Study

Paper Study Workflow

Invoke this skill with a paper PDF path.

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吧" → Generate materials in Chinese
• Example: User says "Let's study this paper" → Generate materials in English

Core Philosophy

Primary Objective: Facilitate deep conceptual understanding and research-level thinking.

Secondary Objective: Create a structured, reusable paper knowledge system.

This workflow is not just for summarizing — it builds a learning environment around the paper.

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

Recommended:

• Node >= 18
• Python 3 with pip (for image extraction)

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

For local paths, use the path directly without downloading.

Step 1b: Parse PDF

Extract structured information:

PARSE_OUTPUT_DIR=$(mktemp -d)
node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js \
  "$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 generating materials. Search it and read relevant sections as needed; do not treat meta.json.content as the complete paper when contentTruncated is true.

After choosing {paper-slug}, create the paper directory and copy both parser artifacts plus the original PDF:

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 exactly 2 tags in Step 2.5 and add them to the saved meta.json.

Fallback: If structured parsing fails, extract raw text and continue with degraded structure.

Step 2: Assess Paper Before Generating Materials

Before generating any files, evaluate:

1. Difficulty Level

   • Beginner
   • Intermediate
   • Advanced
   • Highly Theoretical

2. Paper Nature

   • Theoretical
   • Architecture-based
   • Empirical-heavy
   • System design
   • Survey

3. Methodological Complexity

   • Simple pipeline
   • Multi-stage training
   • Novel architecture
   • Heavy mathematical derivation

This assessment determines:

• Whether to create method.md
• Whether to create .ipynb
• Explanation depth
• Code demo complexity

Step 2.5: Generate Exactly 2 Semantic Tags (Mandatory)

Before generating files, infer exactly 2 tags from semantic understanding of the paper.

Rules:

• Generate exactly 2 tags, no more and no less
• Tags must be distinct
• Each tag should be short (1-3 words)
• Avoid generic tags: paper, research, ai, ml
• Prefer one tag for problem/domain and one for method/core idea

Examples:

• machine translation, self-attention
• 3d detection, bev transformer
• protein folding, structure prediction

Persist these 2 tags in both locations:

• ~/claude-papers/papers/{paper-slug}/meta.json as tags
• ~/claude-papers/index.json entry as tags

Step 3: Generate Core Study Materials

Create folder:

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

Required Files

README.md

• What the paper is about (one paragraph)
• Difficulty level
• How to navigate materials
• Key takeaways
• Estimated study time
• Folder structure overview

summary.md

• Background context
• Problem statement
• Main contributions
• Key results
• Quantitative metrics

insights.md (Most Important)

• Core idea explained plainly
• Why this works
• What conceptual shift it introduces
• Trade-offs
• Limitations
• Comparison to prior work
• Practical implications

qa.md

15 questions:

• 5 basic
• 5 intermediate
• 5 advanced

Use this format:

### Question

<details>
<summary>Answer</summary>

Detailed explanation.

</details>

---

Conditional Files

method.md (Recommended for most papers)

Include:

• Component breakdown
• Algorithm flow
• Architecture diagram (ASCII if needed)
• Step-by-step explanation
• Pseudocode (balanced with explanation)
• Implementation pitfalls
• Hyperparameter sensitivity
• Reproduction risks

mental-model.md (Recommended for most papers)

• What type of problem is this?
• What prior knowledge is assumed?
• How it fits into the broader research map
• How to mentally categorize this work