Win Review: 6-Step Mastery
Description
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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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.
Research Proposal Assistant
Tailored for university faculty and researchers, this AI assistant provides comprehensive guidance in writing humanities and social science research proposals, covering the entire process from topic generation to outcome planning. Whether you need to brainstorm a topic from scratch or optimize a specific section of your proposal, this tool offers expert-level guidance and support. This assistant combines expert experience with a strategy of 'example guidance + writing theory integration' to help you efficiently produce high-quality proposals. You receive targeted analysis of research hotspots in your discipline, topic refinement suggestions, and—based on your research direction and project type—generate a rigorous background, in-depth literature review, and insightful research value statement. In the research content design phase, the assistant helps you define the research subject, build a logically clear research framework, and recommend innovative research ideas and methods. It also helps you distill key points and difficulties, set clear research objectives, and plan a detailed research schedule and feasibility analysis, ensuring your proposal is rigorous and well-structured. Additionally, you can use this tool to deeply explore the innovative aspects of your topic in terms of academic ideas, viewpoints, and research methods, and systematically plan multiple forms of expected outcomes, their applications, and social benefits. Finally, all content is integrated with one click to generate a complete, logically rigorous proposal that meets submission standards, helping you increase your success rate.
WriteEnd-First Humanities Proposals
The biggest risk in a proposal isn’t writing slowly—it’s heading in the wrong direction. Most methods have you “write from start to finish, following the research process.” This Skill takes the opposite approach—backward first, then forward: it pushes you to clarify “what exactly is the innovation,” then work backward from the reviewers’ perspective to determine “what needs to be shown and why they should believe you.” Only after that does it have you write in a logical reading order. It sets three hard gates: you can’t lock in a topic until you’ve searched the literature thoroughly, you can’t start writing until the innovation is clear, and the final draft can’t be completed without your confirmation. It also uses XYZ multi-axis searches to verify that a “research gap” truly exists—never treating “I didn’t find it” as “no one has studied it.” Other AI helps you write faster. This Skill has you start later, so you get it right the first time.
Win Review: 6-Step Mastery
Description
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.
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.
Research Proposal Assistant
Tailored for university faculty and researchers, this AI assistant provides comprehensive guidance in writing humanities and social science research proposals, covering the entire process from topic generation to outcome planning. Whether you need to brainstorm a topic from scratch or optimize a specific section of your proposal, this tool offers expert-level guidance and support. This assistant combines expert experience with a strategy of 'example guidance + writing theory integration' to help you efficiently produce high-quality proposals. You receive targeted analysis of research hotspots in your discipline, topic refinement suggestions, and—based on your research direction and project type—generate a rigorous background, in-depth literature review, and insightful research value statement. In the research content design phase, the assistant helps you define the research subject, build a logically clear research framework, and recommend innovative research ideas and methods. It also helps you distill key points and difficulties, set clear research objectives, and plan a detailed research schedule and feasibility analysis, ensuring your proposal is rigorous and well-structured. Additionally, you can use this tool to deeply explore the innovative aspects of your topic in terms of academic ideas, viewpoints, and research methods, and systematically plan multiple forms of expected outcomes, their applications, and social benefits. Finally, all content is integrated with one click to generate a complete, logically rigorous proposal that meets submission standards, helping you increase your success rate.
WriteEnd-First Humanities Proposals
The biggest risk in a proposal isn’t writing slowly—it’s heading in the wrong direction. Most methods have you “write from start to finish, following the research process.” This Skill takes the opposite approach—backward first, then forward: it pushes you to clarify “what exactly is the innovation,” then work backward from the reviewers’ perspective to determine “what needs to be shown and why they should believe you.” Only after that does it have you write in a logical reading order. It sets three hard gates: you can’t lock in a topic until you’ve searched the literature thoroughly, you can’t start writing until the innovation is clear, and the final draft can’t be completed without your confirmation. It also uses XYZ multi-axis searches to verify that a “research gap” truly exists—never treating “I didn’t find it” as “no one has studied it.” Other AI helps you write faster. This Skill has you start later, so you get it right the first time.
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