Menü
"深度阅读和分析 PDF 论文。支持 PDF 转 Markdown、智能摘要、关键信息提取、问答式学习、笔记生成。"
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
"自动化论文检索、翻译、导入和学习闭环系统。每日从 arXiv 检索论文、自动翻译标题和摘要、导入 Zotero、生成 Obsidian 笔记和学习报告。"
"生成各种类型的图表和流程图：流程图、时序图、类图、甘特图、思维导图等。支持 Mermaid、PlantUML、Graphviz 等格式。"
"GitHub CLI (gh) 自动化操作。管理仓库、Issues、Pull Requests、Workflows、Releases 等。支持所有 gh 命令。"
"深度反思学习过程，巩固知识，发现盲点，优化学习策略。基于笔记内容生成反思报告、学习建议、知识图谱。"
"从 arXiv 自动检索论文、翻译标题摘要、导入 Zotero、生成 Obsidian 笔记。支持关键词检索、自动去重、中文翻译。"
"生成论文学习报告：每日总结、每周总结、月度报告、季度报告、年度总结。统计学习进度、分析研究方向、追踪知识积累。"
| name | PDF Reader |
| description | "深度阅读和分析 PDF 论文。支持 PDF 转 Markdown、智能摘要、关键信息提取、问答式学习、笔记生成。" |
Use this skill when you want to:
Triggers:
阅读 PDF: D:\Papers\attention-is-all-you-need.pdf
阅读 arXiv 论文: 2303.12345
这篇论文的主要贡献是什么？
python {workspace}/PaperVault/scripts/pdf-reader.py --pdf path/to/paper.pdf
Converts PDF to structured Markdown:
Input: PDF file
Output: {paper-name}.md
Conversion includes:
Example Output:
---
title: "Attention Is All You Need"
arxiv_id: "1706.03762"
date_read: 2024-03-25
reading_time: 45min
difficulty: ⭐⭐⭐☆☆
---
# Attention Is All You Need
> [!abstract] 摘要
> The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...
## 1. Introduction
Recurrent neural networks, long short-term memory and gated recurrent neural networks...
### 1.1 Background
The Transformer uses multi-headed self-attention...
## 2. Model Architecture
### 2.1 Encoder and Decoder Stacks
**Encoder**: The encoder is composed of a stack of N = 6 identical layers...
**Decoder**: The decoder is also composed of a stack of N = 6 identical layers...
## 3. Attention
$$
\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
$$
## 4. Experiments
| Model | BLEU | Training Time |
|-------|------|---------------|
| Transformer (big) | 28.4 | 3.5 days |
| Transformer (base) | 27.3 | 12 hours |
## Key Insights
1. Self-attention allows modeling of dependencies regardless of distance
2. Multi-head attention enables attending to information from different positions
3. Positional encoding is necessary since the model contains no recurrence
## Questions
- [ ] Why divide by sqrt(d_k)?
- [ ] How does multi-head attention work in detail?
- [ ] What are the computational complexity trade-offs?
## References
1. Vaswani et al. (2017) - This paper
2. Bahdanau et al. (2015) - Attention mechanism
3. Gehring et al. (2017) - Convolutional sequence models
Generates multiple levels of summaries:
1-sentence summary: "Proposes Transformer, a purely attention-based architecture for sequence transduction."
1-paragraph summary: "The paper introduces the Transformer model, which relies entirely on self-attention mechanisms without recurrence or convolution. It achieves state-of-the-art performance on machine translation tasks with significantly reduced training time."
Key points summary:
Extracts structured information:
Contributions:
{
"contributions": [
"Proposed Transformer architecture",
"Introduced multi-head self-attention",
"Achieved SOTA on WMT 2014",
"Reduced training time significantly"
]
}
Methods:
{
"methods": [
{
"name": "Scaled Dot-Product Attention",
"equation": "Attention(Q,K,V) = softmax(QK^T / sqrt(d_k))V",
"purpose": "Compute attention weights efficiently"
},
{
"name": "Multi-Head Attention",
"description": "Run multiple attention operations in parallel",
"benefit": "Attend to information from different positions"
}
]
}
Experiments:
{
"experiments": [
{
"task": "Machine Translation",
"dataset": "WMT 2014 English-German",
"metric": "BLEU",
"result": 28.4,
"baseline": 26.4
}
]
}
Interactive question answering:
User: "What is the main innovation?" Assistant: "The main innovation is replacing recurrent layers with self-attention mechanisms, allowing for more parallelization and better modeling of long-range dependencies."
User: "How does self-attention work?" Assistant: "Self-attention computes relationships between all positions in a sequence simultaneously. For each position, it computes attention weights with respect to all other positions..."
User: "What are the limitations?" Assistant: "Based on the paper and subsequent research: 1) Quadratic memory complexity for long sequences, 2) Position encoding may not generalize well, 3) Limited to fixed-length contexts..."
Generates comprehensive notes:
Concept Notes:
# Self-Attention
## Definition
Self-attention is a mechanism that relates different positions of a single sequence...
## How It Works
1. Compute Query, Key, Value matrices
2. Calculate attention weights
3. Apply weights to values
4. Output weighted sum
## Why It Matters
- Captures long-range dependencies
- Highly parallelizable
- Interpretable attention patterns
## Applications
- Machine translation
- Text summarization
- Image generation
## Related Concepts
- [[Attention Mechanism]]
- [[Transformer]]
- [[Multi-Head Attention]]
Method Notes:
# Multi-Head Attention
## Overview
Runs multiple self-attention operations in parallel...
## Implementation
```python
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super().__init__()
self.num_heads = num_heads
self.d_k = d_model // num_heads
# ...
## Reading Workflow
### Level 1: Quick Scan (5-10 min)
- Title and abstract
- Introduction
- Conclusion
- Key figures
**Output**: High-level understanding
### Level 2: Standard Read (30-60 min)
- All sections
- Important equations
- Key experiments
- Method details
**Output**: Detailed notes + questions
### Level 3: Deep Dive (2-4 hours)
- Every section in detail
- Derive equations
- Reproduce experiments
- Related work
**Output**: Comprehensive understanding + implementation
## Advanced Features
### Batch Processing
Process multiple PDFs:
```bash
python pdf-reader.py --dir D:\Papers\ --batch
Compare multiple papers:
对比分析这两篇论文的异同
python pdf-reader.py --zotero --query "attention mechanism"
python pdf-reader.py --pdf paper.pdf --save-to-zotero
Create prompts/custom-summary.txt:
请从以下角度总结这篇论文：
1. 研究动机
2. 核心创新
3. 技术方案
4. 实验验证
5. 局限性
Create templates/custom-template.md:
# {title}
## 一句话总结
{one_sentence_summary}
## 核心观点
{key_points}
## 技术细节
{technical_details}
## 我的思考
<!-- Your reflections -->
Error: Cannot extract text from PDF
Solutions:
--ocr flagWarning: Many formatting errors
Solutions:
--enhance flagError: API rate limit exceeded
Solution: Wait or use different API key
| Key | Action |
|---|---|
n | Next section |
p | Previous section |
q | Ask question |
s | Save note |
h | Highlight text |
f | Find in paper |
Esc | Exit |
paper-fetcher - Get papers to readpaper-summarizer - Generate summarieslearning-reflector - Reflect on readingzotero-local - Manage PDF library