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:

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

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 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

reflection.md (Optional auto-generated)

• •If I were to extend this paper
• •What open problems remain
• •What assumptions are fragile
• •Where it might fail in practice

Step 4: Code Demonstrations (Mandatory)

At least one runnable demo must be created.

All code demos must be placed in:

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

Create the code directory first:

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

Guidelines:

• •Self-contained
• •Runnable independently
• •Educational comments (explain why)
• •Focus on core contribution
• •Prefer clarity over completeness

Possible types:

• •Simplified conceptual implementation
• •Visualization script
• •Minimal architecture demo
• •Interactive notebook (.ipynb)

Name descriptively:

• •model_demo.py
• •vectorized_planning_demo.py
• •contrastive_loss_visualization.ipynb

Avoid generic names.

Step 5: Extract Images

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

python3 ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/extract-images.py \
  paper.pdf \
  ~/claude-papers/papers/{paper-slug}/images

Rename key images descriptively:

• •architecture.png
• •training_pipeline.png
• •results_table.png

Step 6: Update Index

If index.json does not exist, create:

{"papers": []}

Add entry:

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

Step 7: Relaunch Web UI

Invoke:

/claude-paper:webui

Step 8: Interactive Deep Learning Loop

After all files are generated:

Present to User:

1. •

   Ask:

   • •What part is still unclear?
   • •Do you want deeper mathematical breakdown?
   • •Do you want implementation-level analysis?
   • •Do you want comparison with another paper?
2. •

   Allow user to:

   • •Ask deeper questions
   • •Summarize their understanding
   • •Propose new ideas

If user asks deeper questions:

Generate a new file inside the same folder:

Examples:

• •deep-dive-contrastive-loss.md
• •math-derivation-breakdown.md
• •comparison-with-transformers.md
• •extension-ideas.md

If user provides their own summary:

1. •Refine it.
2. •Improve structure.
3. •Save as:

• •user-summary-v1.md

If iterated:

• •user-summary-v2.md

If user wants structured consolidation:

Create:

• •consolidated-notes.md
• •study-session-1.md
• •exam-review.md

This makes the paper folder a growing knowledge node.