Detalle del Skill
study
Helps deeply study research papers, but is limited to PDF-paper workflows.
Revisar antes de usar
La revisión automática comprueba relevancia, no seguridad ni respaldo. Lee las instrucciones de la fuente antes de usar este Skill.
SKILL.md
Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.
---
name: study
description: Use this skill when the user wants to read, study, analyze, or deeply understand a research paper (PDF).
disable-model-invocation: false
allowed-tools: Bash, Write, Edit, Read
---
# 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)
```bash
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 "${CLAUDE_PLUGIN_ROOT}/.installed"
echo "Dependencies installed!"
fi
```
Recommended:
* Node >= 18
* Python 3 with pip (for image extraction)
---
# Step 1: Download and Parse PDF
Supports multiple input formats:
* **Local path**: `~/Downloads/paper.pdf`
* **Direct PDF URL**: `https://arxiv.org/pdf/1706.03762.pdf`
* **arXiv URL**: `https://arxiv.org/abs/1706.03762`
## Step 1a: Check input type and download if URL
```bash
USER_INPUT="<user-input>"
# Check if input is a URL (starts with http:// or https://)
if [[ "$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:
```bash
node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js "$INPUT_PATH"
```
Output includes:
* title
* authors
* abstract
* full content
* githubLinks
* codeLinks
* tags (generated in Step 2.5)
Save to:
```
~/claude-papers/papers/{paper-slug}/meta.json
```
Copy original PDF:
```bash
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 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-pLeer la fuente completa en GitHub (abre una página externa)