Proposal Writing &Optimization
Description
Built on best practices for project proposal writing, this skill intelligently diagnoses existing content or helps you write from scratch. It automatically identifies your skill level and needs to provide personalized optimization suggestions or generate high-quality first drafts.
Related Skills
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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.
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.
Social Science Topic Selection
Helps researchers start from scratch to select a research topic, or diagnose and optimize an existing proposal. It addresses common pain points: lacking direction, struggling to formulate questions, difficulty judging value, and inability to express academically. Based on best practices in project proposal methodology, the skill provides step-by-step interactive guidance to produce high-quality topic proposals.
Proposal Writing &Optimization
Description
Built on best practices for project proposal writing, this skill intelligently diagnoses existing content or helps you write from scratch. It automatically identifies your skill level and needs to provide personalized optimization suggestions or generate high-quality first drafts.
Related Skills
View allResearch 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.
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.
Social Science Topic Selection
Helps researchers start from scratch to select a research topic, or diagnose and optimize an existing proposal. It addresses common pain points: lacking direction, struggling to formulate questions, difficulty judging value, and inability to express academically. Based on best practices in project proposal methodology, the skill provides step-by-step interactive guidance to produce high-quality topic proposals.
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