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Get Started Free →Preprint server API for biology and medicine papers
.claude/skills/brycewang-stanford-biorxiv-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 33% | 0% |
bioRxiv (pronounced "bio-archive") is a free online archive and distribution service for unpublished preprints in the life sciences. Operated by Cold Spring Harbor Laboratory, it provides researchers with immediate access to the latest findings before formal peer review. The bioRxiv API enables programmatic access to preprint metadata, content details, and publication linkage data across biology and medical sciences.
The API serves researchers who need to track emerging research trends, monitor preprint activity in specific subfields, or build automated literature surveillance pipelines. It is particularly valuable for systematic reviewers who want to capture the latest evidence before journal publication, and for bibliometric analysts studying the preprint-to-publication pipeline.
bioRxiv hosts preprints across more than 25 subject areas including neuroscience, genomics, bioinformatics, cell biology, and many more. The API returns structured metadata including titles, authors, abstracts, DOIs, publication dates, and links to corresponding published journal articles when available.
No authentication required. The bioRxiv API is fully open and does not require any API key, token, or registration. All endpoints are publicly accessible without rate limiting restrictions.
Fetch detailed metadata for preprints posted within a specified date range or for a specific server (bioRxiv or medRxiv).
GET https://api.biorxiv.org/details/{server}/{interval}/{cursor}| Parameter | Type | Required | Description | |------------|--------|----------|--------------------------------------------------| | server | string | Yes | Server name: biorxiv or medrxiv | | interval | string | Yes | Date range in YYYY-MM-DD/YYYY-MM-DD format | | cursor | int | No | Pagination cursor (default 0, increments of 100) |
bashcurl "https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-31/0"
doi, title, authors, author_corresponding, date, category, abstract, published (journal DOI if available), and jatsxml link.Look up which preprints have been published in peer-reviewed journals, providing the mapping between preprint DOIs and journal article DOIs.
GET https://api.biorxiv.org/pubs/{server}/{interval}/{cursor}| Parameter | Type | Required | Description | |------------|--------|----------|--------------------------------------------------| | server | string | Yes | Server name: biorxiv or medrxiv | | interval | string | Yes | Date range in YYYY-MM-DD/YYYY-MM-DD format | | cursor | int | No | Pagination cursor (default 0, increments of 100) |
bashcurl "https://api.biorxiv.org/pubs/biorxiv/2024-01-01/2024-06-30/0"
preprint_doi, published_doi, preprint_title, published_journal, published_date, and preprint_date.No formal rate limits are documented for the bioRxiv API. However, responsible use is expected. Results are paginated at 100 records per request, and the cursor parameter should be incremented to retrieve additional pages. Avoid excessive concurrent requests to ensure availability for all users.
Retrieve the latest preprints and filter by category to track new submissions in your field:
bash# Fetch recent neuroscience preprints curl "https://api.biorxiv.org/details/biorxiv/2024-06-01/2024-06-07/0" \ | jq '.collection[] | select(.category == "neuroscience")'
Monitor which preprints in your area have been formally published:
bash# Check publication status for recent preprints curl "https://api.biorxiv.org/pubs/biorxiv/2024-01-01/2024-06-30/0" \ | jq '.collection[] | select(.published_doi != "")'
Paginate through all results for a given date range to build a comprehensive alert feed:
pythonimport requests base = "https://api.biorxiv.org/details/biorxiv/2024-06-01/2024-06-07" cursor = 0 all_preprints = [] while True: resp = requests.get(f"{base}/{cursor}").json() records = resp.get("collection", []) if not records: break all_preprints.extend(records) cursor += 100 print(f"Total preprints retrieved: {len(all_preprints)}")
medrxiv as server parameter)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 15,008 | 11,650 | -22% | 1 | 1 | 0% | 2,414 | 3,218 | +33% | 0 | 0 | — |
case-01 | fail→pass | 35,909 | 23,964 | -33% | 1 | 1 | 0% | 6,069 | 3,955 | -35% | 0 | 0 | — |
case-02 | pass→pass | 8,901 | 7,646 | -14% | 1 | 1 | 0% | 1,788 | 2,484 | +39% | 0 | 0 | — |
case-03 | pass→pass | 8,821 | 4,173 | -53% | 1 | 1 | 0% | 1,906 | 2,107 | +11% | 0 | 0 | — |
case-04 | pass→pass | 9,932 | 6,542 | -34% | 1 | 1 | 0% | 1,987 | 2,469 | +24% | 0 | 0 | — |
case-06 | fail→pass | 22,515 | 4,679 | -79% | 1 | 1 | 0% | 3,714 | 1,986 | -47% | 0 | 0 | — |
case-07 | pass→pass | 5,460 | 1,814 | -67% | 1 | 1 | 0% | 996 | 1,518 | +52% | 0 | 0 | — |
case-08 | pass→pass | 8,989 | 2,894 | -68% | 1 | 1 | 0% | 1,404 | 1,778 | +27% | 0 | 0 | — |
case-09 | fail→pass | 27,776 | 4,579 | -84% | 1 | 1 | 0% | 4,587 | 1,958 | -57% | 0 | 0 | — |
case-10 | pass→pass | 10,006 | 3,177 | -68% | 1 | 1 | 0% | 1,395 | 1,669 | +20% | 0 | 0 | — |
case-11 | fail→pass | 17,498 | 2,777 | -84% | 1 | 1 | 0% | 2,843 | 1,761 | -38% | 0 | 0 | — |
case-12 | pass→pass | 14,070 | 11,036 | -22% | 1 | 1 | 0% | 2,277 | 3,711 | +63% | 0 | 0 | — |
case-13 | pass→pass | 9,581 | 6,052 | -37% | 1 | 1 | 0% | 1,880 | 2,539 | +35% | 0 | 0 | — |
case-14 | pass→pass | 7,767 | 2,687 | -65% | 1 | 1 | 0% | 1,520 | 1,607 | +6% | 0 | 0 | — |
case-15 | pass→pass | 5,682 | 2,750 | -52% | 1 | 1 | 0% | 793 | 1,611 | +103% | 0 | 0 | — |
case-16 | pass→pass | 4,584 | 1,907 | -58% | 1 | 1 | 0% | 694 | 1,500 | +116% | 0 | 0 | — |
case-17 | pass→pass | 9,322 | 7,645 | -18% | 1 | 1 | 0% | 1,715 | 2,469 | +44% | 0 | 0 | — |
case-18 | pass→pass | 6,049 | 4,039 | -33% | 1 | 1 | 0% | 1,179 | 1,967 | +67% | 0 | 0 | — |
case-19 | pass→pass | 4,506 | 2,229 | -51% | 1 | 1 | 0% | 765 | 1,532 | +100% | 0 | 0 | — |
case-20 | pass→pass | 9,728 | 4,837 | -50% | 1 | 1 | 0% | 1,549 | 2,082 | +34% | 0 | 0 | — |
case-21 | pass→pass | 8,309 | 2,864 | -66% | 1 | 1 | 0% | 1,342 | 1,798 | +34% | 0 | 0 | — |
case-22 | pass→pass | 7,347 | 7,639 | +4% | 1 | 1 | 0% | 1,141 | 2,495 | +119% | 0 | 0 | — |
case-23 | pass→pass | 16,501 | 9,877 | -40% | 1 | 1 | 0% | 2,292 | 2,666 | +16% | 0 | 0 | — |
case-24 | pass→pass | 10,382 | 6,838 | -34% | 1 | 1 | 0% | 2,064 | 2,373 | +15% | 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. 24 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 24 comparable cases.
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.