Doomsday Grant Review Expert
Description
Step 8 (Final Review v2): Review and diagnose the grant proposal from start to finish, based on the original “Three-Line” review framework from Lecture 7—inner line (topic rationale → research subject → framework → key points → main objective: what to do), outer line (challenges → approach → research methods: how to do it), and side line (plan + feasibility + innovation + foundation + references + outcomes: whether it can be done, from the reviewer’s perspective). Includes the 30-second rule, three points that determine the outcome, a hidden-killer scan, the divide between key points and challenges, the main objective as the intersection of the inner and outer lines, four reviewer questions scored on a 5-point scale, and revision priorities.
Related Skills
View allNSSF App 'Three-Line' Review
Professional-grade deep review tool for NSSF proposals. From the perspective of an evaluator, based on an 85-item checklist three-line logic system (30 items for inner line, 25 for outer line, 30 for side line), integrating cutting-edge methodologies such as 'Three Dimensions of Topic Selection', 'Triangular Relationship of 300-Character Topic Explanation', 'Visualization of Technical Roadmap', and 'Logic-Driven Literature Review Method', it provides item-by-item diagnosis, problem identification, and optimization suggestions for proposals, ensuring alignment with evaluation standards and expert expectations. After completion, receive a review results report and an optimized full report.
Doomsday Research Content Gen
Expert skill for National Social Science Fund (NSSF) or Ministry of Education (MOE) humanities and social science project applications. Based on the methodology from the 4th lecture on 'Research Content' (five pillars, problem tree and problem chain, six techniques, and intuitive clarity), it generates research objects, research objectives (two versions: lecture style and expert standard style), overall framework, key points and difficulties, research plan, and feasibility through multi-round interaction. Each step requires the user to confirm and proceed.
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.
Doomsday Grant Review Expert
Description
Step 8 (Final Review v2): Review and diagnose the grant proposal from start to finish, based on the original “Three-Line” review framework from Lecture 7—inner line (topic rationale → research subject → framework → key points → main objective: what to do), outer line (challenges → approach → research methods: how to do it), and side line (plan + feasibility + innovation + foundation + references + outcomes: whether it can be done, from the reviewer’s perspective). Includes the 30-second rule, three points that determine the outcome, a hidden-killer scan, the divide between key points and challenges, the main objective as the intersection of the inner and outer lines, four reviewer questions scored on a 5-point scale, and revision priorities.
Related Skills
View allNSSF App 'Three-Line' Review
Professional-grade deep review tool for NSSF proposals. From the perspective of an evaluator, based on an 85-item checklist three-line logic system (30 items for inner line, 25 for outer line, 30 for side line), integrating cutting-edge methodologies such as 'Three Dimensions of Topic Selection', 'Triangular Relationship of 300-Character Topic Explanation', 'Visualization of Technical Roadmap', and 'Logic-Driven Literature Review Method', it provides item-by-item diagnosis, problem identification, and optimization suggestions for proposals, ensuring alignment with evaluation standards and expert expectations. After completion, receive a review results report and an optimized full report.
Doomsday Research Content Gen
Expert skill for National Social Science Fund (NSSF) or Ministry of Education (MOE) humanities and social science project applications. Based on the methodology from the 4th lecture on 'Research Content' (five pillars, problem tree and problem chain, six techniques, and intuitive clarity), it generates research objects, research objectives (two versions: lecture style and expert standard style), overall framework, key points and difficulties, research plan, and feasibility through multi-round interaction. Each step requires the user to confirm and proceed.
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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