AIGC Reduction & Rewrite v7.0
AIGC optimization: diagnosis to rewriting
Description
Recommended by
Nico@YouMind
Why we love this skill
Using a three-layer progressive closed-loop logic, it deeply identifies and eliminates model-specific fingerprints, reducing AIGC risk features while ensuring academic depth and logical fidelity through semantic restructuring.
📚 Academic Paper AIGC Reduction and Quality-Preserving Rewriting Expert v7.0 Designed for graduate students, researchers, and paper authors, this academic text optimization skill operates on a core closed loop of 'source control → process correction → result verification → reverse self-check → iterative re-check'. It systematically diagnoses and optimizes issues such as templated expressions, mechanical logic, vague content, inaccurate terminology, and style inconsistencies while preserving original meaning, technical terms, data, and core conclusions. 🔍📝 🌟 Core Capabilities 🔬 Multi-layer Risk Diagnosis Covers 10 types of universal text fingerprints and assists in identifying common expression patterns of models such as ChatGPT, Claude, DeepSeek, and Wenxin Yiyan. 🧠 Deep Semantic Restructuring Goes beyond synonym replacement to rebuild more natural and in-depth academic reasoning by adjusting proposition expression, information order, argument approach, and evidence organization. ✍️ Quality-Preserving Rewriting Comprehensively applies 13 sentence transformation strategies and 20 methods for cleaning high-frequency templated expressions, improving mechanical sentence structures, repetitive connectors, and overly rigid formatting. 📊 Full-text Structure Diagnosis Through macro-cycle and five key triangles, checks whether research problems, theory, literature review, methods, results, conclusions, and innovation form a complete closed loop. 🧩 Fine-grained Section Adaptation Develops differentiated diagnostic and rewriting strategies for abstract, introduction, literature review, research methods, results, discussion, and conclusion respectively. 🌐 Cross-language Risk Scanning Assists in identifying translationese, passive voice stacking, long sentence nesting, and mixed Chinese-English formatting abnormalities to make Chinese academic expression more natural and accurate. 🔄 Reverse Self-check Loop After rewriting, re-verifies from three aspects: technique distribution, new text fingerprints, and information integrity, to avoid 'becoming more templated' or losing key content. 🛡️ Academic Integrity Protection Does not fabricate literature, data, cases, or policy evidence; separately marks information requiring author verification and reminds users to honestly disclose AI usage. 🎯 Use Cases ✅ Single paragraph or partial section optimization ✅ Targeted modification of marked paragraphs from inspection reports ✅ Polishing of abstract, introduction, literature review, discussion, and conclusion ✅ Full-text AIGC risk feature diagnosis ✅ Language and structure adaptation for target journals ✅ Pre-submission quality review and consistency check 📦 Final Deliverables 📄 Quality-preserving rewritten text 🔎 Risk and issue diagnosis report 🛠️ Rewriting strategy and technique description ✅ Reverse self-check and information integrity report 💡 Items requiring author verification and subsequent revision suggestions 🎓 Original meaning preserved · Logic intact · No fabricated data · Academic quality maintained ⚠️ This skill aims to improve academic expression quality and reduce text risk features. It does not guarantee passage through any specific detection platform or achieving a particular detection score.
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AIGC Reduction & Rewrite v7.0
AIGC optimization: diagnosis to rewriting
Description
Recommended by
Nico@YouMind
Why we love this skill
Using a three-layer progressive closed-loop logic, it deeply identifies and eliminates model-specific fingerprints, reducing AIGC risk features while ensuring academic depth and logical fidelity through semantic restructuring.
📚 Academic Paper AIGC Reduction and Quality-Preserving Rewriting Expert v7.0 Designed for graduate students, researchers, and paper authors, this academic text optimization skill operates on a core closed loop of 'source control → process correction → result verification → reverse self-check → iterative re-check'. It systematically diagnoses and optimizes issues such as templated expressions, mechanical logic, vague content, inaccurate terminology, and style inconsistencies while preserving original meaning, technical terms, data, and core conclusions. 🔍📝 🌟 Core Capabilities 🔬 Multi-layer Risk Diagnosis Covers 10 types of universal text fingerprints and assists in identifying common expression patterns of models such as ChatGPT, Claude, DeepSeek, and Wenxin Yiyan. 🧠 Deep Semantic Restructuring Goes beyond synonym replacement to rebuild more natural and in-depth academic reasoning by adjusting proposition expression, information order, argument approach, and evidence organization. ✍️ Quality-Preserving Rewriting Comprehensively applies 13 sentence transformation strategies and 20 methods for cleaning high-frequency templated expressions, improving mechanical sentence structures, repetitive connectors, and overly rigid formatting. 📊 Full-text Structure Diagnosis Through macro-cycle and five key triangles, checks whether research problems, theory, literature review, methods, results, conclusions, and innovation form a complete closed loop. 🧩 Fine-grained Section Adaptation Develops differentiated diagnostic and rewriting strategies for abstract, introduction, literature review, research methods, results, discussion, and conclusion respectively. 🌐 Cross-language Risk Scanning Assists in identifying translationese, passive voice stacking, long sentence nesting, and mixed Chinese-English formatting abnormalities to make Chinese academic expression more natural and accurate. 🔄 Reverse Self-check Loop After rewriting, re-verifies from three aspects: technique distribution, new text fingerprints, and information integrity, to avoid 'becoming more templated' or losing key content. 🛡️ Academic Integrity Protection Does not fabricate literature, data, cases, or policy evidence; separately marks information requiring author verification and reminds users to honestly disclose AI usage. 🎯 Use Cases ✅ Single paragraph or partial section optimization ✅ Targeted modification of marked paragraphs from inspection reports ✅ Polishing of abstract, introduction, literature review, discussion, and conclusion ✅ Full-text AIGC risk feature diagnosis ✅ Language and structure adaptation for target journals ✅ Pre-submission quality review and consistency check 📦 Final Deliverables 📄 Quality-preserving rewritten text 🔎 Risk and issue diagnosis report 🛠️ Rewriting strategy and technique description ✅ Reverse self-check and information integrity report 💡 Items requiring author verification and subsequent revision suggestions 🎓 Original meaning preserved · Logic intact · No fabricated data · Academic quality maintained ⚠️ This skill aims to improve academic expression quality and reduce text risk features. It does not guarantee passage through any specific detection platform or achieving a particular detection score.
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When writing academic papers, have you ever been troubled by how to eliminate AI writing traces and enhance the academic professionalism of the text? This tool is specifically designed for users submitting to Chinese core journals and CSSCI journals, aiming to provide journal-level deep refinement services. It systematically addresses common issues in papers such as AI tone, colloquial expressions, policy document style, low-level logical prompts, and unstable concepts, ensuring your manuscript is closer to the expression standard of mature scholars in core journal papers. Whether you submit the full paper, specific sections, abstract, title system, or typical problem sentences, this tool systematically optimizes around academic problem awareness, title system, concept stability, mechanism analysis, and argument chain. It restructures work-plan-style titles into conceptual, mechanistic, and structured academic expressions, unifies core terminology to avoid polysemy, and strengthens the argument chain, presenting a rigorous logical progression: "Problem Formulation—Theoretical Foundation—Intension Structure—Generation Mechanism—Analytical Framework—Action Field—Implementation Mechanism—Governance Boundary." This tool focuses on improving the academic quality of the paper, not simple text polishing. It will delete or rewrite AI meta-discourse, colloquial transitions, and self-explanatory writing processes, transforming material-style expressions like "service, empowerment, promotion" into academic mechanism analysis. During the refinement process, the tool strictly retains your original research topic, literature basis, and factual materials, never fabricating data or conclusions, ensuring academic integrity. Finally, you will obtain an academic manuscript with rigorous logic, precise expression, and meeting core journal publication requirements, effectively enhancing the competitiveness of your paper for publication.
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