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literature-search-biorxiv

google-deepmind/science-skills · updated Jun 4, 2026

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$npx skills add https://github.com/google-deepmind/science-skills --skill literature-search-biorxiv
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### Literature Search Biorxiv

  • name: "literature-search-biorxiv"
  • description: "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 filte..."
skill.md
name
literature-search-biorxiv
description
> 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.

bioRxiv and medRxiv Literature Search

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in 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.

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.

uv run scripts/search_by_doi.py --server biorxiv \
  --doi "10.1101/2023.08.15.551388" \
  --include_abstracts > paper_info.json

Downloading Full-Text PDFs

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.

bioRxiv Categories:

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

medRxiv Categories:

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

how to use literature-search-biorxiv

How to use literature-search-biorxiv on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add literature-search-biorxiv
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/google-deepmind/science-skills --skill literature-search-biorxiv

The skills CLI fetches literature-search-biorxiv from GitHub repository google-deepmind/science-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/literature-search-biorxiv

Reload or restart Cursor to activate literature-search-biorxiv. Access the skill through slash commands (e.g., /literature-search-biorxiv) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.460 reviews
  • Lucas Abbas· Dec 20, 2024

    literature-search-biorxiv has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hana Abbas· Dec 16, 2024

    literature-search-biorxiv reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Henry Sharma· Dec 16, 2024

    Registry listing for literature-search-biorxiv matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hana Rahman· Dec 8, 2024

    literature-search-biorxiv is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ama Thomas· Dec 4, 2024

    Useful defaults in literature-search-biorxiv — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aditi Mensah· Nov 27, 2024

    Useful defaults in literature-search-biorxiv — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Lucas Wang· Nov 23, 2024

    literature-search-biorxiv is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Lucas Liu· Nov 7, 2024

    We added literature-search-biorxiv from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Lucas Li· Nov 7, 2024

    literature-search-biorxiv fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Henry Desai· Nov 3, 2024

    Solid pick for teams standardizing on skills: literature-search-biorxiv is focused, and the summary matches what you get after install.

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