CN Patent Disclosure Generator
CN patent disclosure generation Agent Skill
CN Patent Disclosure Generator
CN patent disclosure generation Agent Skill
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Description
From project documents to deliverable technical disclosure: full process of patent point mining, online novelty search (prioritizing CNIPA + Google Patents/Scholar dual channel), desensitization drafting, and self-check closure. Adapted to YouMind platform toolchain, automatically scanning kanban materials, outputting disclosure document + independent novelty search report, supporting generateDiagram diagram generation, iterative revision, and version management. Built-in P1-P7 core protocols (factual integrity/auto desensitization/version protection/confirmation gate/self-check isolation/novelty search positioning/social science context).
Why we love this skill
Automatically generates high-quality patent technical disclosures, from patent point mining to novelty search and self-check, ensuring rigor and innovation. An essential tool for patent applications.
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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.
Patent Assistant
label: Patent Assistant description: Provides full-process patent support for R&D staff, covering 5 major scenarios: (1) Writing invention disclosure documents, (2) Writing Chinese invention patent application documents, (3) Patent search and novelty check (generate Chinese and English search keywords + IPC suggestions), (4) Responding to Office Actions (OA), (5) Chinese-English patent translation. Trigger keywords: patent, disclosure, claims, specification, patent search, novelty check, office action, OA, PCT, patent translation, food patent, process patent, packaging patent, formula patent, device patent.
Academic Writing Engineering
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.
Information
- Version
- v2
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
