Study

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

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",
  "githubLinks": ["https://github.com/..."],
  "codeLinks": ["https://..."]
}

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

~/claude-papers/index.json

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.