de-aigc-skills

De-AIGC Skills · 中英双语学术降 AIGC

Restore the language distribution of a real researcher — in English or Chinese — for empirical papers in economics, management, and the social sciences. Not synonym swapping. Not sentence shuffling. Systematic reconstruction of the statistical signatures that mark a manuscript as AI-generated.

Scope 适用范围

Papers: empirical work in economics, management, finance, accounting, sociology, political science, education, public policy — anything built on data, identification, and regression tables. Theory papers and pure humanities essays are out of scope (most patterns still transfer, but the section strategies assume an empirical skeleton).

Languages:

• English manuscripts — targets Turnitin AI, GPTZero, Originality.ai, Copyleaks
• Chinese manuscripts — targets 知网 AMLC、万方、维普通达、Turnitin 中文版
• Mixed manuscripts — a Chinese paper with an English abstract, or a bilingual submission package: run each part through its own pattern library, then check cross-language consistency (the English abstract must not overclaim what the Chinese conclusion hedges, and vice versa)

Typical situations:

• Self-checking AIGC rate before journal submission (中文期刊投稿 / SSCI submission)
• Dissertations facing 知网 AMLC or a university's Turnitin AI screen
• Grant proposals, working papers, and reports drafted with AI assistance
• A human-written draft that detectors misclassify as AI (this happens often — formulaic academic prose looks like AI to n-gram detectors)

What does NOT work 无效做法

1. ❌ Synonym replacement — detectors read n-gram distributions, not word lists. Changing "关键" to "核心"、"important" to "crucial" has no effect.
2. ❌ Sentence inversion — flipping "Because A, B" to "B, because A" leaves the syntactic template intact.
3. ❌ Feeding the text to another AI for a "rewrite" — swaps one AI's signature for another's. Paraphraser tools are the fastest way to a higher AI score.
4. ❌ Injecting typos or awkward grammar to "look human" — human experts do not write badly; graders and reviewers notice, and modern detectors are not fooled.

What works: targeted destruction of the structural signatures listed below, plus restoring the concrete, hedged, evidence-anchored voice of a real researcher.

Structural signatures 结构性特征

The two languages fail differently. English LLM output leans on inflated significance and participle padding; Chinese LLM output leans on four-character formulas and connective scaffolding. The one signature they share — and the single highest-impact fix in either language — is uniform sentence rhythm.

English AI text (details and fixes: references/patterns-en.md, EN01–EN22):

1. Inflated significance — "pivotal", "underscores the importance", "paves the way"
2. Superficial "-ing" tails — "…, highlighting the need for further research"
3. Formulaic scaffolding — "In recent years…", Moreover/Furthermore chains, rule-of-three lists, "not only… but also"
4. Overclaiming verbs with underpowered evidence — "proves", "demonstrates", "establishes" hanging off an observational coefficient
5. Low sentence-length variance — nearly all sentences 20–30 words

Chinese AI text (details and fixes: references/patterns-zh.md, ZH01–ZH17):

1. 四字套话密度过高 — every 200 characters, 3+ instances of "综上所述/毋庸置疑/显而易见"
2. 虚词与关联词冗余 — "此外/因此/而且/在此基础上" mechanically stacked
3. 主语回避 — full of "本文认为/相关研究表明", with no specific researchers or literature
4. 句长方差低 — sentences cluster at 20–35 characters, lacking rhythmic variation
5. 结论绝对化 — "充分证明了/必然导致/毫无疑问"

The six-step loop 六步闭环

0. 定位路由      1. 审计扫描      2. 主张-证据核对
   Intake     →     Audit     →     Claim–evidence
                                        │
5. 冷读复查      4. 五维自评      3. 差异化改写
   Recheck    ←    Self-score  ←     Rewrite

Step 0 · Intake & routing 定位路由

Before touching the text:

1. Detect language(s) — route each part to the right pattern library (patterns-en.md / patterns-zh.md). For mixed manuscripts, note which sections are which.
2. Map sections — abstract, intro, literature, data, empirical strategy, results, mechanisms, robustness, discussion, conclusion. Rewrite intensity differs sharply by section (references/sections.md).
3. Identify the venue — a Chinese CSSCI journal, an SSCI field journal, and a dissertation committee have different tolerances for first-person voice and hedging. Ask the user if unclear.
4. Ask for a voice sample (optional but powerful) — if the author has earlier human-written papers or paragraphs, match their sentence rhythm, connective habits, and hedging placement instead of a generic "human" style.

Step 1 · Audit scan 审计扫描

Scan the full text against both pattern libraries and output a structured audit report — do not edit anything yet. The author must see the whole picture first.

## AI-signature audit / AI 痕迹审计

| ¶ | Excerpt 原文片段 | Rule 规则 | Severity 严重度 |
|---|-----------------|----------|----------------|
| 2 | "毋庸置疑，数字化转型…" | ZH01 四字套话 | 🔴 |
| 5 | "…, underscoring the importance of digital…" | EN02 -ing tail | 🔴 |
| 7 | "This proves that the reform caused…" | EN10 overclaiming verb | 🔴 |

Include a summary line: total hits per severity, the 3 worst sections, and the estimated rewrite depth (light polish / section rewrites / full-pass rewrite).

Step 2 · Claim–evidence audit 主张-证据核对

Empirical papers live or die on the match between verbs and evidence strength. This step is what makes de-AIGC for empirical work different from generic humanizing:

• Every causal or quantitative claim must anchor to a number, table, figure, or citation. "显著提升企业绩效" → which table, which column, which coefficient?
• Verb ↔ design match:
  • Clean identification (RCT, sharp RD, well-defended DiD) → direct statements are fine: "the reform reduced entry by 12%"
  • Observational / correlational → "is associated with", "与…相关"
  • Suggestive / mechanism evidence → "is consistent with", "为…提供了证据", "这与…的解释一致"
• Flag every "prove/demonstrate/establish/充分证明/必然导致" whose design cannot carry that weight — and every unsupported "significant/显著" with no test statistic.
• Never resolve a mismatch by inventing evidence. If a claim has no anchor, flag it for the author; weakening the verb is the default fix.

Step 3 · Differentiated rewrite 差异化改写

Work through the audit list, section by section, using the per-section strategies in references/sections.md. Priorities, in order of impact:

1. Break the rhythm ceiling first 先砸句长方差 — this is the single highest-leverage fix in both languages. Per ~200 words (or 200 字): at least one short sentence (≤8 words / ≤15 字) and one long sentence (≥40 words / ≥50 字). Short sentences open questions or land emphasis ("The data say otherwise." / "数据讲了另一个故事。"); long sentences carry the evidence.
2. Concretize 具体化 — replace vague attributions and inflated adjectives with data, authors, years: "相关研究表明" → "Acemoglu and Restrepo (2020) estimate…"; "profound impact" → "raised TFP by 4.3% (t = 3.81)".
3. De-scaffold 拆脚手架 — remove paragraph-initial connectives (Moreover/Furthermore/此外/因此) and ordinal chains (首先…其次…最后 / First… Second… Finally) outside genuine enumerations; connect paragraphs by semantic relay (the next sentence picks up the previous sentence's key noun).
4. Recalibrate claims 校准断言 — apply Step 2's verb ↔ design matches; add epistemic hedges where missing, and simplify stacked hedges where the model piled up "may potentially suggest the possibility that…".
5. Restore researcher voice 恢复研究者声音 — show choices and trade-offs: "We use X rather than Y because…" / "受限于数据，我们无法识别…". Admitting a limitation or a surprise is the hardest pattern for an LLM to fake.

Hard protections 硬性红线 — regardless of what the patterns say:

• Never alter numbers, coefficients, standard errors, p-values, sample sizes, equations, variable names, or citation contents. Data, coefficients, and citations are never touched.
• Never fabricate data, results, citations, or "surprising findings" for flavor.
• Never inject errors, slang, or archaic vocabulary to game perplexity.
• Never change what the paper claims — only how it says it.
• Do not over-correct: standard academic phrases are not AI tells. Keep "Notably," / "Prior studies have shown that… (with citations)" / "significant at the 1% level" / "robustness check" — flag such phrases only when stacked or citation-free. Full preserve-list: top of references/patterns-en.md.

Step 4 · Five-dimension self-score

Score the rewritten text 1–10 per dimension (rubric: references/scoring.md):

Dimension	Weight	Checkpoint
Concreteness	1.5×	Vague claims replaced by data / authors / cases?
Rhythm	1.2×	Sentence-length variance high enough? Short-long mix?
Calibration	1.3×	Verbs match evidence? Hedges present but not stacked?
Implicit cohesion	1.0×	Paragraphs relay by meaning, not connectives?
Researcher voice	1.0×	Choices, trade-offs, limitations visible?

Weighted total < 35 → back to Step 3. ≥ 42 → pass.

Step 5 · Cold-reader recheck

Re-read the full text as a stranger and run three final checks:

1. Fluency — did any fix damage the argument's flow or academic register?
2. Fidelity — diff every number, name, year, and citation against the original. Zero drift allowed.
3. Consistency — one voice throughout; no visible seam between rewritten and untouched paragraphs; for bilingual packages, EN and ZH parts must make the same claims at the same strength.

Deliver: final text + change log (which sections changed, which rules fired, what was deliberately left alone) + any unresolved flags from Step 2 that need the author's judgment.

Works well with

• 44-matsuikentaro1-humanizer_academic — English medical/academic pattern source; use for biomedical manuscripts
• 45-stephenturner-skill-deslop / 46-hardikpandya-stop-slop — general English prose de-slopping outside the academic register
• 47-conorbronsdon-avoid-ai-writing — structured audit format for non-academic documents
• 49-voidborne-d-humanize-chinese — general Chinese humanizing beyond the academic register
• 70-ssci-polish — SSCI-oriented English polish after de-AIGC is done
• Draft first, de-AIGC last: run this skill on a finished draft, not during drafting — mid-draft humanizing fights the writing process.

References

• references/patterns-en.md — 22 English AI-signature patterns (EN01–EN22), each with detection rule + empirical-paper before/after, plus the preserve-list
• references/patterns-zh.md — 17 categories of Chinese AI-tell patterns (ZH01–ZH17), with identification rules and repair strategies
• references/sections.md — section-by-section rewrite strategies for empirical papers, bilingual symptoms and red lines (differentiated strategies by section, Chinese-English parallel)
• references/scoring.md — five-dimension rubric, bilingual (five-dimension scoring rubric)
• references/examples-en.md — English before/after pairs across an empirical paper's sections
• references/examples-zh.md — 12 sets of Chinese before-and-after rewrites (covering every section of an empirical paper)

Integrity statement

The goal is to return human-written and AI-assisted text to the language distribution of a real researcher — not to help fully AI-generated work evade detection.

• ✅ A researcher's own draft misclassified as AI by a detector
• ✅ AI-assisted drafting + human revision, where the author owns every claim
• ❌ A fully AI-generated paper the "author" hopes to pass off unread
• ❌ Ghostwriting, plagiarism laundering, or data fabrication of any kind

Academic integrity outranks detection scores. No rewrite may touch the research claims, the data, or the citations — and when a claim lacks evidence, the fix is to flag it, not to hide it.