Literature Search Biorxiv logo

Literature Search Biorxiv

OrganizationPopular
google-deepmind
literature-search-biorxiv

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.

Overview

Publishergoogle-deepmind
Repositoryscience-skills
Skill nameliterature-search-biorxiv
Stars
3.1K
Forks
349
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google-deepmind on GitHub. Read the source before you install it.

Installation

Install the Literature Search Biorxiv AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google-deepmind/science-skills.git /tmp/science-skills
mkdir -p .claude/skills
cp -r /tmp/science-skills/skills/literature_search_biorxiv .claude/skills/literature-search-biorxiv
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Search Biorxiv in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Literature Search Biorxiv on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Literature Search Biorxiv is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 .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.

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-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.

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Literature Search Biorxiv AI skill do?

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.

Why use Literature Search Biorxiv on TypingMind?

Because you install it once and use it with any model. Literature Search Biorxiv is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Literature Search Biorxiv in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google-deepmind/science-skills/tree/main/skills/literature_search_biorxiv. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Literature Search Biorxiv?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Literature Search Biorxiv?

As many as you like. As long as a model supports skills, you can use Literature Search Biorxiv with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Literature Search Biorxiv AI skill free?

Yes. It is published on GitHub by google-deepmind under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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