// 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.
CLI tool (arxivterminal) for fetching, searching, and managing arXiv papers locally. Use when working with arXiv papers using the arxivterminal command - fetching new papers by category, searching the local database, viewing papers from specific dates, or managing the local paper database.
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
AI paper reviewer. Use when the user says 'review my paper', 'help me review this paper', '审稿', 'give me feedback on my paper', 'check my manuscript', 'evaluate this paper for NeurIPS/ICLR/EuroSys'. Accepts PDF files and produces structured narrative reviews with venue-specific dimensional scores and Accept/Reject recommendation.
Convert a local paper PDF to structured Markdown and export all figures as PNG + SVG + drawio. Attempts editable figure reconstruction via the built-in autofigure pipeline (SAM3 → RMBG-2.0 → VLM → SVG), falling back to a layered-SVG wrapper when API keys are unavailable. Use when the user wants to parse a paper PDF, extract its text as Markdown, or get editable/exportable figure assets.
Convert raster figure images into editable DrawIO files using SAM3 segmentation, RMBG-2.0 background removal, and multimodal LLM drawio generation.
Autonomous ML research loop — modify train.py, run 5-min GPU experiments, track val_bpb, iterate overnight. Invoke when the user wants to run self-directed architecture search on the autoresearch repo.
| 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":"🔭"}} |
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).
| Tool | Purpose |
|---|---|
arxiv_fetch_range | Crawl submittedDate in [start_date, end_date], optional keywords (OR in title/abstract), categories (default cs.AI, cs.LG, cs.CL, math.OC). Returns JSON papers. |
arxiv_rank_papers | Enhanced metadata score every paper; optionally enriches the top pool with Semantic Scholar / OpenAlex signals and optionally runs one batch LLM call for shortlist scoring; returns selected (top N). |
arxiv_paper_digest | Introduction report (Markdown) for the selected list; uses metadata, bibliometric cues, and LLM rationale when available. |
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.
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).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.arxiv Python package is required; queries use the public Atom API via httpx.semantic_scholar_api_key or set SEMANTIC_SCHOLAR_API_KEY / S2_API_KEY.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.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")