Papers Skill

Overview

Papers Skill turns a coding agent into a literature-research assistant. It orchestrates a bundled Python CLI (scripts/papers.py) that hits the free Semantic Scholar and arXiv APIs, downloads arXiv PDFs, and extracts text with PyMuPDF. The agent decides which subcommand to invoke and how to combine results into a literature scan, a deep read of one paper, an impact analysis, or a reading list.

This skill is the Skill-mode port of the papers-mcp MCP server by the same author. Both projects share the same feature set; this one ships as a Claude Code plugin so it can be installed with a single command and needs no long-running MCP process.

When to Use This Skill

• Use when the user asks to search academic papers by topic, author, or venue.
• Use when the user names a specific paper (by DOI, arXiv ID, or title) and wants metadata, the abstract, the TL;DR, or its reference list.
• Use when the user wants to find work that cites a known paper (impact analysis, follow-up tracking).
• Use when the user wants to download an arXiv PDF and have it summarized.
• Use when the user asks to build a reading list around a topic.

Do Not Use This Skill When

• The user wants paywalled non-arXiv full text. This skill cannot bypass publisher paywalls; it can only fetch arXiv PDFs and metadata everywhere.
• The user wants OCR over scanned PDFs. PyMuPDF extracts embedded text only; scanned image-PDFs return the fallback message and need a separate OCR step.
• The user wants real-time citation alerts or RSS-style watching. This skill is request-driven.

How It Works

Step 1: Verify dependencies

Three Python packages are required. The skill should check once per session, using the same interpreter to import-check and install so the dependency check and install target stay in sync:

python -c "import httpx, arxiv, fitz" 2>&1 || python -m pip install httpx arxiv PyMuPDF

If python is not on PATH, fall back to py (Windows launcher) or the absolute interpreter path — and remember to invoke pip via the same interpreter, e.g. py -m pip install httpx arxiv PyMuPDF.

Step 2: Invoke the bundled CLI

The script lives at ${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py and is bundled with this skill (no separate install needed). Always quote the path so it survives spaces.

python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" <subcommand> [args]

Step 3: Pick the right subcommand

detail and citations auto-detect the ID type: DOIs starting with 10. are used as-is, bare numeric IDs of 10+ digits are treated as arXiv IDs, and long hex strings are treated as Semantic Scholar paperIds.

Examples

Example 1: Literature scan on a topic

python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" search "retrieval augmented generation" --limit 10

Present results as a ranked table with # | Title | Year | Citations | ID, then ask the user which papers to dig into.

Example 2: Deep-read one paper

# 1. Confirm match
python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" detail 2005.11401
# 2. Download
python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" download 2005.11401 --save-dir ./pdfs
# 3. Extract abstract + intro + conclusion
python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" read ./pdfs/2005.11401v4.RAG.pdf --max-pages 10

Summarize as: problem · method · key result · limitations.

Example 3: Impact analysis on an anchor paper

python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" detail 10.48550/arXiv.2005.11401

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