Academic Writing Engineering
Description
An engineering collaborative agent for scholarly manuscript writing across disciplines, covering monographs, textbooks, chapters, institutional texts, management manuals, practical guides, and academic practice texts in natural sciences, engineering, humanities and social sciences, business, law, education, medicine, and more. It does not directly write the main text but follows a seven-stage engineering workflow: task scope diversion → outline lock-in → resource compilation → logical review → section-by-section writing → section-by-section confirmation → full-chapter consolidation. This ensures stable chapter structure, sufficient supporting references, consistent terminology, and precise, plain language, delivering robust manuscripts suitable for formal publication and core journal style. Core capabilities: • Four task scope types: full book / single chapter / outline only / special tasks (consolidation, de-AI rewriting, review response, etc.) • Three approaches for top-level outline: user-provided, system-designed, or editorial board style lock-in • Three differentiated options for second/third-level outlines (normative basic / logic-enhanced / practice-applied) plus a compound recommendation mechanism • LAF-7 seven-dimensional logical review (system completeness, hierarchy symmetry, sentence standardization, title conciseness, cross-chapter repetition rate, terminology standardization, publishing suitability) • Word count weight and title density matching to prevent fragmentation from overly dense third-level headings • Reference compilation and online verification (policies, standards, regulations, literature, cases, data) • Gated manuscript writing (refuses to write if any gate condition is not met) • Section-by-section writing, section-by-section confirmation, full-chapter consolidation and final review • Expert-level de-AI rewriting: diagnosis (generic words, sentence regularity, mechanical connectors, concept stacking, missing context, terminology drift) + rewriting (alternating long/short sentences, qualifiers, reduce template numbering, strengthen causal chains and logical closure) Discipline adaptation: Automatically switches to the corresponding discipline's language style based on the user's academic background and terminology system (e.g., STEM focuses on mechanisms and experiments, business on strategy and cases, law on statutes and precedents, education on theory and classrooms, medicine on evidence-based practice and procedures, etc.) without imposing a fixed disciplinary framework. Target audience: Authors of academic monographs, textbook editors, series editorial committee members, industry practitioners, graduate and doctoral supervisors, researchers and professionals undertaking the writing of management manuals, institutional compilations, and practical guides.
Related Skills
View all
ResearchAcademic Paper Writing v4.1
A full-process academic paper writing system that combines the Five Sources model with the AFP 3.1 three-layer architecture. Other AI tools help you "write" a paper; this one helps you "plug" every hole that could get it rejected—from one vague idea to a complete submission package in a single run through seven modules, with an anti-hallucination gate, archive-code resume, and four-core review. [10-second value] One sentence of an idea → a submittable, complete paper package. Real sources are enforced throughout, every citation is verifiable, and you can resume across sessions right where you left off. [Trigger phrases] write a paper / literature review / research method design / how to write the discussion / paper polishing / abstract & keywords / submission package [Seven modules, all fully executable instructions] Phase 1 Topic & Introduction → Phase 2 Literature Review → Phase 3 Research Method Design → Phase 4 Discussion → Phase 5 Conclusion → Phase 6 Abstract & Keywords → Phase 7 Full-Text Integration & Submission Package. Phase 0 entry routes A–G map one-to-one to the seven modules, with an R option to restore your last progress via an archive code or archived document. [v4.1 upgrades] 1. Evidence-chain gate (hard constraint): a material registry (M1/M2 numbering) + four citation elements (author / year / title / verifiable locator) + three-color marking (✅ user-supplied / ⚠️ to verify / 🚫 prohibited). If the material library is empty, the system outputs only an outline and search queries and refuses to generate body text with citations; generating references from memory or fabricating volume, issue, or page numbers is forbidden. 2. Archive-code protocol: each module ends with a structured archive code that can be saved as a YouMind document. In your next session, simply reference it with @ to resume without re-requesting already locked information. 3. Reproducible scoring: the C core anchors all dimensions to behavioral standards scored at 4/6/8/10 with stated rationale; the ratchet mechanism only moves scores upward, never downward. 4. Four-discipline configuration library (1A Humanities & Arts / 1B Social Sciences / 2A Science & Engineering / 2B Agriculture, Medicine & Life Sciences), with automatic switching of theory libraries, method toolboxes, and disciplinary norms; Category 2B runs ethics review checkpoints at Phases 0, 3, and 7. 5. Three-paradigm routing (quantitative / qualitative / theoretical): literature review narrative strategies and research method design follow different paths. 6. Anti-cliché checklist and prohibitions: discussion sections using phrases like "limited time and energy" or "pending further research," conclusions introducing new data or literature, and abstracts containing claims unsupported by the body text are all sent back by the B core. 7. Adaptivity: user profile (beginner / intermediate / proficient) + paper type (journal / thesis / conference / course) + pacing mode (default / fast-forward / slow-motion) + multi-task isolation. 8. Submission package & compliance: seven consistency checks, a six-link logic chain, reference formatting (GB/T 7714 / APA 7 / Vancouver / IEEE), ethics approval number, data availability statement, generative AI use disclosure draft, and cover letter draft. [How to use] Tell the system: your discipline, target journal / paper type / word count, and which stage you're at. The system advances module by module, pausing after each one to deliver the section, review report, scorecard, and archive code, then waits for your confirmation before continuing. [Responsibility boundaries] The system handles sources 1–4 (structure, material organization, style, integration); source 5, human calibration, is yours: academic judgment, fact-checking, and innovation decisions. All ⚠️-marked citations and data must be verified by you item by item before submission; this skill does not promise any acceptance or pass outcome.
Academic Writing Advisor
Three-expert collaboration, six-dimension baseline, five-property comprehensive professional writing Agent (v2.1 Darwin optimized version, score 91). Core features: 1. Six-dimension content baseline [Way, Method, Technique, Tool, Change, and Trend] 2. Three-expert collaboration + three-gate audit 3. Chapter depth modeling (4 depth templates + quantitative anchors + six-dimension attribution mapping) 4. Five-property comprehensive constraint 5. Zero tolerance for hallucinations + three-level literature verification (V11 unified literature library) 6. Interrupted writing continuation (includes V12 six-dimension status + literature integration progress) 7. Mathematical formula Unicode symbol standards 8. Users can upload references/papers and forcibly integrate them 9. Five-state machine transitions + Pull interaction 10. Chapter-by-chapter saving and combined draft delivery + extra-long chapter volume mechanism 11. Illustration standards (C15, image requirements by six-dimension attribution) 12. C1/C2/C14 three-framework collaboration relationship description 13. V12 six-dimension coverage matrix tracking 14. Initialization six-dimension framework guidance 15. Degradation path for insufficient literature quality + six-dimension incompatibility exemption mechanism Supports automatic planning or user-uploaded table of contents, adapting to fields including computer science, AI, engineering, natural sciences, medicine, social sciences, etc.
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.
Academic Writing Engineering
Description
An engineering collaborative agent for scholarly manuscript writing across disciplines, covering monographs, textbooks, chapters, institutional texts, management manuals, practical guides, and academic practice texts in natural sciences, engineering, humanities and social sciences, business, law, education, medicine, and more. It does not directly write the main text but follows a seven-stage engineering workflow: task scope diversion → outline lock-in → resource compilation → logical review → section-by-section writing → section-by-section confirmation → full-chapter consolidation. This ensures stable chapter structure, sufficient supporting references, consistent terminology, and precise, plain language, delivering robust manuscripts suitable for formal publication and core journal style. Core capabilities: • Four task scope types: full book / single chapter / outline only / special tasks (consolidation, de-AI rewriting, review response, etc.) • Three approaches for top-level outline: user-provided, system-designed, or editorial board style lock-in • Three differentiated options for second/third-level outlines (normative basic / logic-enhanced / practice-applied) plus a compound recommendation mechanism • LAF-7 seven-dimensional logical review (system completeness, hierarchy symmetry, sentence standardization, title conciseness, cross-chapter repetition rate, terminology standardization, publishing suitability) • Word count weight and title density matching to prevent fragmentation from overly dense third-level headings • Reference compilation and online verification (policies, standards, regulations, literature, cases, data) • Gated manuscript writing (refuses to write if any gate condition is not met) • Section-by-section writing, section-by-section confirmation, full-chapter consolidation and final review • Expert-level de-AI rewriting: diagnosis (generic words, sentence regularity, mechanical connectors, concept stacking, missing context, terminology drift) + rewriting (alternating long/short sentences, qualifiers, reduce template numbering, strengthen causal chains and logical closure) Discipline adaptation: Automatically switches to the corresponding discipline's language style based on the user's academic background and terminology system (e.g., STEM focuses on mechanisms and experiments, business on strategy and cases, law on statutes and precedents, education on theory and classrooms, medicine on evidence-based practice and procedures, etc.) without imposing a fixed disciplinary framework. Target audience: Authors of academic monographs, textbook editors, series editorial committee members, industry practitioners, graduate and doctoral supervisors, researchers and professionals undertaking the writing of management manuals, institutional compilations, and practical guides.
Related Skills
View allResearchAcademic Paper Writing v4.1
A full-process academic paper writing system that combines the Five Sources model with the AFP 3.1 three-layer architecture. Other AI tools help you "write" a paper; this one helps you "plug" every hole that could get it rejected—from one vague idea to a complete submission package in a single run through seven modules, with an anti-hallucination gate, archive-code resume, and four-core review. [10-second value] One sentence of an idea → a submittable, complete paper package. Real sources are enforced throughout, every citation is verifiable, and you can resume across sessions right where you left off. [Trigger phrases] write a paper / literature review / research method design / how to write the discussion / paper polishing / abstract & keywords / submission package [Seven modules, all fully executable instructions] Phase 1 Topic & Introduction → Phase 2 Literature Review → Phase 3 Research Method Design → Phase 4 Discussion → Phase 5 Conclusion → Phase 6 Abstract & Keywords → Phase 7 Full-Text Integration & Submission Package. Phase 0 entry routes A–G map one-to-one to the seven modules, with an R option to restore your last progress via an archive code or archived document. [v4.1 upgrades] 1. Evidence-chain gate (hard constraint): a material registry (M1/M2 numbering) + four citation elements (author / year / title / verifiable locator) + three-color marking (✅ user-supplied / ⚠️ to verify / 🚫 prohibited). If the material library is empty, the system outputs only an outline and search queries and refuses to generate body text with citations; generating references from memory or fabricating volume, issue, or page numbers is forbidden. 2. Archive-code protocol: each module ends with a structured archive code that can be saved as a YouMind document. In your next session, simply reference it with @ to resume without re-requesting already locked information. 3. Reproducible scoring: the C core anchors all dimensions to behavioral standards scored at 4/6/8/10 with stated rationale; the ratchet mechanism only moves scores upward, never downward. 4. Four-discipline configuration library (1A Humanities & Arts / 1B Social Sciences / 2A Science & Engineering / 2B Agriculture, Medicine & Life Sciences), with automatic switching of theory libraries, method toolboxes, and disciplinary norms; Category 2B runs ethics review checkpoints at Phases 0, 3, and 7. 5. Three-paradigm routing (quantitative / qualitative / theoretical): literature review narrative strategies and research method design follow different paths. 6. Anti-cliché checklist and prohibitions: discussion sections using phrases like "limited time and energy" or "pending further research," conclusions introducing new data or literature, and abstracts containing claims unsupported by the body text are all sent back by the B core. 7. Adaptivity: user profile (beginner / intermediate / proficient) + paper type (journal / thesis / conference / course) + pacing mode (default / fast-forward / slow-motion) + multi-task isolation. 8. Submission package & compliance: seven consistency checks, a six-link logic chain, reference formatting (GB/T 7714 / APA 7 / Vancouver / IEEE), ethics approval number, data availability statement, generative AI use disclosure draft, and cover letter draft. [How to use] Tell the system: your discipline, target journal / paper type / word count, and which stage you're at. The system advances module by module, pausing after each one to deliver the section, review report, scorecard, and archive code, then waits for your confirmation before continuing. [Responsibility boundaries] The system handles sources 1–4 (structure, material organization, style, integration); source 5, human calibration, is yours: academic judgment, fact-checking, and innovation decisions. All ⚠️-marked citations and data must be verified by you item by item before submission; this skill does not promise any acceptance or pass outcome.
Academic Writing Advisor
Three-expert collaboration, six-dimension baseline, five-property comprehensive professional writing Agent (v2.1 Darwin optimized version, score 91). Core features: 1. Six-dimension content baseline [Way, Method, Technique, Tool, Change, and Trend] 2. Three-expert collaboration + three-gate audit 3. Chapter depth modeling (4 depth templates + quantitative anchors + six-dimension attribution mapping) 4. Five-property comprehensive constraint 5. Zero tolerance for hallucinations + three-level literature verification (V11 unified literature library) 6. Interrupted writing continuation (includes V12 six-dimension status + literature integration progress) 7. Mathematical formula Unicode symbol standards 8. Users can upload references/papers and forcibly integrate them 9. Five-state machine transitions + Pull interaction 10. Chapter-by-chapter saving and combined draft delivery + extra-long chapter volume mechanism 11. Illustration standards (C15, image requirements by six-dimension attribution) 12. C1/C2/C14 three-framework collaboration relationship description 13. V12 six-dimension coverage matrix tracking 14. Initialization six-dimension framework guidance 15. Degradation path for insufficient literature quality + six-dimension incompatibility exemption mechanism Supports automatic planning or user-uploaded table of contents, adapting to fields including computer science, AI, engineering, natural sciences, medicine, social sciences, etc.
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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