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Get Started Free →Remove AI-generated writing patterns from rebuttal prose to make it sound natural, direct, and authentically human-authored. Use when a Stage 2 refined draft or Stage 4 follow-up response reads too formulaic, robotic, or "GPT-like". Supports academic English.
.claude/skills/runtsang-writing-anti-ai/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-07 | ✓→✗ | ▼ Worse | 88% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 12% | 0% |
> RebuttalStudio Utility — Stage 2 / Stage 4 / Polish > Apply this skill after Stage 2 refinement or Stage 4 follow-up drafting when the output reads formulaic or over-polished. Use before the Stage 3 document compilation to ensure the final rebuttal sounds authored by a researcher, not generated by a model.
Remove predictable AI writing patterns from rebuttal text so it reads as natural, confident academic prose — not as machine-generated boilerplate.
Reviewers are experienced academics who read hundreds of papers and rebuttals. A rebuttal that sounds AI-generated signals:
The goal is not to hide AI assistance — it's to ensure the ideas and voice of the actual authors come through clearly.
LLMs predict statistically likely continuations. This produces text that applies to the widest variety of cases — which is exactly what makes it feel generic in a rebuttal. Rebuttal prose needs to be specific to this paper, this reviewer, and this concern.
Core principle: Every sentence in a rebuttal should only be true for this paper. If it could appear in any rebuttal, rewrite it.
Remove throat-clearing openers and emphasis crutches that add length without adding meaning.
Common AI openers to remove:
AI emphasis phrases to cut:
AI tends toward predictable patterns. In rebuttals, these appear as:
| AI Pattern | Rebuttal Fix | |-----------|-------------| | "It's not just X, it's also Y" (negative parallelism) | Drop it; state Y directly | | "First… Second… Third…" for every single response | Only use numbered lists when the reviewer explicitly listed multiple concerns | | Em-dash reveals: "X — which shows Y" | "X, which shows Y" or split into two sentences | | "This represents a significant advancement…" | State what changed specifically |
Check sentence length distribution in each response block:
Reviewers are experts. Do not over-explain or over-justify.
Bad (patronizing): "To ensure complete clarity, and so that the reviewer can fully appreciate our contribution, we explain in detail…"
Good: "To clarify: the specific point]."
Bad (hand-holding): "It could potentially be argued that the metric might perhaps be considered somewhat limited in scope."
Good: "The metric has a known limitation: state it]. We address this in Appendix B."
If a sentence sounds like it was written to be quoted or highlighted, rewrite it.
Bad: "This represents a paradigm shift in how the community will approach problem]."
Good: "The method reduces specific metric] by X% over specific baseline], enabling specific application]."
Academic rebuttals use "we" — but AI-generated text often over-hedges or over-personalizes.
Too hedged: "The authors feel that it may be the case that…" → "We argue that…" Over-personalized: "We personally believe that…" → "We believe that…"
These words are statistically overrepresented in AI-generated academic text:
| Overused | Replace With | |----------|-------------| | additionally | also / and / (restructure sentence) | | furthermore | (restructure sentence) | | notably | (delete or be specific) | | it is worth noting | (just state the point) | | comprehensive | thorough / complete (or be specific) | | leverage | use | | utilize | use | | facilitate | help / enable | | demonstrate | show | | novel | (be specific about what is new) |
Rate each response block (0–10 points each):
| Dimension | 0 pts | 10 pts | |-----------|-------|--------| | Directness | Opens with filler, buries the answer | States the answer in the first sentence | | Specificity | Could appear in any rebuttal | References this specific paper, reviewer comment, or result | | Rhythm | All sentences same length | Mix of short and long sentences | | Tone | Defensive or overly formal | Collegial, confident, factual | | Density | Padded with acknowledgments | Information-dense; every sentence moves forward |
40–50: Ready to submit. 30–39: Needs targeted edits. < 30: Rewrite from outline.
This skill only removes AI patterns. Do not:
{{placeholder}} tokens> **Reviewer's Comment**: / **Response**: block)Adapted from Claude Scholar's writing-anti-ai skill. Original authored by gaoruizhang. Based on Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. Source: https://github.com/Galaxy-Dawn/claude-scholar/blob/main/skills/writing-anti-ai/SKILL.md
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 6,122 | 4,663 | -24% | 1 | 1 | 0% | 902 | 2,145 | +138% | 0 | 0 | — |
case-02 | pass→pass | 6,616 | 4,685 | -29% | 1 | 1 | 0% | 1,233 | 2,101 | +70% | 0 | 0 | — |
case-03 | pass→pass | 7,322 | 5,014 | -32% | 1 | 1 | 0% | 1,154 | 2,233 | +94% | 0 | 0 | — |
case-04 | fail→pass | 12,904 | 6,938 | -46% | 1 | 1 | 0% | 1,757 | 2,482 | +41% | 0 | 0 | — |
case-05 | pass→pass | 7,697 | 5,306 | -31% | 1 | 1 | 0% | 1,119 | 2,368 | +112% | 0 | 0 | — |
case-06 | fail→pass | 9,006 | 6,633 | -26% | 1 | 1 | 0% | 1,275 | 2,318 | +82% | 0 | 0 | — |
case-07 | pass→fail | 8,383 | 4,083 | -51% | 1 | 1 | 0% | 1,177 | 2,215 | +88% | 0 | 0 | — |
case-08 | pass→pass | 8,201 | 5,413 | -34% | 1 | 1 | 0% | 1,122 | 2,329 | +108% | 0 | 0 | — |
case-09 | pass→pass | 7,364 | 5,139 | -30% | 1 | 1 | 0% | 1,191 | 2,225 | +87% | 0 | 0 | — |
case-10 | pass→pass | 3,736 | 5,677 | +52% | 1 | 1 | 0% | 795 | 2,295 | +189% | 0 | 0 | — |
case-11 | pass→pass | 4,013 | 3,550 | -12% | 1 | 1 | 0% | 580 | 1,951 | +236% | 0 | 0 | — |
case-12 | pass→pass | 5,156 | 3,740 | -27% | 1 | 1 | 0% | 872 | 2,051 | +135% | 0 | 0 | — |
case-13 | pass→pass | 7,726 | 6,481 | -16% | 1 | 1 | 0% | 1,201 | 2,652 | +121% | 0 | 0 | — |
case-14 | pass→pass | 9,591 | 8,389 | -13% | 1 | 1 | 0% | 1,610 | 2,967 | +84% | 0 | 0 | — |
case-15 | pass→pass | 8,699 | 6,293 | -28% | 1 | 1 | 0% | 1,220 | 2,511 | +106% | 0 | 0 | — |
case-16 | pass→pass | 5,500 | 6,526 | +19% | 1 | 1 | 0% | 890 | 2,232 | +151% | 0 | 0 | — |
case-17 | fail→pass | 9,226 | 5,260 | -43% | 1 | 1 | 0% | 1,365 | 2,230 | +63% | 0 | 0 | — |
case-18 | pass→pass | 4,120 | 3,911 | -5% | 1 | 1 | 0% | 755 | 2,134 | +183% | 0 | 0 | — |
case-19 | pass→pass | 10,742 | 7,186 | -33% | 1 | 1 | 0% | 1,559 | 2,665 | +71% | 0 | 0 | — |
case-20 | pass→fail | 12,254 | 3,711 | -70% | 1 | 1 | 0% | 1,871 | 2,095 | +12% | 0 | 0 | — |
case-21 | pass→pass | 6,674 | 3,252 | -51% | 1 | 1 | 0% | 1,223 | 1,986 | +62% | 0 | 0 | — |
case-22 | pass→pass | 7,917 | 4,387 | -45% | 1 | 1 | 0% | 1,141 | 2,220 | +95% | 0 | 0 | — |
case-23 | pass→pass | 7,165 | 5,276 | -26% | 1 | 1 | 0% | 1,036 | 2,175 | +110% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.