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 ${CLAUDE_PAPER_PLUGIN_ROOT} reference below as that resolved absolute directory. Do not substitute the current workspace root.
When this workflow asks to launch the viewer, load and follow the claude-paper-webui skill.
Paper Study Workflow
Invoke this skill with a paper PDF path.
Language Detection: Detect the user's language from their input and generate ALL materials in that language.
- Example: User says "ๆไปฌๅญฆไน ไธไธ่ฟ็ฏ่ฎบๆๅง" โ Generate materials in Chinese
- Example: User says "Let's study this paper" โ Generate materials in English
Core Philosophy
Primary Objective:
Facilitate deep conceptual understanding and research-level thinking.
Secondary Objective:
Create a structured, reusable paper knowledge system.
This workflow is not just for summarizing โ it builds a learning environment around the paper.
Step 0: Check Dependencies (First Run Only)
if [ ! -f "${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
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 ${CLAUDE_PAPER_PLUGIN_ROOT}/skills/study/scripts/download-pdf.cjs "$USER_INPUT")
else
# Use local path directly
INPUT_PATH="$USER_INPUT"
fi
For URLs, the download script will:
- Download PDFs to
/tmp/claude-paper-downloads/
- Convert arXiv
/abs/ URLs to PDF URLs automatically
- Validate that URLs point to PDF files
- Return the local file path for processing
For local paths, use the path directly without downloading.
Step 1b: Parse PDF
Extract structured information:
PARSE_OUTPUT_DIR=$(mktemp -d)
node ${CLAUDE_PAPER_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js \
"$INPUT_PATH" \
--output-dir "$PARSE_OUTPUT_DIR"
The command prints a small, strict JSON summary to stdout and writes:
meta.json โ title, authors, abstract, links, page count, and a context-safe content previewpaper.txt โ complete extracted text without the 50k preview limit
Use paper.txt as the source for generating materials. Search it and read relevant sections as needed; do not treat meta.json.content as the complete paper when contentTruncated is true.
After choosing {paper-slug}, create the paper directory and copy both parser artifacts plus the original PDF:
mkdir -p ~/claude-papers/papers/{paper-slug}
cp "<metaPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/meta.json
cp "<fullTextPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/paper.txt
cp "$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:
-
Difficulty Level
- Beginner
- Intermediate
- Advanced
- Highly Theoretical
-
Paper Nature
- Theoretical
- Architecture-based
- Empirical-heavy
- System design
- Survey
-
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-attention3d detection, bev transformerprotein 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
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: Generate Interactive HTML Explorer
Create a single self-contained HTML file for interactively exploring the paper's core concepts.
Output path:
~/claude-papers/papers/{paper-slug}/index.html
Requirements
- Single HTML file, all CSS/JS inline, zero external dependencies
- Uses real data from the paper (actual metrics, hyperparameters, comparisons) โ never invent numbers
- Must work in a sandboxed iframe (no external fetches, no localStorage)
Guidelines
Choose the interaction pattern that best fits the paper โ architecture diagrams, parameter explorers, result dashboards, formula breakdowns, comparison matrices, etc. Let the paper's content dictate the format rather than forcing a fixed layout, focusing on the core ideas of the paper.
Every interactive control (slider, toggle, dropdown) should visibly change the visualization. Include brief explanatory text alongside interactive elements to teach concepts.
Step 6: Extract Images
mkdir -p ~/claude-papers/papers/{paper-slug}/images
python3 ${CLAUDE_PAPER_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 7: 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": ["tag-1", "tag-2"],
"githubLinks": ["https://github.com/..."],
"codeLinks": ["https://..."]
}
IMPORTANT: The index.json file must be located at:
~/claude-papers/index.json
Step 8: Relaunch Web UI
Load and follow the claude-paper-webui skill.
Step 9: Interactive Deep Learning Loop
After all files are generated:
Present to User:
-
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?
-
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:
- Refine it.
- Improve structure.
- Save as:
If iterated:
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.