Literature Search Biorxiv
Browse and filter life sciences, biology, and medical preprints from bioRxiv and medRxiv.
Install
npx skills add google-deepmind/science-skills --skill literature-search-biorxiv -g -a claude-code -y- Repository
- google-deepmind/science-skills3.4k
- Category
- Engineering
- Updated
- 12d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 1,840 tokens
- Runs scripts
- Yes, review before use
What it does
Browse and filter life sciences, biology, and medical preprints from bioRxiv and medRxiv. Supports fetching paper metadata by DOI, and browsing by date range with category and keyword filters. Keyword filtering is local, so date ranges MUST be narrow (1-4 weeks) with a category to prevent timeouts.
bioRxiv and medRxiv Literature Search
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/literature_search_biorxiv_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://api.biorxiv.org/ and https://www.biorxiv.org/content/about-biorxiv and to always check the license of the papers retrieved by the skill for any restrictions, then (2) create the file recording the notification text and timestamp.
Search Strategy Guide (Read First)
This skill browses a date-based preprint archive. It is NOT a keyword search engine. Choose your approach based on what you already know:
- A DOI (e.g., from a citation): Use
search_by_doi.py. Fast and reliable. - Approximate date + category: Use
search_by_dates.pywith a 1, 4 week range and--category. - Only a topic or keywords, no date: Do NOT use this skill for discovery. Use a keyword-capable literature skill first to find relevant DOIs, then return here to fetch metadata.
CRITICAL ANTI-PATTERN, Do NOT do this: Do NOT attempt to search broad date ranges (months or years) with
--keywordshoping to find a specific paper. The bioRxiv API does not support server-side keyword search. The script must download ALL metadata for the entire date range and filter locally in Python. Broad ranges will result in thousands of API calls, timeouts, and your request being blocked for API abuse. This is the #1 reason this skill fails.
