This skill provides efficient Python-based tools for searching and retrieving preprints from the bioRxiv database. It enables comprehensive searches by keywords, authors, date ranges, and categories, returning structured JSON metadata that includes titles, abstracts, DOIs, and citation information. The skill also supports PDF downloads for full-text analysis.
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node --versionbiorxiv-databaseExecute the skills CLI command in your project's root directory to begin installation:
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill provides efficient Python-based tools for searching and retrieving preprints from the bioRxiv database. It enables comprehensive searches by keywords, authors, date ranges, and categories, returning structured JSON metadata that includes titles, abstracts, DOIs, and citation information. The skill also supports PDF downloads for full-text analysis.
Use this skill when:
Search for preprints containing specific keywords in titles, abstracts, or author lists.
Basic Usage:
python scripts/biorxiv_search.py \
--keywords "CRISPR" "gene editing" \
--start-date 2024-01-01 \
--end-date 2024-12-31 \
--output results.json
With Category Filter:
python scripts/biorxiv_search.py \
--keywords "neural networks" "deep learning" \
--days-back 180 \
--category neuroscience \
--output recent_neuroscience.json
Search Fields:
By default, keywords are searched in both title and abstract. Customize with --search-fields:
python scripts/biorxiv_search.py \
--keywords "AlphaFold" \
--search-fields title \
--days-back 365
Find all papers by a specific author within a date range.
Basic Usage:
python scripts/biorxiv_search.py \
--author "Smith" \
--start-date 2023-01-01 \
--end-date 2024-12-31 \
--output smith_papers.json
Recent Publications:
# Last year by default if no dates specified
python scripts/biorxiv_search.py \
--author "Johnson" \
--output johnson_recent.json
Retrieve all preprints posted within a specific date range.
Basic Usage:
python scripts/biorxiv_search.py \
--start-date 2024-01-01 \
--end-date 2024-01-31 \
--output january_2024.json
With Category Filter:
python scripts/biorxiv_search.py \
--start-date 2024-06-01 \
--end-date 2024-06-30 \
--category genomics \
--output genomics_june.json
Days Back Shortcut:
# Last 30 days
python scripts/biorxiv_search.py \
--days-back 30 \
--output last_month.json
Retrieve detailed metadata for a specific preprint.
Basic Usage:
python scripts/biorxiv_search.py \
--doi "10.1101/2024.01.15.123456" \
--output paper_details.json
Full DOI URLs Accepted:
python scripts/biorxiv_search.py \
--doi "https://doi.org/10.1101/2024.01.15.123456"
Download the full-text PDF of any preprint.
Basic Usage:
python scripts/biorxiv_search.py \
--doi "10.1101/2024.01.15.123456" \
--download-pdf paper.pdf
Batch Processing: For multiple PDFs, extract DOIs from a search result JSON and download each paper:
import json
from biorxiv_search import BioRxivSearcher
# Load search results
with open('results.json') as f:
data = json.load(f)
searcher = BioRxivSearcher(verbose=True)
# Download each paper
for i, paper in enumerate(data['results'][:10]): # First 10 papers
doi = paper['doi']
searcher.download_pdf(doi, f"papers/paper_{i+1}.pdf")
Filter searches by bioRxiv subject categories:
animal-behavior-and-cognitionbiochemistrybioengineeringbioinformaticsbiophysicscancer-biologycell-biologyclinical-trialsdevelopmental-biologyecologyepidemiologyevolutionary-biologygeneticsgenomicsimmunologymicrobiologymolecular-biologyneurosciencepaleontologypathologypharmacology-and-toxicologyphysiologyplant-biologyscientific-communication-and-educationsynthetic-biologysystems-biologyzoologyAll searches return structured JSON with the following format:
{
"query": {
"keywords": ["CRISPR"],
"start_date": "2024-01-01",
"end_date": "2024-12-31",
"category": "genomics"
},
"result_count": 42,
"results": [
{
"doi": "10.1101/2024.01.15.123456",
"title": "Paper Title Here",
"authors": "Smith J, Doe J, Johnson A",
"author_corresponding": "Smith J",
"author_corresponding_institution": "University Example",
"date": "2024-01-15",
"version": "1",
"type": "new results",
"license": "cc_by",
"category": "genomics",
"abstract": "Full abstract text...",
"pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf",
"html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1",
"jatsxml": "https://www.biorxiv.org/content/...",
"published": ""
}
]
}
python scripts/biorxiv_search.py \
--keywords "organoids" "tissue engineering" \
--start-date 2023-01-01 \
--end-date 2024-12-31 \
--category bioengineering \
--output organoid_papers.json
import json
with open('organoid_papers.json') as f:
data = json.load(f)
print(f"Found {data['result_count']} papers")
for paper in data['results'][:5Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
biorxiv-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: biorxiv-database is focused, and the summary matches what you get after install.
biorxiv-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added biorxiv-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in biorxiv-database — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in biorxiv-database — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: biorxiv-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
biorxiv-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
biorxiv-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for biorxiv-database matched our evaluation — installs cleanly and behaves as described in the markdown.
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