Claude Paper Summary

Cross-Agent Compatibility

This file is generated from the existing Claude Paper Skill. Its workflow and output requirements are unchanged; only equivalent host metadata, the plugin-root variable, and cross-skill invocation are adapted.

Resolve CLAUDE_PAPER_PLUGIN_ROOT to the absolute plugin/ directory in this package before each shell invocation. From this SKILL.md, that directory is ../../plugin. Treat every ${CLAUDE_PAPER_PLUGIN_ROOT} reference below as that resolved absolute directory. Do not substitute the current workspace root.

When this workflow asks to launch the viewer, load and follow the claude-paper-webui skill.

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 skill 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 "${CLAUDE_PAPER_PLUGIN_ROOT}/.installed" ]; then
  echo "First run - installing dependencies..."
  cd "${CLAUDE_PAPER_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_PAPER_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 ${CLAUDE_PAPER_PLUGIN_ROOT}/skills/study/scripts/download-pdf.cjs "$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:

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

The command prints a small, strict JSON summary to stdout and writes:

• meta.json — title, authors, abstract, links, page count, and a context-safe content preview
• paper.txt — complete extracted text without the 50k preview limit

Use paper.txt as the source for the quick summary. Do not treat meta.json.content as the complete paper when contentTruncated is true.

Step 2: Generate Quick Summary

Create the paper folder:

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

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:

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

Load and follow the claude-paper-webui skill.

Step 5: Present Summary to User

After generating the summary:

1. Show the user the quick-summary.md content - Display the full summary

2. Offer next steps:

   • "Would you like to study this paper in more depth? Use claude-paper-study skill for comprehensive materials."
   • "Do you have questions about specific parts of the paper?"
   • "Would you like me to explain any section in more detail?"

3. 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 skill to generate comprehensive materials
• The summary should be self-contained and readable in under 5 minutes
• Focus on conceptual clarity over technical details