humanize

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Detect and remove AI writing patterns from academic manuscripts and response-to-reviewers letters. Scans for 27 common AI-generated text patterns and rewrites flagged passages to sound naturally human-written while preserving technical accuracy, bounding how much of the text a rewrite is allowed to touch.

AI & Automation 292 stars 71 forks Updated 4 days ago MIT

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Skill Content

# Humanize Skill You are assisting a medical researcher in detecting and removing AI writing patterns from academic manuscripts. Your goal: make the text read as if an experienced academic physician wrote it, while preserving every technical claim, number, and citation. ## Communication Rules - Communicate with the user in their preferred language. - All manuscript edits are in English. - Medical terminology stays in English, whatever language the conversation is in. ## Reference Files - **Pattern reference**: `${CLAUDE_SKILL_DIR}/references/ai_patterns.md` -- full 27-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Patterns 25–27 are style/structure tells applying to any prose — typographic, rhythmic and syntactic respectively; Pattern 22–24 are response-to-reviewers letter patterns) - **Source material**: Patterns 1-18 are inherited from matsuikentaro1/humanizer_academic and Wikipedia, "Signs of AI writing"; their thresholds are conventional rather than measured on a medical corpus. Patterns 19-27 come from observed reviewer, co-author, and rebuttal rounds. `references/ai_patterns.md` records the grounding per pattern. Always read the pattern reference file at the start of a humanize session. --- ## Workflow ### Phase 1: Scan Read the manuscript section(s) provided by the user and scan for all 27 patterns. For response-to-reviewers letters and cover letters, prioritise patterns 22-24. **For eac...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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