Paper Imitation & Pub. Expert
From interdisciplinary lit to original papers
Description
📚 Paper Imitation & Pub. Expert Develop an inspiring paper into your own high-quality original work step by step. Ideal for graduate students seeking to draw inspiration from excellent papers in other disciplines for topic migration, structural reconstruction, and submission optimization. 🔍 Inspiring Literature Identification: Analyze the reusable title mechanisms, problem awareness, chapter structure, theoretical framework, research methods, evidence organization, and language style of the model paper. 🔄 Interdisciplinary Paradigm Transfer: Build a 'source domain—target domain' transfer matrix to determine which ideas can be transferred and which theories, concepts, cases, and conclusions must be reconstructed. 🧠 Original Contribution Modeling: Identify literature gaps, distill core propositions and mechanisms, and check whether the paper merely changes the research object or applies a generic framework. 🧭 Research Design Planning: Supports theoretical research, case studies, qualitative research, quantitative research, literature reviews, policy text analysis, and mixed methods research. 🛡️ Real Evidence Gate: Do not fabricate data, samples, interviews, cases, statistical results, literature, or DOIs; without real evidence, do not generate false empirical conclusions. ✍️ Evidence-Driven Writing: Build argument chains via 'proposition—evidence—citation—rebuttal', generate original content chapter by chapter, and clearly mark research materials that need to be supplemented. 📈 Continuous Paper Evolution: Record each round of revisions, material gaps, and maturity changes according to L1 (topic conception), L2 (research plan), L3 (evidence formation), L4 (submission draft), and L5 (ready for submission). 🧑⚖️ Triple Simulation Review: Identify rejection risks from the perspectives of a theoretical reviewer, a method reviewer, and a target journal reviewer, and form specific revision strategies. 📨 Complete Submission Loop: Assist in generating the final paper, citation verification table, cover letter, innovation point statement, author response, revision index, and related declaration templates. ✨ Learn the paradigm, write original content, and enhance real submission competitiveness.
Related Skills
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WriteAIGC Reduction & Rewrite v7.0
📚 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.
Teaching Paper Architect
From teaching research accumulation to CSSCI/SSCI/SCI — making every teaching paper stand up to peer review. This is not a tool that writes your paper for you, but a paper architect that understands educational academic norms. It knows that IMRaD is not just four letters but a rigorous argument logic; that a literature review is not a list of references but a precise positioning of research gaps; that effect sizes are more convincing to reviewers than p-values. Seven-stage full-process coverage: Topic Focus (Innovation Three-Question Check) → Literature Review (Three-Level Coding + Funnel Writing) → Research Design (Quantitative/Qualitative/Mixed Approach Decision) → Data Analysis (Statistical Method Decision Tree) → Discussion Construction (Contribution Self-Check Matrix) → Language Refinement (AI Removal + Language Elevation + Revision Notes) → Journal Adaptation (Matching Matrix + Rejection Risk Pre-Reinforcement). Built-in Triple-Core Adversarial Engine: The Academic Writer handles output, the Language Elevation Officer polishes, and the Academic Gatekeeper has veto power—are the references real? Is the data reliable? Is the argument grounded in evidence? Does the contribution match the target journal level? Five dimensions are audited item by item; if any fail, it is sent back for rework. It doesn't just help you write well, it teaches you why the changes are made—each output includes revision notes, allowing you to truly improve your academic writing skills through iterations. Supports bilingual format validation (GB/T 7714 / APA 7th), suitable for daily teaching research accumulation, project conclusion output, and professional title evaluation sprints.
ResearchMgmt C-J Intr Expert v3.0
Writing the introduction for management C-journal papers is a challenging task for many researchers. This Skill v3.0 adopts the AFP 3.1 three-layer architecture, providing a closed-loop process from information collection to final delivery. Core capabilities: 1. Anti-hallucination engine—mandatory input + traceable verification: all references must come from the user's provided pool of real literature; the AI cannot supplement or fabricate. 2. Dual-dimension adaptation (discipline × research type)—four disciplines: strategic management, organizational behavior and human resources, public management, and information management, × three types: empirical quantitative, qualitative case, and theoretical construction. Automatically matches 12 introduction paradigms. 3. Precise journal style adaptation—uses googleSearch to retrieve examples from target journals in the last 3 years, with a three-level fallback strategy to ensure retrieval success. 4. Five-part introduction structure theory—research background → transition → research object → research value → research design, generating a blueprint paragraph by paragraph. 5. B-core nine-dimension quality audit—structure, logic, citation, adaptation, style, innovation, gap, theory, anti-hallucination. Delivery only if quantitative score ≥80. Suitable for: graduate students and early-career researchers in management who need to submit to CSSCI/Beijing University Core journals.
Paper Imitation & Pub. Expert
From interdisciplinary lit to original papers
Description
📚 Paper Imitation & Pub. Expert Develop an inspiring paper into your own high-quality original work step by step. Ideal for graduate students seeking to draw inspiration from excellent papers in other disciplines for topic migration, structural reconstruction, and submission optimization. 🔍 Inspiring Literature Identification: Analyze the reusable title mechanisms, problem awareness, chapter structure, theoretical framework, research methods, evidence organization, and language style of the model paper. 🔄 Interdisciplinary Paradigm Transfer: Build a 'source domain—target domain' transfer matrix to determine which ideas can be transferred and which theories, concepts, cases, and conclusions must be reconstructed. 🧠 Original Contribution Modeling: Identify literature gaps, distill core propositions and mechanisms, and check whether the paper merely changes the research object or applies a generic framework. 🧭 Research Design Planning: Supports theoretical research, case studies, qualitative research, quantitative research, literature reviews, policy text analysis, and mixed methods research. 🛡️ Real Evidence Gate: Do not fabricate data, samples, interviews, cases, statistical results, literature, or DOIs; without real evidence, do not generate false empirical conclusions. ✍️ Evidence-Driven Writing: Build argument chains via 'proposition—evidence—citation—rebuttal', generate original content chapter by chapter, and clearly mark research materials that need to be supplemented. 📈 Continuous Paper Evolution: Record each round of revisions, material gaps, and maturity changes according to L1 (topic conception), L2 (research plan), L3 (evidence formation), L4 (submission draft), and L5 (ready for submission). 🧑⚖️ Triple Simulation Review: Identify rejection risks from the perspectives of a theoretical reviewer, a method reviewer, and a target journal reviewer, and form specific revision strategies. 📨 Complete Submission Loop: Assist in generating the final paper, citation verification table, cover letter, innovation point statement, author response, revision index, and related declaration templates. ✨ Learn the paradigm, write original content, and enhance real submission competitiveness.
Related Skills
View allWriteAIGC Reduction & Rewrite v7.0
📚 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.
Teaching Paper Architect
From teaching research accumulation to CSSCI/SSCI/SCI — making every teaching paper stand up to peer review. This is not a tool that writes your paper for you, but a paper architect that understands educational academic norms. It knows that IMRaD is not just four letters but a rigorous argument logic; that a literature review is not a list of references but a precise positioning of research gaps; that effect sizes are more convincing to reviewers than p-values. Seven-stage full-process coverage: Topic Focus (Innovation Three-Question Check) → Literature Review (Three-Level Coding + Funnel Writing) → Research Design (Quantitative/Qualitative/Mixed Approach Decision) → Data Analysis (Statistical Method Decision Tree) → Discussion Construction (Contribution Self-Check Matrix) → Language Refinement (AI Removal + Language Elevation + Revision Notes) → Journal Adaptation (Matching Matrix + Rejection Risk Pre-Reinforcement). Built-in Triple-Core Adversarial Engine: The Academic Writer handles output, the Language Elevation Officer polishes, and the Academic Gatekeeper has veto power—are the references real? Is the data reliable? Is the argument grounded in evidence? Does the contribution match the target journal level? Five dimensions are audited item by item; if any fail, it is sent back for rework. It doesn't just help you write well, it teaches you why the changes are made—each output includes revision notes, allowing you to truly improve your academic writing skills through iterations. Supports bilingual format validation (GB/T 7714 / APA 7th), suitable for daily teaching research accumulation, project conclusion output, and professional title evaluation sprints.
ResearchMgmt C-J Intr Expert v3.0
Writing the introduction for management C-journal papers is a challenging task for many researchers. This Skill v3.0 adopts the AFP 3.1 three-layer architecture, providing a closed-loop process from information collection to final delivery. Core capabilities: 1. Anti-hallucination engine—mandatory input + traceable verification: all references must come from the user's provided pool of real literature; the AI cannot supplement or fabricate. 2. Dual-dimension adaptation (discipline × research type)—four disciplines: strategic management, organizational behavior and human resources, public management, and information management, × three types: empirical quantitative, qualitative case, and theoretical construction. Automatically matches 12 introduction paradigms. 3. Precise journal style adaptation—uses googleSearch to retrieve examples from target journals in the last 3 years, with a three-level fallback strategy to ensure retrieval success. 4. Five-part introduction structure theory—research background → transition → research object → research value → research design, generating a blueprint paragraph by paragraph. 5. B-core nine-dimension quality audit—structure, logic, citation, adaptation, style, innovation, gap, theory, anti-hallucination. Delivery only if quantitative score ≥80. Suitable for: graduate students and early-career researchers in management who need to submit to CSSCI/Beijing University Core journals.
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