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Get Started Free →Browse, filter, and download 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.
.claude/skills/mkurman-literature-search-biorxiv/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | 152% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 19% | 0% |
| case-19 | ✓→✗ | ▼ Worse | 111% | 0% |
| case-22 | ✓→✗ | ▼ Worse | 129% | 0% |
uv: Read the uv skill and follow its Setup instructions to ensureuv is installed and on PATH.
this skill 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.
This skill browses a date-based preprint archive. It is NOT a keyword search engine. Choose your approach based on what you already know:
search_by_doi.py. Fast andreliable.
search_by_dates.py with a 1–4 weekrange and --category.
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.
database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
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.
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. Always redirect output to a file (e.g., > results.json) and parse the file separately.
output AND list the URLs of all papers that were used in producing the output.
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.
search_by_dates.py)Search for preprints within an explicit date range, optionally filtering by category, keywords, or author.
bash# 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-DDOptional 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.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.
bashuv run scripts/search_by_doi.py --server biorxiv \ --doi "10.1101/2023.08.15.551388" \ --include_abstracts > paper_info.json
> 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.
You can pass these to the --category flag in search_by_dates.py. The script will strictly validate them.
animal_behavior_and_cognition, biochemistry, bioengineering, bioinformatics, biophysics, cancer_biology, cell_biology, clinical_trials, developmental_biology, ecology, epidemiology, evolutionary_biology, genetics, genomics, immunology, microbiology, molecular_biology, neuroscience, paleontology, pathology, pharmacology_and_toxicology, physiology, plant_biology, scientific_communication_and_education, synthetic_biology, systems_biology, zoology
addiction_medicine, allergy_and_immunology, anesthesia, cardiovascular_medicine, dentistry_and_oral_medicine, dermatology, emergency_medicine, endocrinology, epidemiology, forensic_medicine, gastroenterology, genetic_and_genomic_medicine, health_informatics, health_economics_and_outcomes_research, health_policy, health_systems_and_quality_improvement, hematology, hiv_aids, infectious_diseases, intensive_care_and_critical_care_medicine, medical_education, medical_ethics, nephrology, neurology, nursing, nutrition, obstetrics_and_gynecology, occupational_and_environmental_health, oncology, ophthalmology, orthopedics, otolaryngology, pain_medicine, palliative_care, pathology, pediatrics, pharmacology_and_therapeutics, primary_care_research, psychiatry_and_clinical_psychology, public_and_global_health, radiology_and_imaging, rehabilitation_medicine_and_physical_therapy, respiratory_medicine, rheumatology, sexual_and_reproductive_health, sports_medicine, surgery, toxicology, transplantation, urology
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-07 | fail→fail | 13,367 | 4,841 | -64% | 1 | 1 | 0% | 1,495 | 2,220 | +48% | 0 | 0 | — |
case-08 | fail→fail | 16,091 | 6,414 | -60% | 1 | 1 | 0% | 2,758 | 2,385 | -14% | 0 | 0 | — |
case-01 | fail→fail | 17,822 | 7,284 | -59% | 1 | 1 | 0% | 2,954 | 2,523 | -15% | 0 | 0 | — |
case-02 | fail→fail | 11,406 | 6,348 | -44% | 1 | 1 | 0% | 2,027 | 2,495 | +23% | 0 | 0 | — |
case-03 | fail→fail | 10,961 | 5,298 | -52% | 1 | 1 | 0% | 1,955 | 2,292 | +17% | 0 | 0 | — |
case-04 | pass→fail | 12,775 | 7,741 | -39% | 1 | 1 | 0% | 2,053 | 2,434 | +19% | 0 | 0 | — |
case-05 | fail→fail | 16,692 | 7,643 | -54% | 1 | 1 | 0% | 2,030 | 2,558 | +26% | 0 | 0 | — |
case-06 | fail→fail | 3,849 | 8,029 | +109% | 1 | 1 | 0% | 643 | 2,504 | +289% | 0 | 0 | — |
case-09 | fail→fail | 14,803 | 4,777 | -68% | 1 | 1 | 0% | 2,707 | 2,277 | -16% | 0 | 0 | — |
case-10 | fail→fail | 9,547 | 6,344 | -34% | 1 | 1 | 0% | 1,809 | 2,414 | +33% | 0 | 0 | — |
case-11 | fail→pass | 9,614 | 17,740 | +85% | 1 | 1 | 0% | 1,787 | 4,497 | +152% | 0 | 0 | — |
case-12 | fail→fail | 11,831 | 5,409 | -54% | 1 | 1 | 0% | 2,179 | 2,247 | +3% | 0 | 0 | — |
case-13 | fail→fail | 13,653 | 5,731 | -58% | 1 | 1 | 0% | 2,347 | 2,322 | -1% | 0 | 0 | — |
case-14 | fail→pass | 13,723 | 12,201 | -11% | 1 | 1 | 0% | 2,647 | 3,665 | +38% | 0 | 0 | — |
case-15 | fail→fail | 11,690 | 6,490 | -44% | 1 | 1 | 0% | 1,810 | 2,518 | +39% | 0 | 0 | — |
case-16 | fail→fail | 12,721 | 33,639 | +164% | 1 | 1 | 0% | 2,051 | 2,288 | +12% | 0 | 0 | — |
case-17 | fail→fail | 16,951 | 8,613 | -49% | 1 | 1 | 0% | 2,932 | 2,717 | -7% | 0 | 0 | — |
case-18 | fail→fail | 9,894 | 5,064 | -49% | 1 | 1 | 0% | 2,051 | 2,356 | +15% | 0 | 0 | — |
case-19 | pass→fail | 6,110 | 5,958 | -2% | 1 | 1 | 0% | 1,111 | 2,346 | +111% | 0 | 0 | — |
case-20 | fail→fail | 8,590 | 5,210 | -39% | 1 | 1 | 0% | 716 | 2,304 | +222% | 0 | 0 | — |
case-21 | fail→fail | 10,632 | 4,603 | -57% | 1 | 1 | 0% | 1,963 | 2,382 | +21% | 0 | 0 | — |
case-22 | pass→fail | 5,964 | 7,792 | +31% | 1 | 1 | 0% | 1,137 | 2,601 | +129% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 3 counted toward the lift figure. The other 19 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -5 percentage points is the difference between those two pass rates over the 3 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.