing

1. Target (which norm pack)

• If --target is given, use it.
• Otherwise infer from the text's CJK ratio: a high count of CJK characters (roughly > 2% of characters, like infer_language in paper-workbench/scripts/normalize_paper.py) means zh-dissertation; otherwise en-journal.
• If the language is genuinely ambiguous (mixed-language, very short), use AskUserQuestion to pick en-journal vs zh-dissertation. Do not guess silently.

2. Section (which gates)

• Infer the section from headings or structure: abstract, introduction, methods, results, discussion, conclusion. --section overrides.
• Section drives the calibrated tells (most importantly passive voice: keep in Methods, may activate in Discussion). If undeterminable, treat as "general academic body".

Core loop

1. Classify. Read the input. Resolve target and section (Routing above). Identify each AI tell using references/ai-tells-academic.md, applying its keep / calibrate / disable gate — do not apply general-prose defaults blindly.
2. Load the norm pack. Read references/en-journal.md or references/zh-dissertation.md for the active target. It decides per-section behavior and adds register-specific norms.
3. Register-aware draft. Rewrite to remove the tells while obeying the norm pack. Cover everything the original covers (same number of paragraphs/claims). Calibrate hedging (do not delete it), keep section-appropriate passive, unify terminology, fix ghost citations by supplying real (author, year) / [n] or lowering claim strength, replace hollow generalities with specific data/method/citation, and vary sentence cadence.
4. Mechanical lint. Run the script (below) for the quantifiable tells the model tends to miss (dash/quote characters, sentence-length and burstiness stats, "首先/综上" comma-clauses, optional terminology variants). The script is a copilot: it gives coordinates; it does not rewrite.
5. "Still-AI" audit. Ask: what still reads as AI-generated here? List the remaining tells briefly (uniform cadence, residual ghost citations, templated paragraphs, over-stacked hedges).
6. Final. Revise to address the audit and the lint hits. In zh-dissertation, conform punctuation to GB and keep full-text terminology consistent; in en-journal, keep tense and citation style consistent per the norm pack.

Mechanical check

Run the linter for quantifiable tells (planned entry; if the script is not yet present, do the mechanical scan by hand from the same checklist):

python "<skill-dir>/scripts/polish_lint.py" \
  --target "<en-journal|zh-dissertation>" \
  --file "<path-or-omit-to-read-stdin>" \
  --json

The linter reports surface tells (em/en dash, curly quotes, AI high-frequency words, Chinese "几字+逗号" short clauses), cadence stats (sentence count, mean length, burstiness, over-long ratio with the zh > 28 字 threshold), and optional terminology variants when a --glossary is supplied. It is a reporter (exit code always 0); the rewrite stays with the model, guided by references/.

Output contract

Deliver, in order:

1. Draft rewrite — register-correct, all covered content preserved.
2. "Still-AI" audit — brief bullets of remaining tells.
3. Final rewrite — addresses the audit and the lint hits.
4. Change summary — what was changed and why (and, optionally, the lint report).

For --check-only, skip the rewrite: return the lint report plus a short list of the tells found, by gate, with no edited text.

References

• references/ai-tells-academic.md — the re-gated 33-pattern kernel (keep / calibrate / disable) plus 5 new academic-specific tells, with academic before/after.
• references/en-journal.md — English journal norm pack (register, hedging, section-gated passive, IMRaD/CARS, tense, citation styles, dash/comma, spelling).
• references/zh-dissertation.md — Chinese dissertation norm pack (语体, 术语统一, GB/T 15834 标点, GB/T 15835 数字, 法定计量单位, 摘要/结论/标题/致谢, AIGC 量化特征).

Attribution

The pattern taxonomy is from Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. This skill (MIT) re-gates that taxonomy for academic register and adds two norm packs and a mechanical linter.