Multi-View Paper Review Expert
Description
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (Editor-in-Chief + 3 peer reviewers + 1 adversarial critique expert) with domain expertise. Supports full review, re-review (verification), quick assessment, research method focus, Socratic guided review, and calibration mode. Trigger commands: review paper, peer review, manuscript review, reviewer report, review my paper, paper evaluation, simulation review, editorial review, calibrate reviewers, reviewer calibration, evaluate review accuracy.
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Journal Pre-review Expert V7.0
Top Journal Submission Pre-review Expert V7.0 is a full-process pre-review Skill for social sciences and humanities scholars. It simulates the perspective of senior reviewers at Nature/Science/ASR/AJS-level journals, driven by a dual-core engine (Reviewer Butcher + Polishing Craftsman) across six phases. V7.0 core upgrades: · Governance layer: unified dashboard + step mode (default/fast-forward/slow-motion) + ratchet version management + mid-join logic · All 15 quick commands implemented (/express /jump /benchmark /ethics-scan /cover-letter /polish /abstract /checklist /audit /resume /skip /rerun /export /persona /lang) · Cross-phase vulnerability tracking table: tracks from Phase 2 to Phase 5 with status closure · 6 virtual reviewer personas (methodology hawk, theoretical purist, contextualist, ethics reviewer, etc.), each with 4-5 typical criticisms, automatically activating 3-4 based on research type · Full language pair support: Chinese→English, English→English, Chinese→Chinese, with Chinglish terminator engine covering 7 categories of high-frequency errors · Benchmark paper five-dimensional comparison + no-benchmark degradation path · New AI usage transparency check and citation quality deep audit Applicable scenarios: PhD students/early-career faculty pre-submission self-check, advisors guiding students to revise manuscripts, research group submission strategy discussions. Supports full-process guidance for Chinese native speakers submitting in English.
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.
Grant Proposal Reviewer v2.0
A project proposal review expert system built on a super prompt architecture. It supports types such as the National Natural Science Foundation, National Social Science Fund, and provincial and ministerial research projects. Equipped with a built-in dual-core review engine (dual evaluation of academic value and feasibility), multi-dimensional scoring system, problem diagnosis and improvement suggestions, and academic norm checking, it simulates the real expert review process to help applicants identify issues and improve quality before submission.
Multi-View Paper Review Expert
Description
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (Editor-in-Chief + 3 peer reviewers + 1 adversarial critique expert) with domain expertise. Supports full review, re-review (verification), quick assessment, research method focus, Socratic guided review, and calibration mode. Trigger commands: review paper, peer review, manuscript review, reviewer report, review my paper, paper evaluation, simulation review, editorial review, calibrate reviewers, reviewer calibration, evaluate review accuracy.
Related Skills
View allJournal Pre-review Expert V7.0
Top Journal Submission Pre-review Expert V7.0 is a full-process pre-review Skill for social sciences and humanities scholars. It simulates the perspective of senior reviewers at Nature/Science/ASR/AJS-level journals, driven by a dual-core engine (Reviewer Butcher + Polishing Craftsman) across six phases. V7.0 core upgrades: · Governance layer: unified dashboard + step mode (default/fast-forward/slow-motion) + ratchet version management + mid-join logic · All 15 quick commands implemented (/express /jump /benchmark /ethics-scan /cover-letter /polish /abstract /checklist /audit /resume /skip /rerun /export /persona /lang) · Cross-phase vulnerability tracking table: tracks from Phase 2 to Phase 5 with status closure · 6 virtual reviewer personas (methodology hawk, theoretical purist, contextualist, ethics reviewer, etc.), each with 4-5 typical criticisms, automatically activating 3-4 based on research type · Full language pair support: Chinese→English, English→English, Chinese→Chinese, with Chinglish terminator engine covering 7 categories of high-frequency errors · Benchmark paper five-dimensional comparison + no-benchmark degradation path · New AI usage transparency check and citation quality deep audit Applicable scenarios: PhD students/early-career faculty pre-submission self-check, advisors guiding students to revise manuscripts, research group submission strategy discussions. Supports full-process guidance for Chinese native speakers submitting in English.
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.
Grant Proposal Reviewer v2.0
A project proposal review expert system built on a super prompt architecture. It supports types such as the National Natural Science Foundation, National Social Science Fund, and provincial and ministerial research projects. Equipped with a built-in dual-core review engine (dual evaluation of academic value and feasibility), multi-dimensional scoring system, problem diagnosis and improvement suggestions, and academic norm checking, it simulates the real expert review process to help applicants identify issues and improve quality before submission.
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