Quick Paper Summary Workflow

This skill generates a concise summary of a research paper's core ideas and key points.

When to use:

You want to quickly understand what a paper is about
You need the main contributions without deep technical details
You're screening papers to decide which to study in depth

When NOT to use:

You want comprehensive study materials (use /claude-paper:study instead)
You need code demonstrations
You want interactive visualizations

Language Detection: Detect the user's language from their input and generate ALL materials in that language.

Example: User says "我们学习一下这篇论文" → Generate materials in Chinese
Example: User says "Let's study this paper" → Generate materials in English

Step 0: Check Dependencies (First Run Only)

if [ ! -f "${CLAUDE_PLUGIN_ROOT}/.installed" ]; then
  echo "First run - installing dependencies..."
  cd "${CLAUDE_PLUGIN_ROOT}"
  npm install || exit 1

  # Install Python dependencies for image extraction
  python3 -m pip install pymupdf --user 2>/dev/null || pip3 install pymupdf --user 2>/dev/null || echo "Warning: Failed to install pymupdf"

  touch "${CLAUDE_PLUGIN_ROOT}/.installed"
  echo "Dependencies installed!"
fi

Step 1: Download and Parse PDF

Supports multiple input formats:

Local path: ~/Downloads/paper.pdf
Direct PDF URL: https://arxiv.org/pdf/1706.03762.pdf
arXiv URL: https://arxiv.org/abs/1706.03762

Step 1a: Check input type and download if URL

USER_INPUT="<user-input>"

# Check if input is a URL (starts with http:// or https://)
if [[ "$USER_INPUT" =~ ^https?:// ]]; then
  # Download PDF from URL
  INPUT_PATH=$(node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/download-pdf.cjs "$USER_INPUT")
else
  # Use local path directly
  INPUT_PATH="$USER_INPUT"
fi

For URLs, the download script will:

Download PDFs to /tmp/claude-paper-downloads/
Convert arXiv /abs/ URLs to PDF URLs automatically
Validate that URLs point to PDF files
Return the local file path for processing

Step 1b: Parse PDF

Extract structured information:

node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js "$INPUT_PATH"

Output includes:

title
authors
abstract
full content
githubLinks
codeLinks

Save to:

~/claude-papers/papers/{paper-slug}/meta.json

Copy original PDF:

cp <pdf-path> ~/claude-papers/papers/{paper-slug}/paper.pdf

Step 2: Generate Quick Summary

Create the paper folder:

mkdir -p ~/claude-papers/papers/{paper-slug}

Generate quick-summary.md with the following structure:

# Quick Summary: [Paper Title]

## One Sentence
[One sentence that captures what the paper is about]

## Problem
[What problem does this paper solve? Why is it important?]

## Core Idea
[The key innovation explained in 2-3 sentences. What makes this paper novel?]

## Key Contributions
- [Contribution 1]
- [Contribution 2]
- [Contribution 3]
- [Contribution 4 if applicable]

## Main Results
| Metric | Value | Dataset/Benchmark |
|--------|-------|-------------------|
| [metric1] | [value] | [dataset] |
| [metric2] | [value] | [dataset] |

## Why It Matters
[Practical implications. How does this advance the field? What can we now do that we couldn't before?]

## Limitations
- [Limitation 1]
- [Limitation 2]

Guidelines for each section:

Section	Length	Focus
One Sentence	1 sentence	High-level summary
Problem	2-3 sentences	Context and motivation
Core Idea	2-3 sentences	The main innovation
Key Contributions	3-5 bullets	What's new/novel
Main Results	1 table	Quantitative metrics from the paper
Why It Matters	2-3 sentences	Practical value
Limitations	2-3 bullets	What the paper doesn't solve

Total length: ~300-500 words (excluding results table)

Step 3: Update Index

CRITICAL: Read existing index.json first, then append the new paper. Never overwrite the entire file.

If index.json does not exist, create:

{"papers": []}

Append new entry to the papers array:

{
  "id": "paper-slug",
  "title": "Paper Title",
  "slug": "paper-slug",
  "authors": ["Author 1", "Author 2"],
  "abstract": "Paper abstract...",
  "year": 2024,
  "date": "2024-01-01",
  "tags": ["quick-summary"],
  "githubLinks": ["https://github.com/..."],
  "codeLinks": ["https://..."]
}

IMPORTANT: The index.json file must be located at:

~/claude-papers/index.json

Step 4: Relaunch Web UI

Invoke:

/claude-paper:webui

Step 5: Present Summary to User

After generating the summary:

Show the user the quick-summary.md content - Display the full summary
Offer next steps:
- "Would you like to study this paper in more depth? Use /claude-paper:study for comprehensive materials."
- "Do you have questions about specific parts of the paper?"
- "Would you like me to explain any section in more detail?"
File location reminder:
- Summary saved to: ~/claude-papers/papers/{paper-slug}/quick-summary.md
- Web UI available at: http://localhost:5815

Example Output

# Quick Summary: Attention Is All You Need

## One Sentence
This paper introduces the Transformer, a neural network architecture based entirely on attention mechanisms, achieving state-of-the-art results in machine translation.

## Problem
Sequence transduction models at the time (RNNs, LSTMs, GRUs) process data sequentially, limiting parallelization and struggling with long-range dependencies.

## Core Idea
Replace recurrent layers with self-attention mechanisms, enabling full parallelization during training and direct modeling of dependencies regardless of distance. The Transformer uses multi-head attention to jointly attend to information from different representation subspaces.

## Key Contributions
- First transduction model relying entirely on self-attention, no recurrence
- Multi-head attention mechanism for joint attention across subspaces
- Positional encodings to inject sequence order information
- Achieved 28.4 BLEU on WMT 2014 English-to-German (2+ BLEU improvement)
- Training was significantly faster than previous state-of-the-art

## Main Results
| Metric | Value | Dataset/Benchmark |
|--------|-------|-------------------|
| BLEU (EN-DE) | 28.4 | WMT 2014 |
| BLEU (EN-FR) | 41.8 | WMT 2014 |
| Training cost | 3.3 × 10^18 FLOPs | WMT 2014 EN-DE |
| Training time | 12 hours on 8 P100 | WMT 2014 EN-DE |

## Why It Matters
The Transformer eliminated recurrence, enabling massive parallelization and scaling. This architecture became the foundation for BERT, GPT, and virtually all modern large language models, fundamentally changing NLP and beyond.

## Limitations
- Self-attention has O(n²) complexity, limiting sequence length
- No explicit modeling of position beyond learned encodings
- Requires large amounts of training data

Notes

This skill is intentionally minimal - it generates only the summary, no code demos, no interactive HTML, no deep-dive materials
For users who want more, they can use /claude-paper:study to generate comprehensive materials
The summary should be self-contained and readable in under 5 minutes
Focus on conceptual clarity over technical details

summary