humanizer-zh-academic-student

Remove AI-generated traces from Chinese student academic writing.

27|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/Smith-2758/Humanizer-zh-academic --skill humanizer-zh-academic-student
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: humanizer-zh-academic-student
Source: https://github.com/Smith-2758/Humanizer-zh-academic/tree/main
Command: npx skills add https://github.com/Smith-2758/Humanizer-zh-academic --skill humanizer-zh-academic-student

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the mechanical, template-heavy, and pretentious tone common in AI-generated Chinese student academic writing. It transforms AI-generated papers, lab reports, and assignments into authentic, rigorous, and natural student expressions without sacrificing factual accuracy or academic clarity.

Core Features & Use Cases

  • Interactive Parameter Collection: Automatically confirms writing seriousness (low/medium/high) and role (course paper, thesis, lab report, defense speech) before rewriting, ensuring the output matches the intended academic scenario.
  • Four-Dimension De-AI Cleaning: Removes AI traces from vocabulary, syntax, structure, and formatting by targeting show-off expressions, mechanical transitions, hollow summaries, and dangerous formatting patterns.
  • Controlled Custom Roles: Supports custom role definition by collecting only three bounded parameters—discipline background, education stage, and text purpose—preventing unbounded style drift.

Quick Start

Use the humanizer-zh-academic-student skill to process the attached Chinese academic text by first confirming the desired seriousness level and writing role, then returning the rewritten text with a parameter confirmation and change log.

Frequently Asked Questions about humanizer-zh-academic-student

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I remove AI traces from my Chinese academic paper?

Removing AI traces from a Chinese academic paper involves cleaning mechanical syntax, template-heavy expressions, and pretentious vocabulary while calibrating the writing tone to match authentic student academic standards. This Skill targets those exact AI traces across vocabulary, syntax, structure, and formatting.

What is the best way to humanize AI-generated Chinese lab reports for students?

Humanizing AI-generated Chinese lab reports requires applying a four-dimensional de-AI cleaning process to eliminate hollow summaries and show-off expressions, transforming the text into natural, rigorous student writing. This Skill automatically confirms your writing role as a lab report before executing the rewrite.

Can I calibrate the writing tone for a Chinese thesis defense speech using de-AI rewriting?

Yes, de-AI rewriting can calibrate the writing tone for a Chinese thesis defense speech by setting the appropriate seriousness level and writing role during the interactive parameter collection phase. This ensures the output matches the exact academic scenario and education stage required.

Does de-AI rewriting work for both undergraduate and graduate Chinese course papers?

De-AI rewriting works for both undergraduate and graduate Chinese course papers by applying controlled custom roles defined through discipline background, education stage, and text purpose parameters. This prevents unbounded style drift while tailoring the academic expression to the correct level.

How to reduce mechanical syntax and pretentious vocabulary in Chinese student academic writing?

Reducing mechanical syntax and pretentious vocabulary in Chinese student academic writing requires a structured de-AI cleaning process that targets dangerous formatting patterns and mechanical transitions. This Skill outputs the rewritten text alongside a parameter confirmation and a detailed change log.

What are the limitations of using automated de-AI tools for Chinese academic texts?

Automated de-AI tools for Chinese academic texts rely on bounded parameters like discipline background and education stage to prevent style drift, but users must verify factual accuracy and academic clarity in the final output. The change log helps track modifications, yet human review remains essential for technical correctness.