uv: Read the uv skill and follow its Setup instructions to ensure
uv is 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.py with 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 --keywords hoping 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.
Core Rules
Use the Wrapper: ALWAYS execute the provided helper scripts to query the
database rather than accessing the database directly. The scripts
automatically enforce the required rate limit gracefully.
Local Filtering (CRITICAL WARNING): Unlike arXiv, the bioRxiv API does
not support server-side keyword or author searches. Keyword and author
filtering is performed locally by the scripts after downloading all
metadata for a specified date range. You MUST use narrow date ranges
(e.g., 1-4 weeks) AND the --category filter when searching with
--keywords or --author.
Abstracts Excluded By Default: To save context space in the resulting
JSON, abstracts are stripped from the output by default. If you are
searching by --keywords and want to read the abstracts of the resulting
papers to understand their context, you MUST pass the
--include_abstracts flag.
Output Redirection: Search commands output JSON arrays to standard
output. Always redirect output to a file (e.g., > results.json) and parse
the file separately.
List Sources If this skill is used, ensure this is mentioned in the
output AND list the URLs of all papers that were used in producing the
output.
Utility Scripts
All tools enforce a cross-process rate limits and retry with backoff on failure.
To ensure you respect terms-of-service, do NOT write custom curl queries.
Pagination: The bioRxiv API returns results in pages of up to 100 papers.
The search_by_dates.py script automatically fetches all pages and reports
pagination progress to stderr (e.g., [Page 2] Fetched 200/543 papers...). The
JSON output to stdout contains the complete filtered result set across all
pages — no manual pagination is needed.
1. Search by Dates (search_by_dates.py)
Search for preprints within an explicit date range, optionally filtering by
category, keywords, or author.
# Broad category search over a 2-week period
uv run scripts/search_by_dates.py --server biorxiv \
--start_date 2024-01-01 --end_date 2024-01-14 \
--category neuroscience > results.json
# Deep keyword filtering using OR logic and including abstracts
uv run scripts/search_by_dates.py --server medrxiv \
--start_date 2023-11-01 --end_date 2023-11-30 \
--category infectious_diseases \
--keywords "covid" "sars-cov-2" --match_logic OR \
--include_abstracts > covid_papers.json
# Finding papers by a specific author in a narrow window
uv run scripts/search_by_dates.py \
--start_date 2024-05-01 --end_date 2024-05-14 \
--author "Smith" > smith_papers.json
Required Arguments:
--start_date: YYYY-MM-DD
--end_date: YYYY-MM-DD
Optional Arguments:
--server: biorxiv (default) or medrxiv
--category: A valid subject category (see below). Highly recommended —
dramatically reduces the data the script must download and filter.
--keywords: List of strings to search in the title/abstract.
--match_logic: AND (default) or OR for keywords.
--author: Author name (case-insensitive string match).
--include_abstracts: Flag to include full abstracts in the JSON output.
2. Fetch Metadata by DOI (search_by_doi.py)
Retrieve the detailed JSON metadata for a single paper if you already know its
DOI. This is the most reliable entry point.
This skill does NOT support PDF downloads. To download the full-text PDF
of a bioRxiv or medRxiv preprint, use the literature-search-europepmc
skill. First, use the paper's DOI to look up its PMCID via EuropePMC, then use
EuropePMC's PDF retrieval to download the document.
Valid Subject Categories
You can pass these to the --category flag in search_by_dates.py. The script
will strictly validate them.
