version: "1.0.0" name: paper-scout description: Fetch arXiv papers by date range and topics, rank them for research value, and produce introduction digests. Use when the user wants a literature sweep, daily or weekly paper triage, or a written overview of the best papers in a niche — without relying on the arxivterminal local database. metadata: {"clawphd":{"emoji":"🔭"}}

Paper Scout (arXiv pipeline)

Built-in tools implement a three-step workflow inspired by PaperFlow (arXiv query + metadata-style scoring) and PaperBrain (LLM screening of title/abstract + narrative digest).

Tools

Recommended workflow

1. Fetch

Call arxiv_fetch_range with start_date, end_date, keywords, and optionally categories / max_results. Pass the returned JSON (whole object) into the next step.

1. Rank

Call arxiv_rank_papers with:

• papers_json: the string from step 1 (or json.dumps of the object).
• interest_keywords: what “high value” means for the user (used for scoring and LLM).
• top_n: how many papers to keep.
• use_external_ranking: defaults to true; uses Semantic Scholar first and OpenAlex fallback for stronger selection.
• use_llm_refinement: true for stronger selection (needs OpenRouter / VLM configured for ClawPhD).

1. Digest

Call arxiv_paper_digest with:

• selected_papers_json: json.dumps of the selected array from step 2, or the full rank output object.
• interest_keywords: same as step 2.
• language: zh or en.

Notes

• No `arxiv` Python package is required; queries use the public Atom API via httpx.
• External ranking uses Semantic Scholar first and OpenAlex as fallback. It works without a key, but if you have one you can pass semantic_scholar_api_key or set SEMANTIC_SCHOLAR_API_KEY / S2_API_KEY.
• LLM refinement and rich digest need a configured multimodal/text provider (same stack as diagram tools — typically OpenRouter).
• For local cached papers and interactive CLI workflows, still use the `arxivterminal` skill (arxiv fetch, scripts/arxiv_search.py). All papers are stored under paper_library/{subject}/{date}/ in the workspace root, where {subject} is a snake_case topic slug and {date} is YYYY-MM-DD.

Example (conceptual)

arxiv_fetch_range(start_date="2026-03-01", end_date="2026-03-10", keywords=["world model", "VLA"], categories=["cs.RO","cs.LG"])
→ arxiv_rank_papers(papers_json=<output>, interest_keywords=["world model","VLA","embodied AI"], top_n=5, use_external_ranking=true, use_llm_refinement=true)
→ arxiv_paper_digest(selected_papers_json=<selected>, interest_keywords=[...], language="zh")