name: read-paper description: Read a research paper and extract structured notes. Use when analyzing papers for a survey. allowed-tools: Bash, Read, Write, Glob, Grep, mcp__document-loader__read_document, mcp__memory__create_entities, mcp__memory__create_relations, mcp__memory__search_nodes

Paper Reading Methodology

Startup Assertion

Before reading any papers, verify document-loader MCP is available:

• Call mcp__document-loader__read_document on any PDF in your batch
• If it fails or is not available, STOP and message the team lead: "ERROR: document-loader MCP unavailable"
• Do NOT proceed without PDF reading capability. Do NOT use pdftotext (it garbles equations).

Reading Priority

For each paper in your assigned batch:

1. paper.md (pandoc from LaTeX) — best quality, equations preserved as $...$
2. document-loader MCP on the PDF — use mcp__document-loader__read_document(path) for structured extraction with equation preservation
3. source/*.tex files — read raw LaTeX for equations, algorithms, figures

Reading Process

Pass 1: Overview (from paper.md or PDF)

• Title, authors, year, venue
• Abstract — what's the main contribution?
• Introduction — what problem does it solve?
• Conclusion — what are the key takeaways?

Pass 2: Technical Details (from source/*.tex)

• Key equations — copy the LaTeX math notation verbatim
• Algorithms — extract pseudocode or algorithm descriptions
• Figures — note figure numbers, captions, and file paths
• Tables — extract key results tables with numbers

Pass 3: Code & Implementation

• Check workspace/code_repos.md for this paper's repository (Researcher may have found it)
• If not found, search within the paper text for GitHub/GitLab URLs:

  grep -i "github.com\|gitlab.com\|code.*available\|implementation.*available" workspace/papers/{id}_*/paper.md workspace/papers/{id}_*/source/*.tex 2>/dev/null
  

• If a repo exists, note: URL, language, framework, stars, key implementation details
• If no repo found, mark as "❌ No public code found"

Pass 4: Analysis

• How does this paper relate to other papers in the survey?
• Does it extend, contradict, or complement other work?
• What are its limitations?
• What is its specific relevance to the survey topic?

Output: Structured Notes

For each paper, write a note file using the template at $CLAUDE_SKILL_DIR/template.md.

Save to: workspace/notes/batch-{N}/{arxiv_id}.md

Memory Storage

After reading each paper, store key findings in memory MCP:

create_entities([{
  name: "{paper_title}",
  entityType: "paper",
  observations: [
    "arxiv:{arxiv_id}",
    "year:{year}",
    "cite_key:{bibtex_key}",
    "contribution: {one-line summary}",
    "method: {key method}",
    "result: {key quantitative result}"
  ]
}])

Store relationships between papers:

create_relations([{
  from: "{paper_A}",
  to: "{paper_B}",
  relationType: "extends|contradicts|uses|compares_with|improves_upon"
}])

Quality Checks

Before marking a paper as read:

• All template fields are filled (no empty sections)
• Key equations are in LaTeX notation
• At least one quantitative result is noted
• Relevance to survey topic is explicitly stated
• Relationships to other papers (if known) are stored in memory