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name: papers-skill description: "Skill for academic research workflows: search Semantic Scholar (200M+ papers), inspect citations, download arXiv PDFs, and extract PDF text. Bundles a self-contained Python CLI." category: research risk: safe source: community source_repo: xwmxcz/papers-skill source_type: community date_added: "2026-06-11" author: xwmxcz tags: [research, academic, papers, citations, arxiv, semantic-scholar, pdf] tools: [claude-code, antigravity, cursor, gemini-cli, codex-cli, opencode] license: "MIT" license_source: "https://github.com/xwmxcz/papers-skill/blob/main/LICENSE"

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

Subcommand	Purpose	Example
search <query> [--limit N]	Semantic Scholar search, max 20	search "diffusion models" --limit 5
detail <paper_id>	Full metadata, TL;DR, top references	detail 10.48550/arXiv.2310.06825
citations <paper_id> [--limit N]	Papers citing this one, max 20	citations <id> --limit 15
arxiv <query> [--max-results N]	arXiv preprint search, max 10	arxiv "RLHF" --max-results 5
download <arxiv_id> [--save-dir D]	Save PDF locally	download 2310.06825 --save-dir ./pdfs
read <pdf_path> [--max-pages N]	Extract PDF text via PyMuPDF	read ./pdfs/foo.pdf --max-pages 20

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 "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" detail 10.48550/arXiv.2005.11401
python "${CLAUDE_PLUGIN_ROOT}/skills/papers-skill/scripts/papers.py" citations 10.48550/arXiv.2005.11401 --limit 20

Cluster the citing papers by year/theme and highlight the most-cited follow-ups.

Best Practices

• ✅ Always call detail before download to confirm the paper matches user intent. Skipping this leads to wrong PDFs being fetched.
• ✅ Include the paper ID alongside every title in your output so the user can re-query precisely.
• ✅ Cite as [FirstAuthor et al., Year] *Title* (cites: N).
• ✅ For PDFs you download, always report the absolute save path.
• ❌ Don't crawl. The script auto-retries 429s with exponential backoff; don't pile on parallel queries.
• ❌ Don't raise --max-pages to 100+ without warning the user — it can consume a large amount of context.

Limitations

• The skill cannot fetch full text from paywalled publishers (Elsevier, Springer, Wiley, etc.). It can only read open arXiv PDFs.
• PyMuPDF extracts embedded text only. Scanned image-PDFs return the fallback message PDF无法提取文本（可能是扫描件）; offer the user an alternative version or note that OCR is required.
• Semantic Scholar's anonymous tier rate-limits aggressively. The script retries 3× with exponential backoff; persistent 429s during heavy use surface as 搜索失败: rate limit, retries exhausted.
• This skill does not replace environment-specific validation, testing, or expert review. Stop and ask for clarification if required inputs are missing.

Security & Safety Notes

• The CLI performs outbound HTTPS only to api.semanticscholar.org and arxiv.org (and the arXiv-listed mirror for the bundled arxiv package). No authentication tokens are sent.
• download writes a PDF to the directory the user specifies (default: the current working directory). Confirm the save path with the user before downloading to an unexpected location.
• read opens a local PDF file with PyMuPDF — make sure the path the user supplies is one they trust.
• No credentials or API keys are needed or stored anywhere.

Common Pitfalls

• Problem: 需要安装 arxiv: pip install arxiv or 需要安装 PyMuPDF: pip install PyMuPDF. Solution: The script returns this friendly message instead of crashing when an optional dependency is missing. Offer to run the install command.

• Problem: 搜索失败: rate limit, retries exhausted from search or detail or citations. Solution: Semantic Scholar is rate-limiting. Wait ~10 seconds and retry once. For repeated runs, fall back to arxiv for arXiv-indexed work.

• Problem: download fails with 找不到 arXiv ID: …. Solution: The user gave a non-arXiv ID (likely a DOI for a non-arXiv paper). Use detail to inspect; only papers with an externalIds.ArXiv field can be downloaded.

• Problem: Garbled Chinese output on Windows. Solution: The script already forces UTF-8 stdout. If the host terminal is still misconfigured, set PYTHONIOENCODING=utf-8 in the shell environment.

Additional Resources

• Skill home (this plugin): https://github.com/xwmxcz/papers-skill
• Upstream MCP server: https://github.com/xwmxcz/papers-mcp
• Semantic Scholar API docs: https://api.semanticscholar.org/
• arXiv API docs: https://info.arxiv.org/help/api/
• PyMuPDF docs: https://pymupdf.readthedocs.io/