Melody Cary
Party building themed red-themed exquisite PPT generator
A professional, red-themed, exquisite PPT generator for Party building. This fully automated engine generates Party and government style PPTs for Party building work content. Input: Speech drafts/Work summaries/Study notes/Any Party building-related text. Output: Complete and deliverable professional and exquisite red-themed Party building style PPTs. ↓ ✓ Intelligent layering: Automatically identifies key points, difficulties, and highlights → Generates 6 standard pages (cover, chapter, content, prompts, tables, ending page) ✓ Party and government color scheme library: Chinese red, gold, and beige three-color system, with 16+ built-in layout schemes ✓ Real-time image search engine: Automatically retrieves and embeds real case images of Party building, ideological and political education, Youth League, and red themes ✓ Scene adaptation: One-click switching between Party building learning lectures/ideological and political education/Youth League reports/Young Pioneers work/policy interpretation ✓ Batch generation + automatic assembly: Lots of content? Automatically slices, generates pages, and seamlessly stitches them together. You copy the speech draft → select the application scenario → confirm the color scheme → output the PPT in 15 minutes, delivering a professional PPT with enterprise-level design standards.
Game Design Learning Coach
A professional, structured learning coach for game design. Based on a 5-stage path (understanding the game → mechanics to the loop → paper prototype and testing → system levels and narrative → portfolio mini-projects), it guides users from scratch to independently creating playable prototypes and compiling portfolios through diagnostic phases, concept explanations, exercise assignments, homework feedback, and resource recommendations. It includes a built-in MDA framework, a core glossary, an authoritative database, a list of accompanying videos, and diagnostic tools.
writeTeaching Achievement Award Application: Fully Automated Expert System
Teaching Achievement Award Application - Full-Process Automated Consultant System 🎯 Coverage Level Adaptive Recognition: Education Type: Basic Education / Vocational Education / Higher Education → Automatically Matches Application System Award Level: National / Provincial / School Level → Automatically Identifies Competition Difficulty and Focus 📊 Four Delivery Stages (Phase-Based Closed Loop): Table Stage Output Content Core Value 1. Diagnostic Filing 7-8 Questionnaires + Achievement Positioning Report Accurately identifies the application level, avoiding overestimation or underestimation 2. Topic Selection Topic direction matrix + 3-5 similar successful cases Know which direction to revise to for the highest visibility 3. Topic Incubation 5-8 alternative topics (SCPAR naming) Once the topic is right, half the application is correct 4. Main Text Writing A complete version of the application with strict word count control How many words should be allocated to innovation points, achievements, and promotional value—precise to the paragraph level 5. Diagnostic Scoring Three-dimensional innovation assessment + expert review checklist Reviewing it yourself after revision is equivalent to professional review 🔧 Built-in Standardized Tool Library: ✓ SCPAR Naming Convention—An implicit scoring sheet for teaching achievement titles ✓ Hard word count constraint verification—Automatic positioning within the 5000-12000 word range ✓ Three-dimensional innovation evaluation model—Complete reproduction of the scoring logic of teaching achievement judges ✓ Title template library—Reference templates for national/provincial/school-level achievements ✓ Expert review checklist—Revised application forms for item-by-item self-checking📈 Expected results: Application success rate from random 30% → to precise 70%+ Application preparation time from 2-3 months → 2-3 weeks per submission, higher hit rate (more accurate topic selection)
AFP: A powerful tool for selecting research topics in the humanities and social sciences
Humanities and Social Sciences Research Topic Selection Engineering System 🎯 Adaptive Coverage System: Table-based Topic Type System Adaptability Project Approval Difficulty National Social Science Fund ✅ Core Application Scenarios ★★★★★ Ministry of Education Humanities and Social Sciences Planning ✅ Fully Adaptable ★★★★☆ National Educational Science Planning Key Projects ✅ Fully Adaptable ★★★★☆ Provincial Social Science Planning ✅ Fully Adaptable ★★★☆☆ Vocational Education/Teaching Reform Projects ✅ Fully Adaptable ★★★☆☆ 📊 Six-Stage Delivery Process (P0-P5 Mandatory Step-by-Step): Table Stage Step Core Output Key Actions P0 Baseline Anchoring Personal Resources & Position Assessment Clearly assess your academic accumulation, existing achievements, and team resources P1 Policy Decoding and Real Problem Extraction Policy Analysis Table + Problem List Scan the latest policy documents to uncover the issues the review committee truly cares about P2 Atomic-Level Object Dimensionality Reduction Minimal Granularity Object Table Break down grand propositions into the most core research objects P3 Nine-Square Grid Topic Selection Explosion 9 Topic Selection Directions Matrix: One object, 9 angles, selecting the optimal direction. P4: Critical Audit to finalize the topic. Modification suggestion sheet + risk list. Dual-role confrontation: Applicant vs. blind review expert, repeated refinement. P5: Asset Packaging: "Topic Selection Core Asset Memorandum". Final title + argumentation logic + directly connectable to application writing. 🔧 Built-in standardized tool library: ✓ Dual-role confrontation system—Project planner ↔ Blind review expert (self-PK) ✓ Naming red line check—Strictly prohibit colons/dashes/subtitles; must end with "Research" ✓ Three-in-one rule—Qualifier + Object + Question (implicit scoring table) ✓ Real-time policy library updates—Synchronized with the latest guidelines from the National Social Science Fund, Ministry of Education, and Educational Science Planning ✓ Nine-square grid topic selection matrix—Systematically unfolds 9 angles, visually comparing innovation ✓ Blind review risk assessment—Predict the weaknesses of the topic in the review process. 📈 Expected results: 📌 Project success rate— From an average of 20-30% to 60-70%+ (depending on prior preparation) ⏱️ Topic selection cycle—from 3-6 months of topic refinement to 2-3 weeks of systematic finalization🎯 Accuracy—from "casting a wide net" to precisely targeting the committee's review logic📦 Continuity—The "Topic Selection Memorandum" can be directly linked to "AFP (Approval Process for Research Proposals) writing," creating a complete closed loop.
AFP Engineering Prompt Generator - Master-Level System Architecture
AFP Engineering Prompt Generator - Master-Level System Architecture 🎯 Core Positioning: A meta-level prompt engineering system—the foundational architecture used to generate other AFP skills. The entity is both an independent product and the core of AFP skills across various domains. 📊 Core Capability Matrix: Table Capability Dimensions System Support Application Scenarios Task Adaptive Recognition ✅ Automatic recognition of 8+ types of tasks including classification, generation, analysis, and decision-making. No need to manually specify task type; automatic complexity trimming. ✅ Automatic adjustment of components based on task complexity (1 layer/multi-layer/conditional branch). Avoids over-design and omission of key elements. AFP design gene encapsulation. ✅ Meets engineering prompt word standards (input specifications → processing flow → output verification). Generated prompt words are naturally high-quality and support multiple modes. ✅ Seamless switching between independent use and being called. Can be deployed independently or used as a foundation for other skills; cross-domain reusability. ✅ Automatically splits general parts/domain-specific parts. The same framework can be applied across 5+ domains. Large model compatibility. ✅ Universal for ChatGPT/Claude/GPT-4/domestic large models. Generated prompt words are not bound to a single model. 🔧 Technical Architecture: First Layer: Task Analysis Engine Natural Language Understanding User-input task description Automatically classifies task type (generational/analytical/decision-making/creative, etc.) Calculates task complexity score. Second Layer: The component library management includes 50+ built-in engineered prompt components (role setting, input specifications, process design, exception handling, etc.), categorized by complexity level (L0 Simple/L1 Medium/L2 Advanced/L3 Expert). It supports selective assembly and custom expansion of components. The third layer, AFP framework generation, organizes prompt structures according to AFP design principles (functional layering → process orchestration → output formatting), automatically generating complete instruction chains. Built-in quality checks (coverage, redundancy, consistency checks) are also included. The fourth layer, output and integration, generates plain text prompts (ready to use directly) and structured configuration files (callable by programs). Version management and iterative optimization are supported. Expected performance indicators: Table Indicators | Improvement | Description Prompt Design Cycle | From 1-2 weeks → 10-30 minutes | From manual design to automatic generation | Output Quality Stability | From 70-80% → 85-92% | Engineered design is inherently more stable | Cross-domain Reusability | From 30% → 80%+ | Automatic separation of general and special parts | Team Learning Cost | From 3-6 months to master → 1-2 weeks to get started Newcomers can quickly reuse high-quality frameworks, reducing the migration cost of large models. The framework is stable, requiring no major modifications for model upgrades, moving from a complete rewrite to minor tweaks.