中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), sect
SkillScore82Maintained
Automatic
◆Writing & Editing pack
Last verified 8/28/2026
What it changes
Passed 18 of 22 test cases with the skill, and 13 of 22 without it.
Without skill59%
With skill82%
Checks it passed that the model alone missed
输出明确要求包含至少一句 50 字以上的长句。
The response identifies Concreteness as the dimension weighted at 1.5x.
The response explicitly refuses to alter standard errors, p-values, or numerical data.
and 4 more in the full report
6 fixed12 passed either way3 still failing1 broke
Pass-rate lift
+23 pp
Tokens
+167%
Agent turns
0%
Response time
0%
Model gemini-3.6-flashMeasured 8/28/2026v118 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 6
Prompt
In the five-dimension evaluation step, what is the exact weighted total threshold score that requires sending the manuscript back for another rewrite iteration?
What was checked, without and with the skill
Checked
Without skill
With skill
The response states that a weighted score below 35 requires re-entering the rewrite loop.
Missed
Passed
case-15 · gemini-3.6-flash · 8/28/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md240 lines7 files
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
❌ Synonym replacement — detectors read n-gram distributions, not word lists.
把"关键"改成"核心"、"important" 改成 "crucial" 毫无作用。
❌ Sentence inversion — flipping "Because A, B" to "B, because A" leaves the
syntactic template intact.
❌ 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.
❌ Injecting typos or awkward grammar to "look human" — human experts do not