Dual-Core Proposal Reviewer
Dual-Core Review: Worth Doing & I Can Do
Description
Why we love this skill
This skill centers on 'Worth Doing' and 'I Can Do', and like a review expert, it cross-examines your grant proposal, providing a prioritized revision checklist and example rewrites to boost your application success rate.
Using 'Worth Doing' and 'I Can Do' as the dual-core propositions, this skill conducts a cross-examining review of your draft project proposal. It systematically evaluates the value of the topic (genuine question, incremental contribution, contemporary relevance) and research capability (prior accumulation, plan feasibility, team conditions), automatically scans for red-flag expressions like 'fill the blank', tests the tension between the two cores, and outputs a prioritized revision checklist. Applicable to optimizing project proposals at various levels including National Social Science Fund, Provincial Social Science Planning, and Ministry of Education Humanities and Social Sciences projects.
Related Skills
View all
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.
Win Review: 6-Step Mastery
Based on Zhao Xin’s *AI Breakthrough for Project Proposals: A Practical Six-Step Method for Project Approval*, this Skill uses the six-step framework of “Analyze the Brief—Find the Question—Substantiate the Question—Frame the Argument—Solve the Problem—Establish the Proposal” to co-create with AI rather than write for you. From understanding the field to final refinement, it systematically strengthens your project proposal. It includes core methodologies such as the TMAQ model, ADFV topic-selection method, four-dimensional evaluation, and four rounds of iterative revision, along with diagnostic prompts modeled on a “demanding expert reviewer.” These tools help applicants clarify their research, build a compelling argument, and polish their writing to gain an edge in front of reviewers.
Dual-Core Proposal Reviewer
Dual-Core Review: Worth Doing & I Can Do
Description
Why we love this skill
This skill centers on 'Worth Doing' and 'I Can Do', and like a review expert, it cross-examines your grant proposal, providing a prioritized revision checklist and example rewrites to boost your application success rate.
Using 'Worth Doing' and 'I Can Do' as the dual-core propositions, this skill conducts a cross-examining review of your draft project proposal. It systematically evaluates the value of the topic (genuine question, incremental contribution, contemporary relevance) and research capability (prior accumulation, plan feasibility, team conditions), automatically scans for red-flag expressions like 'fill the blank', tests the tension between the two cores, and outputs a prioritized revision checklist. Applicable to optimizing project proposals at various levels including National Social Science Fund, Provincial Social Science Planning, and Ministry of Education Humanities and Social Sciences projects.
Related Skills
View allGrant 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.
Win Review: 6-Step Mastery
Based on Zhao Xin’s *AI Breakthrough for Project Proposals: A Practical Six-Step Method for Project Approval*, this Skill uses the six-step framework of “Analyze the Brief—Find the Question—Substantiate the Question—Frame the Argument—Solve the Problem—Establish the Proposal” to co-create with AI rather than write for you. From understanding the field to final refinement, it systematically strengthens your project proposal. It includes core methodologies such as the TMAQ model, ADFV topic-selection method, four-dimensional evaluation, and four rounds of iterative revision, along with diagnostic prompts modeled on a “demanding expert reviewer.” These tools help applicants clarify their research, build a compelling argument, and polish their writing to gain an edge in front of reviewers.
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