SCI Review Novelty Check – 慢老师
Description
The 慢老师 SCI Review Novelty Check skill is an academic tool specially optimized for depth. It helps researchers systematically and accurately assess whether the innovation points of their own SCI review articles are sufficiently novel and unique, avoiding duplicate publication. The skill strictly follows Professor Slow's T+D Innovation Paradigm through a complete 4-step process: collect 6 core pieces of information from the manuscript, generate multi-dimensional search strategies in Chinese and English, real-time comparison with highly similar literature from the past 5 years, and output a professional report. Ultimately, it produces a complete Chinese report covering three major sections: [Overall Novelty Judgment], [Innovation Points and Unique Contributions], and [Potential Risks and Countermeasure Suggestions].
Related Skills
View all
ResearchProf. Man's Innovation Eval
This skill is a complete replication of Professor Man's (Professor Man's Research World) 2025 released world-class prompt for 'Multi-dimensional Innovation Assessment of Top Journal SCI Papers'. After entering the paper title, core scientific question, core innovation strategy, and sub-innovation points, it automatically performs deconstruction of six innovation types, literature comparison benchmarking, multi-dimensional scoring (novelty, importance, transcendence, logic), and generates a complete evaluation report including a one-sentence conclusion, radar chart scores, strengths & risks analysis, submission suggestions, and Cover Letter phrasing. Suitable for researchers aiming for top journals such as Nature, Science, Nat. Energy, Joule, Adv. Mater., etc., to accurately diagnose innovation before submission and avoid blind submissions. Trigger words: top journal innovation assessment, SCI innovation evaluation, Professor Man innovation evaluation, Nature
慢老师 Review T+D Topic Picks
This is an intelligent tool designed specifically for researchers to choose a topic before writing a review. After you directly provide a research field (e.g., 'Electrochemical Energy Storage', 'Protein Structure Prediction', 'AI for Science', etc.), the skill systematically retrieves high-impact reviews, latest research hotspots, and research gap literature from the past 3-5 years. Strictly based on Professor Man's established T+D innovation paradigm (T = Innovation Themes: methods/structures/materials/mechanisms/systems/performances; D = Innovation Directions: time dimension/summary method/new framework integration), it recommends 3-5 review writing directions with the highest innovative potential.
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
SCI Review Novelty Check – 慢老师
Description
The 慢老师 SCI Review Novelty Check skill is an academic tool specially optimized for depth. It helps researchers systematically and accurately assess whether the innovation points of their own SCI review articles are sufficiently novel and unique, avoiding duplicate publication. The skill strictly follows Professor Slow's T+D Innovation Paradigm through a complete 4-step process: collect 6 core pieces of information from the manuscript, generate multi-dimensional search strategies in Chinese and English, real-time comparison with highly similar literature from the past 5 years, and output a professional report. Ultimately, it produces a complete Chinese report covering three major sections: [Overall Novelty Judgment], [Innovation Points and Unique Contributions], and [Potential Risks and Countermeasure Suggestions].
Related Skills
View allResearchProf. Man's Innovation Eval
This skill is a complete replication of Professor Man's (Professor Man's Research World) 2025 released world-class prompt for 'Multi-dimensional Innovation Assessment of Top Journal SCI Papers'. After entering the paper title, core scientific question, core innovation strategy, and sub-innovation points, it automatically performs deconstruction of six innovation types, literature comparison benchmarking, multi-dimensional scoring (novelty, importance, transcendence, logic), and generates a complete evaluation report including a one-sentence conclusion, radar chart scores, strengths & risks analysis, submission suggestions, and Cover Letter phrasing. Suitable for researchers aiming for top journals such as Nature, Science, Nat. Energy, Joule, Adv. Mater., etc., to accurately diagnose innovation before submission and avoid blind submissions. Trigger words: top journal innovation assessment, SCI innovation evaluation, Professor Man innovation evaluation, Nature
慢老师 Review T+D Topic Picks
This is an intelligent tool designed specifically for researchers to choose a topic before writing a review. After you directly provide a research field (e.g., 'Electrochemical Energy Storage', 'Protein Structure Prediction', 'AI for Science', etc.), the skill systematically retrieves high-impact reviews, latest research hotspots, and research gap literature from the past 3-5 years. Strictly based on Professor Man's established T+D innovation paradigm (T = Innovation Themes: methods/structures/materials/mechanisms/systems/performances; D = Innovation Directions: time dimension/summary method/new framework integration), it recommends 3-5 review writing directions with the highest innovative potential.
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
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