Paper Reader Skill

Name: paper-reader
Rating: 76
Author: obiyoag

Read and analyze AI/CS research papers from PDF files.

Usage

code

/paper-reader /path/to/paper.pdf
/paper-reader /path/to/paper.pdf "attention mechanism"

Instructions

When given a PDF paper path:

•Load the /pdf skill to access PDF processing capabilities

•Extract text from the PDF using pdfplumber:

python

import pdfplumber

with pdfplumber.open("paper.pdf") as pdf:
    text = ""
    for page in pdf.pages:
        text += page.extract_text() or ""

•Analyze the extracted text following the steps below
•
Write output to a markdown file with the same name as the PDF (e.g., paper.pdf → paper.md) in the same directory
- •If the .md file already exists, append to it (no separator needed)
- •Do NOT output the analysis in the conversation; only write to the file

Analysis Steps

•Extract basic info: title, authors, venue/year
•Identify the problem: what problem does this paper solve? what are limitations of existing methods?
•Summarize contributions: 1-3 key contributions
•Explain methodology: overall framework, key techniques, innovations
•Report results: datasets, main conclusions, key metrics
•Note limitations: what are the limitations and future work?

Output Format (in Chinese)

Write the following template to the .md file (do NOT output in conversation):

markdown

# Paper Summary

## 1. Bibliographic Information
- **Title**:
- **Authors**:
- **Venue** (Journal/Conference, Year):
- **Paper link**:
- **Code / Project**:

---

## 2. Problem & Motivation
**What problem does this paper address?**  
Describe the task and why it matters.

**Why are existing methods insufficient?**  
What are the key limitations, bottlenecks, or failure modes of prior approaches?

---

## 3. High-level Idea
**What is the main idea of the paper in one or two sentences?**  
What is the core intuition behind the proposed method?

---

## 4. Method Overview
### 4.1 System / Model Architecture
Describe the overall pipeline or architecture.
- What are the main modules?
- How does data flow through the system?
- Where does learning happen?

(Optional: include a figure reference)

---

### 4.2 Key Components
For each major component:

#### Component A: <name>
- **Purpose**:
- **Input**:
- **Output**:
- **How it works**:

#### Component B: <name>
- **Purpose**:
- **Input**:
- **Output**:
- **How it works**:

---

### 4.3 Learning / Optimization
- What is the objective function?
- What losses are used?
- How is the model trained?
- What supervision signals are required?

---

### 4.4 Inference & Usage
- How is the model used at test time?
- What decisions does it make?
- What is produced as output?

---

## 5. What Makes This Method Different?
**Compared to prior work, what is fundamentally new?**

Describe the novelty in terms of:
- Modeling
- Architecture
- Learning strategy
- Inference or deployment
- Human–AI interaction (if applicable)

---

## 6. Why Does It Work?
**What is the underlying mechanism?**  
Explain why this method should perform better than previous ones.
- What inductive bias is introduced?
- What information is being captured that others miss?

---

## 7. Experimental Evidence
- **Datasets**:
- **Baselines**:
- **Evaluation metrics**:

### Key Results
- Main quantitative results
- Improvements over baselines
- Where it helps the most (and least)

---

## 8. Ablations & Analysis
- What happens when components are removed?
- Which parts matter the most?
- What insights do the authors provide?

---

## 9. Limitations
- What does the method not handle well?
- What assumptions does it rely on?
- What could go wrong in real-world use?

---

## 10. Takeaway
**One-sentence summary of the method:**

> “This paper proposes ______ by ______ in order to ______.”

Guidelines

•Be thorough but concise
•Focus on technical details that matter
•If a focus area is specified, emphasize that aspect
•Use the exact output format above
•All analysis output should be in English
•Always write to file, never output in conversation
•Confirm file path after writing (e.g., "Saved at /path/to/paper.md")