// 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.
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.
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.
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 | research-lit |
| description | 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. |
| argument-hint | ["paper-topic-or-url"] |
| allowed-tools | Bash(*), Read, Glob, Grep, WebSearch, WebFetch, Write, arxivterminal, arxiv-doc-builder |
Research topic: $ARGUMENTS
paper_library/ — Local directory in the workspace root containing user's paper collection (PDFs). Directory structure: paper_library/{subject}/{date}/, where {subject} is a snake_case topic slug (e.g. diffusion_models, world_models) and {date} is YYYY-MM-DD (the date the paper was saved).true, download top 3-5 most relevant arXiv PDFs to PAPER_LIBRARY after search. When false (default), only fetch metadata (title, abstract, authors) via arXiv API — no files are downloaded.ARXIV_DOWNLOAD = true.💡 Overrides:
/research-lit "topic" — sources: web— only search the web (skip all local)/research-lit "topic" — arxiv download: true— download top relevant arXiv PDFs/research-lit "topic" — arxiv download: true, max download: 10— download up to 10 PDFs
This skill checks multiple sources in priority order. All are optional — if a source is not configured or not requested, skip it silently.
Parse $ARGUMENTS for a — sources: directive:
— sources: is specified: Only search the listed sources (comma-separated). Valid values: local, web, all.all — search every available source in priority order.Examples:
/research-lit "diffusion models" → all (default)
/research-lit "diffusion models" — sources: all → all
/research-lit "diffusion models" — sources: local → local PDFs only
/research-lit "diffusion models" — sources: web → web search only
| Priority | Source | ID | How to detect | What it provides |
|---|---|---|---|---|
| 1 | Local PDFs | local | Glob: paper_library/{subject}/{date}/*.pdf | Raw PDF content (first 3 pages) |
| 2 | Web search | web | Always available (WebSearch) | arXiv, Semantic Scholar, Google Scholar |
Graceful degradation: If a source is unavailable, skip it silently and continue with the remaining sources.
Before searching online, check if the user already has relevant papers locally:
Locate library: Scan PAPER_LIBRARY for PDF files
Glob: paper_library/**/*.pdf
Papers are organized as paper_library/{subject}/{date}/filename.pdf. When searching for a specific topic, prioritize matching {subject} directories first, then scan broadly.
Filter by relevance: Match filenames and first-page content against the research topic. Skip clearly unrelated papers.
Summarize relevant papers: For each relevant local PDF (up to MAX_LOCAL_PAPERS):
Build local knowledge base: Compile summaries into a "papers you already have" section. This becomes the starting point — external search fills the gaps.
📚 If no local papers are found, skip to Step 1. If the user has a comprehensive local collection, the external search can be more targeted (focus on what's missing).
arXiv API search (always runs, no download by default):
Locate the fetch script and search arXiv directly:
# Try to find arxiv_fetch.py in the project
SCRIPT=$(find tools/ -name "arxiv_fetch.py" 2>/dev/null | head -1)
# Search arXiv API for structured results (title, abstract, authors, categories)
python3 "$SCRIPT" search "QUERY" --max 10
If arxiv_fetch.py is not found, fall back to WebSearch for arXiv.
The arXiv API returns structured metadata (title, abstract, full author list, categories, dates) — richer than WebSearch snippets. Merge these results with WebSearch findings and de-duplicate.
Optional PDF download (only when ARXIV_DOWNLOAD = true):
After all sources are searched and papers are ranked by relevance:
# Download top N most relevant arXiv papers
python3 "$SCRIPT" download ARXIV_ID --dir paper_library/{subject}/$(date +%Y-%m-%d)/
For each relevant paper (from all sources), extract:
Present as a structured literature table:
| Paper | Venue | Method | Key Result | Relevance to Us | Source |
|-------|-------|--------|------------|-----------------|--------|
Plus a narrative summary of the landscape (3-5 paragraphs).
paper_library/{subject}/{YYYY-MM-DD}/