alaliqing/claude-paper/summary

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

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

PARSE_OUTPUT_DIR=$(mktemp -d)
node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js \
  "$INPUT_PATH" \
  --output-dir "$PARSE_OUTPUT_DIR"

mkdir -p ~/claude-papers/papers/{paper-slug}
cp "<metaPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/meta.json
cp "<fullTextPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/paper.txt
cp "$INPUT_PATH" ~/claude-papers/papers/{paper-slug}/paper.pdf

# 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]

{"papers": []}

{
  "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://..."]
}

~/claude-papers/index.json

/claude-paper:webui

# 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