[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-literature-search-biorxiv":3,"mdc-s0pzp7-key":36,"related-org-google-deepmind-literature-search-biorxiv":1642,"related-repo-google-deepmind-literature-search-biorxiv":1807},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"literature-search-biorxiv","search and download life science preprints","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"google-deepmind","Google DeepMind","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fgoogle-deepmind.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":17,"slug":18,"type":15},"bioRxiv","biorxiv",{"name":20,"slug":21,"type":15},"Life Sciences","life-sciences",{"name":23,"slug":24,"type":15},"medRxiv","medrxiv",2333,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills","2026-07-12T07:52:01.827847",null,234,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other databases and tools.","https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fliterature_search_biorxiv","---\nname: literature-search-biorxiv\ndescription: >\n  Browse, filter, and download life sciences, biology, and medical preprints\n  from bioRxiv and medRxiv. Supports fetching paper metadata by DOI,\n  and browsing by date range with category and keyword filters. Keyword\n  filtering is local, so date ranges MUST be narrow (1-4 weeks) with a\n  category to prevent timeouts.\n---\n\n# bioRxiv and medRxiv Literature Search\n\n## Prerequisites\n\n1.  **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure\n    `uv` is installed and on PATH.\n2.  **User Notification**: If .licenses\u002Fliterature_search_biorxiv_LICENSE.txt\n    does not already exist in the workspace root directory then (1) prominently\n    notify the user to check the terms at https:\u002F\u002Fapi.biorxiv.org\u002F and\n    https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fabout-biorxiv and to always check the\n    license of the papers retrieved by the skill for any restrictions, then (2)\n    create the file recording the notification text and timestamp.\n\n## Search Strategy Guide (Read First)\n\n**This skill browses a date-based preprint archive. It is NOT a keyword search\nengine.** Choose your approach based on what you already know:\n\n-   **A DOI** (e.g., from a citation): Use `search_by_doi.py`. Fast and\n    reliable.\n-   **Approximate date + category**: Use `search_by_dates.py` with a 1–4 week\n    range and `--category`.\n-   **Only a topic or keywords, no date**: **Do NOT use this skill for\n    discovery.** Use a keyword-capable literature skill first to find relevant\n    DOIs, then return here to fetch metadata.\n\n> **CRITICAL ANTI-PATTERN — Do NOT do this:** Do NOT attempt to search broad\n> date ranges (months or years) with `--keywords` hoping to find a specific\n> paper. The bioRxiv API does not support server-side keyword search. The script\n> must download ALL metadata for the entire date range and filter locally in\n> Python. Broad ranges will result in thousands of API calls, timeouts, and your\n> request being blocked for API abuse. This is the #1 reason this skill fails.\n\n## Core Rules\n\n-   **Use the Wrapper**: ALWAYS execute the provided helper scripts to query the\n    database rather than accessing the database directly. The scripts\n    automatically enforce the required rate limit gracefully.\n-   **Local Filtering (CRITICAL WARNING)**: Unlike arXiv, the bioRxiv API **does\n    not support server-side keyword or author searches**. Keyword and author\n    filtering is performed *locally* by the scripts after downloading all\n    metadata for a specified date range. You **MUST** use narrow date ranges\n    (e.g., 1-4 weeks) AND the `--category` filter when searching with\n    `--keywords` or `--author`.\n-   **Abstracts Excluded By Default**: To save context space in the resulting\n    JSON, abstracts are stripped from the output by default. If you are\n    searching by `--keywords` and want to read the abstracts of the resulting\n    papers to understand their context, you **MUST** pass the\n    `--include_abstracts` flag.\n-   **Output Redirection**: Search commands output JSON arrays to standard\n    output. Always redirect output to a file (e.g., `> results.json`) and parse\n    the file separately.\n-   **List Sources** If this skill is used, ensure this is mentioned in the\n    output AND list the URLs of all papers that were used in producing the\n    output.\n\n## Utility Scripts\n\nAll tools enforce a cross-process rate limits and retry with backoff on failure.\nTo ensure you respect terms-of-service, do NOT write custom `curl` queries.\n\n**Pagination:** The bioRxiv API returns results in pages of up to 100 papers.\nThe `search_by_dates.py` script automatically fetches all pages and reports\npagination progress to stderr (e.g., `[Page 2] Fetched 200\u002F543 papers...`). The\nJSON output to stdout contains the **complete** filtered result set across all\npages — no manual pagination is needed.\n\n### 1. Search by Dates (`search_by_dates.py`)\n\nSearch for preprints within an explicit date range, optionally filtering by\ncategory, keywords, or author.\n\n```bash\n# Broad category search over a 2-week period\nuv run scripts\u002Fsearch_by_dates.py --server biorxiv \\\n  --start_date 2024-01-01 --end_date 2024-01-14 \\\n  --category neuroscience > results.json\n\n# Deep keyword filtering using OR logic and including abstracts\nuv run scripts\u002Fsearch_by_dates.py --server medrxiv \\\n  --start_date 2023-11-01 --end_date 2023-11-30 \\\n  --category infectious_diseases \\\n  --keywords \"covid\" \"sars-cov-2\" --match_logic OR \\\n  --include_abstracts > covid_papers.json\n\n# Finding papers by a specific author in a narrow window\nuv run scripts\u002Fsearch_by_dates.py \\\n  --start_date 2024-05-01 --end_date 2024-05-14 \\\n  --author \"Smith\" > smith_papers.json\n```\n\n*Required Arguments:*\n\n-   `--start_date`: YYYY-MM-DD\n-   `--end_date`: YYYY-MM-DD\n\n*Optional Arguments:*\n\n-   `--server`: `biorxiv` (default) or `medrxiv`\n-   `--category`: A valid subject category (see below). **Highly recommended** —\n    dramatically reduces the data the script must download and filter.\n-   `--keywords`: List of strings to search in the title\u002Fabstract.\n-   `--match_logic`: `AND` (default) or `OR` for keywords.\n-   `--author`: Author name (case-insensitive string match).\n-   `--include_abstracts`: Flag to include full abstracts in the JSON output.\n\n### 2. Fetch Metadata by DOI (`search_by_doi.py`)\n\nRetrieve the detailed JSON metadata for a single paper if you already know its\nDOI. **This is the most reliable entry point.**\n\n```bash\nuv run scripts\u002Fsearch_by_doi.py --server biorxiv \\\n  --doi \"10.1101\u002F2023.08.15.551388\" \\\n  --include_abstracts > paper_info.json\n```\n\n### Downloading Full-Text PDFs\n\n> **This skill does NOT support PDF downloads.** To download the full-text PDF\n> of a bioRxiv or medRxiv preprint, use the **`literature-search-europepmc`**\n> skill. First, use the paper's DOI to look up its PMCID via EuropePMC, then use\n> EuropePMC's PDF retrieval to download the document.\n\n## Valid Subject Categories\n\nYou can pass these to the `--category` flag in `search_by_dates.py`. The script\nwill strictly validate them.\n\n### bioRxiv Categories:\n\n`animal_behavior_and_cognition`, `biochemistry`, `bioengineering`,\n`bioinformatics`, `biophysics`, `cancer_biology`, `cell_biology`,\n`clinical_trials`, `developmental_biology`, `ecology`, `epidemiology`,\n`evolutionary_biology`, `genetics`, `genomics`, `immunology`, `microbiology`,\n`molecular_biology`, `neuroscience`, `paleontology`, `pathology`,\n`pharmacology_and_toxicology`, `physiology`, `plant_biology`,\n`scientific_communication_and_education`, `synthetic_biology`,\n`systems_biology`, `zoology`\n\n### medRxiv Categories:\n\n`addiction_medicine`, `allergy_and_immunology`, `anesthesia`,\n`cardiovascular_medicine`, `dentistry_and_oral_medicine`, `dermatology`,\n`emergency_medicine`, `endocrinology`, `epidemiology`, `forensic_medicine`,\n`gastroenterology`, `genetic_and_genomic_medicine`, `health_informatics`,\n`health_economics_and_outcomes_research`, `health_policy`,\n`health_systems_and_quality_improvement`, `hematology`, `hiv_aids`,\n`infectious_diseases`, `intensive_care_and_critical_care_medicine`,\n`medical_education`, `medical_ethics`, `nephrology`, `neurology`, `nursing`,\n`nutrition`, `obstetrics_and_gynecology`,\n`occupational_and_environmental_health`, `oncology`, `ophthalmology`,\n`orthopedics`, `otolaryngology`, `pain_medicine`, `palliative_care`,\n`pathology`, `pediatrics`, `pharmacology_and_therapeutics`,\n`primary_care_research`, `psychiatry_and_clinical_psychology`,\n`public_and_global_health`, `radiology_and_imaging`,\n`rehabilitation_medicine_and_physical_therapy`, `respiratory_medicine`,\n`rheumatology`, `sexual_and_reproductive_health`, `sports_medicine`, `surgery`,\n`toxicology`, `transplantation`, `urology`\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,57,120,126,137,202,224,230,355,361,374,406,420,425,791,799,823,831,928,940,950,1026,1032,1056,1062,1081,1087,1280,1286,1636],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"biorxiv-and-medrxiv-literature-search",[47],{"type":48,"value":49},"text","bioRxiv and medRxiv Literature Search",{"type":42,"tag":51,"props":52,"children":54},"h2",{"id":53},"prerequisites",[55],{"type":48,"value":56},"Prerequisites",{"type":42,"tag":58,"props":59,"children":60},"ol",{},[61,92],{"type":42,"tag":62,"props":63,"children":64},"li",{},[65,76,78,83,85,90],{"type":42,"tag":66,"props":67,"children":68},"strong",{},[69],{"type":42,"tag":70,"props":71,"children":73},"code",{"className":72},[],[74],{"type":48,"value":75},"uv",{"type":48,"value":77},": Read the ",{"type":42,"tag":70,"props":79,"children":81},{"className":80},[],[82],{"type":48,"value":75},{"type":48,"value":84}," skill and follow its Setup instructions to ensure\n",{"type":42,"tag":70,"props":86,"children":88},{"className":87},[],[89],{"type":48,"value":75},{"type":48,"value":91}," is installed and on PATH.",{"type":42,"tag":62,"props":93,"children":94},{},[95,100,102,110,112,118],{"type":42,"tag":66,"props":96,"children":97},{},[98],{"type":48,"value":99},"User Notification",{"type":48,"value":101},": If .licenses\u002Fliterature_search_biorxiv_LICENSE.txt\ndoes not already exist in the workspace root directory then (1) prominently\nnotify the user to check the terms at ",{"type":42,"tag":103,"props":104,"children":108},"a",{"href":105,"rel":106},"https:\u002F\u002Fapi.biorxiv.org\u002F",[107],"nofollow",[109],{"type":48,"value":105},{"type":48,"value":111}," and\n",{"type":42,"tag":103,"props":113,"children":116},{"href":114,"rel":115},"https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fabout-biorxiv",[107],[117],{"type":48,"value":114},{"type":48,"value":119}," and to always check the\nlicense of the papers retrieved by the skill for any restrictions, then (2)\ncreate the file recording the notification text and timestamp.",{"type":42,"tag":51,"props":121,"children":123},{"id":122},"search-strategy-guide-read-first",[124],{"type":48,"value":125},"Search Strategy Guide (Read First)",{"type":42,"tag":127,"props":128,"children":129},"p",{},[130,135],{"type":42,"tag":66,"props":131,"children":132},{},[133],{"type":48,"value":134},"This skill browses a date-based preprint archive. 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The bioRxiv API does not support server-side keyword search. The script\nmust download ALL metadata for the entire date range and filter locally in\nPython. Broad ranges will result in thousands of API calls, timeouts, and your\nrequest being blocked for API abuse. This is the #1 reason this skill fails.",{"type":42,"tag":51,"props":225,"children":227},{"id":226},"core-rules",[228],{"type":48,"value":229},"Core Rules",{"type":42,"tag":138,"props":231,"children":232},{},[233,243,296,327,345],{"type":42,"tag":62,"props":234,"children":235},{},[236,241],{"type":42,"tag":66,"props":237,"children":238},{},[239],{"type":48,"value":240},"Use the Wrapper",{"type":48,"value":242},": ALWAYS execute the provided helper scripts to query the\ndatabase rather than accessing the database directly. 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The\nJSON output to stdout contains the ",{"type":42,"tag":66,"props":400,"children":401},{},[402],{"type":48,"value":403},"complete",{"type":48,"value":405}," filtered result set across all\npages — no manual pagination is needed.",{"type":42,"tag":407,"props":408,"children":410},"h3",{"id":409},"_1-search-by-dates-search_by_datespy",[411,413,418],{"type":48,"value":412},"1. Search by Dates (",{"type":42,"tag":70,"props":414,"children":416},{"className":415},[],[417],{"type":48,"value":174},{"type":48,"value":419},")",{"type":42,"tag":127,"props":421,"children":422},{},[423],{"type":48,"value":424},"Search for preprints within an explicit date range, optionally filtering by\ncategory, keywords, or author.",{"type":42,"tag":426,"props":427,"children":432},"pre",{"className":428,"code":429,"language":430,"meta":431,"style":431},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Broad category search over a 2-week period\nuv run scripts\u002Fsearch_by_dates.py --server biorxiv \\\n  --start_date 2024-01-01 --end_date 2024-01-14 \\\n  --category neuroscience > results.json\n\n# Deep keyword filtering using OR logic and including abstracts\nuv run scripts\u002Fsearch_by_dates.py --server medrxiv \\\n  --start_date 2023-11-01 --end_date 2023-11-30 \\\n  --category infectious_diseases \\\n  --keywords \"covid\" 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Use when you want to query regulatory annotations or raw experimental data across human cell types.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1765,1766,1769,1770],{"name":1651,"slug":1118,"type":15},{"name":1767,"slug":1768,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":1771,"slug":1772,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":1775,"name":1775,"fn":1776,"description":1777,"org":1778,"tags":1779,"stars":25,"repoUrl":26,"updatedAt":1784},"ensembl-database","query genomic and protein data from Ensembl","Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1780,1781,1782,1783],{"name":1651,"slug":1118,"type":15},{"name":1665,"slug":1181,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":1786,"name":1786,"fn":1787,"description":1788,"org":1789,"tags":1790,"stars":25,"repoUrl":26,"updatedAt":1794},"foldseek-structural-search","perform 3D protein structural searches","Performs 3D structural searches of proteins against various databases (PDB, AlphaFold, CATH, MGnify, etc.) using the Foldseek API. Use ONLY when the user provides a physical 3D coordinate file (.cif, .mmcif, or .pdb) and wants to find structurally similar proteins. Do NOT use if the user only provides a protein sequence, gene name, or UniProt ID.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1791,1792,1793],{"name":1651,"slug":1118,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:09.354992",{"slug":1796,"name":1796,"fn":1797,"description":1798,"org":1799,"tags":1800,"stars":25,"repoUrl":26,"updatedAt":1805},"gnomad-database","query genetic variant data from gnomAD","Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1801,1802,1803,1804],{"name":1651,"slug":1118,"type":15},{"name":1665,"slug":1181,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009",38,{"items":1808,"total":1806},[1809,1816,1823,1831,1837,1844,1850],{"slug":1645,"name":1645,"fn":1646,"description":1647,"org":1810,"tags":1811,"stars":25,"repoUrl":26,"updatedAt":1656},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1812,1813,1814,1815],{"name":1651,"slug":1118,"type":15},{"name":1653,"slug":1188,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":1658,"name":1658,"fn":1659,"description":1660,"org":1817,"tags":1818,"stars":25,"repoUrl":26,"updatedAt":1670},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1819,1820,1821,1822],{"name":1651,"slug":1118,"type":15},{"name":1665,"slug":1181,"type":15},{"name":13,"slug":14,"type":15},{"name":1668,"slug":1669,"type":15},{"slug":1672,"name":1672,"fn":1673,"description":1674,"org":1824,"tags":1825,"stars":25,"repoUrl":26,"updatedAt":1690},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1826,1827,1828,1829,1830],{"name":1678,"slug":1679,"type":15},{"name":1681,"slug":1682,"type":15},{"name":1684,"slug":1685,"type":15},{"name":1687,"slug":1688,"type":15},{"name":13,"slug":14,"type":15},{"slug":1692,"name":1692,"fn":1693,"description":1694,"org":1832,"tags":1833,"stars":25,"repoUrl":26,"updatedAt":1702},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1834,1835,1836],{"name":1698,"slug":1699,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":1704,"name":1704,"fn":1705,"description":1706,"org":1838,"tags":1839,"stars":25,"repoUrl":26,"updatedAt":1717},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1840,1841,1842,1843],{"name":1710,"slug":1711,"type":15},{"name":1665,"slug":1181,"type":15},{"name":1714,"slug":1715,"type":15},{"name":13,"slug":14,"type":15},{"slug":1719,"name":1719,"fn":1720,"description":1721,"org":1845,"tags":1846,"stars":25,"repoUrl":26,"updatedAt":1733},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1847,1848,1849],{"name":1725,"slug":1726,"type":15},{"name":1728,"slug":1729,"type":15},{"name":1731,"slug":1732,"type":15},{"slug":1735,"name":1735,"fn":1736,"description":1737,"org":1851,"tags":1852,"stars":25,"repoUrl":26,"updatedAt":1746},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1853,1854,1855,1856],{"name":1651,"slug":1118,"type":15},{"name":1665,"slug":1181,"type":15},{"name":1743,"slug":1744,"type":15},{"name":13,"slug":14,"type":15}]