钟小波
AI Job Search Assistant
A full-stack job search assistant, covering a complete closed loop from profile creation, job search, fit assessment, CV/cover letter customization, interview preparation to result tracking. Based on the AFP architecture, it adopts a drafting-review dual-agent adversarial mechanism, with built-in honesty red lines (no fabricated skills/experiences), ATS keyword compliance checks, relevance-weighted CV trimming, and supports reverse calibration of the assessment framework from actual application results.
Professional productization process
An end-to-end process for productizing professional experience and transforming it into personal IP content and skills delivery: Needs and trust diagnosis → IP anchoring → Minimum distributable product → Content master draft → Multi-platform tailoring → Distribution skills configuration → Publication verification → Optional visual output. Suitable for lawyers, consultants, teachers, researchers, and other experts who want to turn their professional capabilities into content, products, skills, and monetization pathways.
speech coach
From scenario analysis and script development to voice and body language optimization, a one-stop speech coach.
Lesson Preparation Transcript V1.0
This skill helps instructors/knowledge IPs directly transform their prepared lesson topics into immediately usable 30-minute scripts for spoken delivery—ready to read aloud, not just secondary prompts. It addresses the traditional lesson preparation process where instructors must complete "idea → module breakdown → script writing → pacing → interaction design," which is time-consuming and prone to omissions. This skill compresses this process into 5 interactive steps; instructors only need to provide the topic and knowledge points, and the rest is automated. Core capabilities: ① Five-step structured interaction: Course type selection → Receiving lesson content → In-depth analysis and option presentation → Style preference inquiry → Direct generation of complete spoken delivery script. Each step requires user confirmation before proceeding to the next. ② CXO three-dimensional teaching verification: Each module is labeled with teaching dimensions—C (content/knowledge points), X (experience/practice), O (goals/outputs), ensuring complete coverage. ③ Spoken delivery script template: Each section follows a four-element framework: key points → main points → structure → call to action. The structure supports four modes: cause/method/situation response/time sequence. ④ The complete output includes: approximately 7,900 words of spoken text across 7 modules + tone prompts (【2-second pause】【emphasis】) + student interaction instructions + CXO paragraph annotations + full statistics. ⑤ 7-dimensional quality control: word count deviation / speaking style / template completeness / CXO coverage / logical coherence / pacing / reusability – if any item fails, it must be corrected and re-output.
Project Proposal Creation, Review, and Polishing PRO V2.0
🎯 Core Function Overview This is an intelligent review and optimization system specifically designed for applications for national social science, Ministry of Education, and provincial-level research projects. It simulates the thinking mode of a senior review expert with 15 years of experience and ensures the academic rigor and competitiveness of applications through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ A 12-step structured methodology fully covers the entire lifecycle of research proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcements (funding guidance, review standards, application requirements) - Interdisciplinary type judgment (precise identification of 8 types) - Research GAP five-dimensional identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - TMAQ model analysis of research questions (four dimensions of theory/methodology/ideas/problems) - SMART principle verification of research objectives - Completeness assessment of research content framework - Matching of research ideas (6 types) Phase 8-10: In-depth Quality Improvement - Precise extraction of key difficulties (differentiation criteria + breakthrough path) - 7-dimensional exploration of innovation points - 7-dimensional feasibility demonstration Phase 11-12: Overall Optimization - 9-dimensional quality detection (academic rigor, innovation, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-core confrontation mechanism (Builder vs Supervisor) Working principle: - Builder (Academic Writer): Generates optimized solutions based on user materials - Supervisor (Top Journal Reviewer): Challenges the Builder's solutions with the most stringent standards - Iterative Challenges: Ensures solutions withstand scrutiny through 3 rounds of challenges. Application Scenarios: - Innovation Point Discovery: Builder proposes innovation points → Supervisor questions their novelty → Iterative optimization - Feasibility Demonstration: Builder designs solutions → Supervisor challenges their feasibility → Supplementary demonstration - Literature Citation: Builder cites literature → Supervisor verifies authenticity → Ensures academic integrity 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Uses markers such as [Literature Placeholder-001] to replace specific literature - Outputs a "Literature Requirement List", clarifying the search requirements for each placeholder - Users fill in the actual literature after their own search Mode B: Real-Time Verification Mode - Calls Google Scholar to verify literature authenticity in real time - Generates a "Literature Verification Report" (authenticity/relevance/authoritativeness score) - Ensures every citation is traceable Prevents AI Illusions: - Prohibits fabricating authors, journals, and DOIs out of thin air - All literature must be verified or marked as placeholders - ensuring the bottom line of academic integrity 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Solved 1. Lack of Academic Rigor: AI-generated content often contains fake literature and logical loopholes 2. Insufficient Innovation: Difficulty in uncovering genuine academic innovation points 3. Weak Feasibility: Research plans lack systematic argumentation 4. Difficulty in Interdisciplinary Research: Interdisciplinary topics are prone to being "neither fish nor fowl" 🎓 Applicable Users - University teachers (social sciences, education, humanities) - Researchers (applying for national and provincial-level projects) - Academic teams (requiring a systematic review process) 📋 Typical Usage Process 1. Input: Upload project announcement + draft application 2. Review: The system performs a 12-step structured analysis 3. Countermeasures: Dual-core mechanism iteratively optimizes key parts 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimensions | Traditional Manual Review | Expert Review System | |------|------------|---------| | Review Depth| Reliance on Personal Experience| 12-Step Structured Review + 9-Dimensional Quality Inspection| | Academic Rigor| Difficult to Fully Verify| Literature Verification + Dual-Core Countermeasures| | Innovation Mining| Subjective Judgment| 7-Dimensional System Analysis| | Feasibility Demonstration| Experience-Driven| 7-Dimensional Item-by-Item Demonstration| | Consistency| Personalized| Standardized Process| | Efficiency| Several Days to Several Weeks| Initial Review Completed in 1-2 Hours| The core advantage of this system lies in: making the tacit knowledge of 15 years of senior review experts explicit, structured, and replicable, allowing every user to obtain top-level expert review services.
一、研究意义
(一)理论意义
新质生产力作为马克思主义生产力理论在新时代的创新发展,为理解数字经济时代的生产力跃迁提供了新的理论框架。本研究从统计学视角切入,具有三方面理论价值:
第一,拓展新质生产力的测度理论。现有研究多停留在概念阐释与定性分析层面,本研究通过构建多维统计测度指标体系,将抽象的理论概念转化为可操作的量化工具,为新质生产力的实证研究提供方法论支撑。这一转化不仅回应了"如何测度新质生产力"这一基础性理论问题,也为后续跨区域比较研究奠定基础。
Project Proposal Creation, Review, and Polishing v1.1
A project proposal review and polishing expert system. It features a dual-core adversarial engine for real-time review, a literature review mechanism to prevent fabricated citations, and a three-stage workflow (creation → diagnosis → polishing).
一、优化概览
针对《广州新质生产力发展中人工智能等产业新赛道的多维度研究——基于体育科技与统计测度的双重视角》课题申报书，基于课题申报书评审专家的专业评审意见，已完成5大关键优化，解决了评审中发现的6个核心问题。
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二、已完成的关键优化
Project Proposal Review Expert v2.0 (Dual-core Engine Version)
This is a project application review expert system built on a super-prompt keyword architecture. It supports various types of research projects, including the National Natural Science Foundation of China, the National Social Science Foundation of China, and provincial and ministerial-level research projects. It features a built-in dual-core review engine (dual assessment of academic value and feasibility), a multi-dimensional scoring system, problem diagnosis and improvement suggestions, and academic compliance checks. Simulating a real expert review process, it helps applicants identify problems and improve quality before submission.