A Claude Code skill that removes signs of AI-generated writing from academic medical papers, making them sound more natural and professionally written.
mkdir -p ~/.claude/skills
git clone https://github.com/matsuikentaro1/humanizer_academic.git ~/.claude/skills/humanizer_academicIf you already have this repo cloned (or you downloaded SKILL.md), copy the skill file into Claude Code's skills directory:
mkdir -p ~/.claude/skills/humanizer_academic
cp SKILL.md ~/.claude/skills/humanizer_academic/In Claude Code, invoke the skill:
/humanizer_academic
[paste your manuscript text here]
Or ask Claude to humanize text directly:
Please humanize this academic text: [your text]
Based on Wikipedia's "Signs of AI writing" guide, adapted for medical and scientific literature. Examples are derived from the EMPA-REG OUTCOME trial publications and from the author's (K. Matsui) observations during academic manuscript editing.
"LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."
| # | Pattern | Before | After |
|---|---|---|---|
| 1 | Significance inflation | "represents a pivotal challenge in the evolving landscape" | "is highly prevalent in patients with diabetes" |
| 2 | Notability claims | "landmark trial, led by renowned investigators" | "A total of 7020 patients..." |
| 3 | Superficial -ing analyses | "highlighting the cardioprotective effects" | Report data without interpretation |
| 4 | Promotional language | "groundbreaking study showcases the profound impact" | "empagliflozin reduced heart failure hospitalization" |
| 5 | Vague attributions | "Studies have shown... Experts argue..." | "In the EMPA-REG OUTCOME trial..." |
| 6 | Formulaic challenges | "Despite challenges... future outlook" | State specific limitations |
| # | Pattern | Before | After |
|---|---|---|---|
| 7 | AI vocabulary | "Additionally... pivotal... landscape... crucial" | Remove or replace with simple words |
| 8 | Copula avoidance | "serves as... standing as... representing" | "is" |
| 9 | Negative parallelisms | "Not only X but also Y" | "X and Y" |
| 10 | Rule of three | "efficacy, safety, and tolerability" | Use natural number of items |
| 11 | Synonym cycling | "Patients... Participants... Subjects" | "Patients" (consistent terminology) |
| 12 | False ranges | "from renal function to cardiac outcomes" | List benefits directly |
| # | Pattern | Before | After |
|---|---|---|---|
| 13 | Em dash elimination (zero tolerance) | "benefits—a 35% reduction—appeared early—" | Use commas, parentheses, or periods. ALL em dashes removed, no exceptions |
| 14 | Title Case Headings | "Statistical Analysis And Primary Endpoints" | "Statistical analysis and primary endpoints" |
| 15 | Curly quotes | \u201cclinically significant\u201d | "clinically significant" |
| # | Pattern | Before | After |
|---|---|---|---|
| 16 | Filler phrases | "In order to", "Due to the fact that" | "To", "Because" |
| 17 | Redundant multi-layered hedging | "may suggest... have the potential to confer" | "suggest... may reduce" (keep 1-2 hedges) |
| 18 | Generic conclusions | "The future looks bright" | Specific findings and implications |
| # | Pattern | Before | After |
|---|---|---|---|
| 19 | "linked to" → "associated with" | "has been linked to shorter sleep duration" | "has been reported to be associated with" |
| 20 | "Beyond" → "In addition to" | "Beyond the association with..." | "In addition to the association with..." |
| 21 | "via" → "through" | "obtained via the online form" | "obtained through an online form" |
| 22 | Insufficient hedging | "may reduce the risk of..." | "may help reduce the risk of..." |
| 23 | Artificially condensed expressions | "fatigue–sleepiness cycle", "mutual reinforcement" | "cycle of fatigue and sleepiness", "a self-reinforcing cycle, with each behavior possibly exacerbating the other" |
| 24 | "where" as a non-locative connector | "...at the most intensive level, where almost daily use was..." | "...at the most intensive level, with almost daily use..." |
| 25 | "yield" as a result verb | "did not yield stable estimates" | "failed to produce stable estimates" |
The skill now explicitly preserves standard academic phrases that were previously over-corrected:
- Transitional phrases: "Notably,", "Furthermore,", "In contrast,", etc.
- Attribution phrases with citations: "Prior studies have shown that...", "Evidence suggests that...", etc.
These are only flagged when used in excessive clusters or without supporting citations/data.
Before (AI-sounding):
Heart failure represents a pivotal challenge in the evolving landscape of diabetes care, underscoring the critical importance of addressing cardiovascular comorbidities. This groundbreaking study showcases the profound impact of empagliflozin. Additionally, empagliflozin reduced the risk of hospitalization for heart failure or cardiovascular death by 34%—a remarkable finding—highlighting the cardioprotective effects of this intervention. The future looks bright for patients with type 2 diabetes.
After (Humanized):
Heart failure is highly prevalent in patients with diabetes, occurring in more than one in five patients with type 2 diabetes aged over 65 years. In the EMPA-REG OUTCOME trial, empagliflozin reduced the risk of hospitalization for heart failure or cardiovascular death by 34%. The benefit was consistent in patients with and without heart failure at baseline.
- Wikipedia: Signs of AI writing - Primary source for AI writing patterns
- WikiProject AI Cleanup - Maintaining organization
Medical paper examples (Patterns 1–18) are adapted from:
Fitchett D, Inzucchi SE, Cannon CP, et al. Empagliflozin Reduced Mortality and Hospitalization for Heart Failure Across the Spectrum of Cardiovascular Risk in the EMPA-REG OUTCOME Trial. Circulation. 2019;139(11):1384-1395. doi:10.1161/CIRCULATIONAHA.118.037778
This article is published under CC-BY-4.0 license.
Examples for Patterns 19–25 are based on the author's (K. Matsui) observations during academic manuscript editing in sleep medicine research.
Matsui K. Delving Into PubMed Records: How AI-Influenced Vocabulary has Transformed Medical Writing since ChatGPT. Perspect Med Educ. 2025 Dec 2;14(1):882-890. doi:10.5334/pme.1929
This is a paper I wrote. Using PubMed records, I measured how frequently LLMs such as ChatGPT tend to overuse certain words in medical writing. Take a look if you're curious!
The image above is Figure 1 from the paper cited above, reproduced under the CC-BY 4.0 license.
- 1.1.3 - Added patterns 24 ("where" as a non-locative connector) and 25 ("yield" as a result verb); added author paper reference and Fig.1 to README
- 1.1.2 - Pattern 13: Em dash rule upgraded to zero-tolerance elimination (no exceptions, mandatory final check step)
- 1.1.1 - Merged compressed noun-dash phrases and vague abstractions into single "Artificially condensed expressions" pattern (23)
- 1.1.0 - Added LLM-specific word choice patterns (19-23), preserved legitimate academic phrases, fixed hedging guidance consistency
- 1.0.0 - Initial release adapted for academic medical writing
MIT
Based on blader/humanizer.