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Comprehensive LLM learning assistant. Use when studying transformer architecture, attention mechanisms, pre-training, fine-tuning, or prompt engineering. Helps with understanding LLM principles and practical applications.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
PPTX to Markdown 转换。Use when (1) 将 .pptx 转换为 .md, (2) 提取 PPT 内容和截图, (3) 需求文档/修改事项 PPTX 解析, (4) 包含标注截图的 PPTX 处理, (5) 批量转换演示文稿
专业的AI Agent(AI Agents)顾问助手，探索 AI Agent 框架和应用。当用户询问以下问题时使用：(1) 技术选型和对比 (2) 使用指南和最佳实践 (3) 问题诊断和解决 (4) 资源推荐 (5) 常见问题解答
Comprehensive CV learning assistant. Use when studying image processing, object detection, segmentation, or any CV tasks. Helps with algorithm understanding, implementation, and model optimization.
Comprehensive DL learning assistant. Use when studying neural networks, CNN, RNN, LSTM, Transformer, or any DL architectures. Helps with network design, training strategies, debugging, and optimization techniques.
Comprehensive ML learning assistant. Use when studying supervised learning, unsupervised learning, regression, classification, clustering, or any ML concepts. Helps with algorithm understanding, implementation guidance, model evaluation, and practical applications.
Comprehensive MV learning assistant for industrial computer vision applications. Use when studying image processing, feature extraction, object detection, quality inspection, or automation systems. Helps with (1) concept explanation with real-world examples, (2) OpenCV code analysis and debugging, (3) homework guidance without direct answers, (4) lab experiment setup and troubleshooting, (5) quiz generation for self-assessment, (6) knowledge summarization and review materials, (7) vision system design and optimization, (8) research paper reading and comprehension, (9) generating MV lab code with bilingual comments.
| name | LLM Learning Assistant |
| description | Comprehensive LLM learning assistant. Use when studying transformer architecture, attention mechanisms, pre-training, fine-tuning, or prompt engineering. Helps with understanding LLM principles and practical applications. |
Explain complex concepts in simple terms with real-world analogies and visual descriptions.
Analyze and explain algorithm implementations, trace execution flow, and identify key components.
Help understand assignment requirements and develop solution approaches without providing direct answers.
Guide through hands-on experiments with step-by-step instructions and result interpretation.
Create practice questions and exercises with detailed explanations to test understanding.
Generate concise summaries, flashcards, and knowledge maps for efficient review.
Provide guidance on project selection, design, implementation, and optimization.
Help understand research papers by extracting key insights and explaining complex formulations.
Add specific content for each capability:
See references/ directory for detailed documentation and examples.
