[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-string-database":3,"mdc--ew75mu-key":33,"related-org-google-deepmind-string-database":372,"related-repo-google-deepmind-string-database":538},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":22,"repoUrl":23,"updatedAt":24,"license":25,"forks":26,"topics":27,"repo":28,"sourceUrl":31,"mdContent":32},"string-database","query protein interactions from STRING database","Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.\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],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":17,"slug":18,"type":15},"Life Sciences","life-sciences",{"name":20,"slug":21,"type":15},"Bioinformatics","bioinformatics",2333,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills","2026-07-12T07:51:45.44302",null,234,[],{"repoUrl":23,"stars":22,"forks":26,"topics":29,"description":30},[],"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\u002Fstring_database","---\nname: string-database\ndescription: >\n  Query the STRING database for protein-protein interactions (PPIs), functional\n  enrichment, and homology. Use when the user asks about interactions between\n  specific proteins, interaction evidence, confidence scores, protein\n  interaction partners, or pathway enrichments.\n---\n\n# STRING Database Skill\n\nThis skill allows you to query the STRING database programmatically using a\nbundled Python CLI wrapper.\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\u002Fstring_database_LICENSE.txt does not\n    already exist in the workspace root directory then (1) prominently notify\n    the user to check the terms at https:\u002F\u002Fstring-db.org\u002Fcgi\u002Faccess, then (2)\n    create the file recording the notification text and timestamp.\n\n## Core Rules\n\n1.  **MANDATORY: Ask for Species First:** The STRING API requires NCBI Taxon\n    IDs. **You MUST NOT guess or assume a species.** If the user does not\n    explicitly state a species or Taxon ID, you MUST stop and ask: \"Which\n    species are you interested in? I need the NCBI Taxon ID to proceed.\" Even\n    for well-known proteins like TP53, BRCA1, or MDM2 that are commonly\n    associated with human studies, you MUST still ask — do not default to Human.\n2.  **Never print output to stdout:** The `--output \u003Cfile.tsv>` is required.\n    Never read large outputs into context. Instead use jq, python or file\n    operations (`grep`, `head`) to process large output.\n3.  **Map Identifiers first:** If you only have common gene names (e.g.,\n    'TP53'), map them to STRING IDs first as this guarantees much faster server\n    responses. Use the `map` command for this.\n4.  **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\n## Tool Execution\n\nThe CLI is at `scripts\u002Fstring_cli.py` and should be run using `uv run`:\n\n```bash\nuv run scripts\u002Fstring_cli.py \u003Ccommand> [options] --output \u002Ftmp\u002Fout.tsv\n```\n\n## Feature Domains (Progressive Disclosure)\n\nRead the following reference files based on the user's request:\n\n*   **[Mapping Identifiers](references\u002Fmapping.md)** - Map common protein names\n    to STRING IDs.\n*   **[Interactions & Network](references\u002Finteractions.md)** - Find interacting\n    proteins, network topologies, mediators, homology, and visual network\n    images.\n*   **[Enrichment & Functional Annotations](references\u002Fenrichment.md)** -\n    Analyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all\n    proteins associated with a specific term (e.g. Melanoma).\n*   **[Values\u002FRanks Enrichment](references\u002Fvaluesranks.md)** - Submit full\n    experimental datasets (e.g., logFC, p-values) for rank-based enrichment\n    analysis using the async background API.\n\nTo begin, read the reference file most appropriate to the current task to\ndiscover the correct CLI command.\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,47,53,60,115,121,203,209,230,290,296,301,361,366],{"type":39,"tag":40,"props":41,"children":43},"element","h1",{"id":42},"string-database-skill",[44],{"type":45,"value":46},"text","STRING Database Skill",{"type":39,"tag":48,"props":49,"children":50},"p",{},[51],{"type":45,"value":52},"This skill allows you to query the STRING database programmatically using a\nbundled Python CLI wrapper.",{"type":39,"tag":54,"props":55,"children":57},"h2",{"id":56},"prerequisites",[58],{"type":45,"value":59},"Prerequisites",{"type":39,"tag":61,"props":62,"children":63},"ol",{},[64,95],{"type":39,"tag":65,"props":66,"children":67},"li",{},[68,79,81,86,88,93],{"type":39,"tag":69,"props":70,"children":71},"strong",{},[72],{"type":39,"tag":73,"props":74,"children":76},"code",{"className":75},[],[77],{"type":45,"value":78},"uv",{"type":45,"value":80},": Read the ",{"type":39,"tag":73,"props":82,"children":84},{"className":83},[],[85],{"type":45,"value":78},{"type":45,"value":87}," skill and follow its Setup instructions to ensure\n",{"type":39,"tag":73,"props":89,"children":91},{"className":90},[],[92],{"type":45,"value":78},{"type":45,"value":94}," is installed and on PATH.",{"type":39,"tag":65,"props":96,"children":97},{},[98,103,105,113],{"type":39,"tag":69,"props":99,"children":100},{},[101],{"type":45,"value":102},"User Notification",{"type":45,"value":104},": If .licenses\u002Fstring_database_LICENSE.txt does not\nalready exist in the workspace root directory then (1) prominently notify\nthe user to check the terms at ",{"type":39,"tag":106,"props":107,"children":111},"a",{"href":108,"rel":109},"https:\u002F\u002Fstring-db.org\u002Fcgi\u002Faccess",[110],"nofollow",[112],{"type":45,"value":108},{"type":45,"value":114},", then (2)\ncreate the file recording the notification text and timestamp.",{"type":39,"tag":54,"props":116,"children":118},{"id":117},"core-rules",[119],{"type":45,"value":120},"Core Rules",{"type":39,"tag":61,"props":122,"children":123},{},[124,141,175,193],{"type":39,"tag":65,"props":125,"children":126},{},[127,132,134,139],{"type":39,"tag":69,"props":128,"children":129},{},[130],{"type":45,"value":131},"MANDATORY: Ask for Species First:",{"type":45,"value":133}," The STRING API requires NCBI Taxon\nIDs. ",{"type":39,"tag":69,"props":135,"children":136},{},[137],{"type":45,"value":138},"You MUST NOT guess or assume a species.",{"type":45,"value":140}," If the user does not\nexplicitly state a species or Taxon ID, you MUST stop and ask: \"Which\nspecies are you interested in? I need the NCBI Taxon ID to proceed.\" Even\nfor well-known proteins like TP53, BRCA1, or MDM2 that are commonly\nassociated with human studies, you MUST still ask — do not default to Human.",{"type":39,"tag":65,"props":142,"children":143},{},[144,149,151,157,159,165,167,173],{"type":39,"tag":69,"props":145,"children":146},{},[147],{"type":45,"value":148},"Never print output to stdout:",{"type":45,"value":150}," The ",{"type":39,"tag":73,"props":152,"children":154},{"className":153},[],[155],{"type":45,"value":156},"--output \u003Cfile.tsv>",{"type":45,"value":158}," is required.\nNever read large outputs into context. Instead use jq, python or file\noperations (",{"type":39,"tag":73,"props":160,"children":162},{"className":161},[],[163],{"type":45,"value":164},"grep",{"type":45,"value":166},", ",{"type":39,"tag":73,"props":168,"children":170},{"className":169},[],[171],{"type":45,"value":172},"head",{"type":45,"value":174},") to process large output.",{"type":39,"tag":65,"props":176,"children":177},{},[178,183,185,191],{"type":39,"tag":69,"props":179,"children":180},{},[181],{"type":45,"value":182},"Map Identifiers first:",{"type":45,"value":184}," If you only have common gene names (e.g.,\n'TP53'), map them to STRING IDs first as this guarantees much faster server\nresponses. Use the ",{"type":39,"tag":73,"props":186,"children":188},{"className":187},[],[189],{"type":45,"value":190},"map",{"type":45,"value":192}," command for this.",{"type":39,"tag":65,"props":194,"children":195},{},[196,201],{"type":39,"tag":69,"props":197,"children":198},{},[199],{"type":45,"value":200},"Notification",{"type":45,"value":202},": If this skill is used, ensure this is mentioned in the\noutput.",{"type":39,"tag":54,"props":204,"children":206},{"id":205},"tool-execution",[207],{"type":45,"value":208},"Tool Execution",{"type":39,"tag":48,"props":210,"children":211},{},[212,214,220,222,228],{"type":45,"value":213},"The CLI is at ",{"type":39,"tag":73,"props":215,"children":217},{"className":216},[],[218],{"type":45,"value":219},"scripts\u002Fstring_cli.py",{"type":45,"value":221}," and should be run using ",{"type":39,"tag":73,"props":223,"children":225},{"className":224},[],[226],{"type":45,"value":227},"uv run",{"type":45,"value":229},":",{"type":39,"tag":231,"props":232,"children":237},"pre",{"className":233,"code":234,"language":235,"meta":236,"style":236},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","uv run scripts\u002Fstring_cli.py \u003Ccommand> [options] --output \u002Ftmp\u002Fout.tsv\n","bash","",[238],{"type":39,"tag":73,"props":239,"children":240},{"__ignoreMap":236},[241],{"type":39,"tag":242,"props":243,"children":246},"span",{"class":244,"line":245},"line",1,[247,252,258,263,269,274,280,285],{"type":39,"tag":242,"props":248,"children":250},{"style":249},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[251],{"type":45,"value":78},{"type":39,"tag":242,"props":253,"children":255},{"style":254},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[256],{"type":45,"value":257}," run",{"type":39,"tag":242,"props":259,"children":260},{"style":254},[261],{"type":45,"value":262}," scripts\u002Fstring_cli.py",{"type":39,"tag":242,"props":264,"children":266},{"style":265},"--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF",[267],{"type":45,"value":268}," \u003C",{"type":39,"tag":242,"props":270,"children":271},{"style":254},[272],{"type":45,"value":273},"comman",{"type":39,"tag":242,"props":275,"children":277},{"style":276},"--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8",[278],{"type":45,"value":279},"d",{"type":39,"tag":242,"props":281,"children":282},{"style":265},[283],{"type":45,"value":284},">",{"type":39,"tag":242,"props":286,"children":287},{"style":276},[288],{"type":45,"value":289}," [options] --output \u002Ftmp\u002Fout.tsv\n",{"type":39,"tag":54,"props":291,"children":293},{"id":292},"feature-domains-progressive-disclosure",[294],{"type":45,"value":295},"Feature Domains (Progressive Disclosure)",{"type":39,"tag":48,"props":297,"children":298},{},[299],{"type":45,"value":300},"Read the following reference files based on the user's request:",{"type":39,"tag":302,"props":303,"children":304},"ul",{},[305,319,333,347],{"type":39,"tag":65,"props":306,"children":307},{},[308,317],{"type":39,"tag":69,"props":309,"children":310},{},[311],{"type":39,"tag":106,"props":312,"children":314},{"href":313},"references\u002Fmapping.md",[315],{"type":45,"value":316},"Mapping Identifiers",{"type":45,"value":318}," - Map common protein names\nto STRING IDs.",{"type":39,"tag":65,"props":320,"children":321},{},[322,331],{"type":39,"tag":69,"props":323,"children":324},{},[325],{"type":39,"tag":106,"props":326,"children":328},{"href":327},"references\u002Finteractions.md",[329],{"type":45,"value":330},"Interactions & Network",{"type":45,"value":332}," - Find interacting\nproteins, network topologies, mediators, homology, and visual network\nimages.",{"type":39,"tag":65,"props":334,"children":335},{},[336,345],{"type":39,"tag":69,"props":337,"children":338},{},[339],{"type":39,"tag":106,"props":340,"children":342},{"href":341},"references\u002Fenrichment.md",[343],{"type":45,"value":344},"Enrichment & Functional Annotations",{"type":45,"value":346}," -\nAnalyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all\nproteins associated with a specific term (e.g. Melanoma).",{"type":39,"tag":65,"props":348,"children":349},{},[350,359],{"type":39,"tag":69,"props":351,"children":352},{},[353],{"type":39,"tag":106,"props":354,"children":356},{"href":355},"references\u002Fvaluesranks.md",[357],{"type":45,"value":358},"Values\u002FRanks Enrichment",{"type":45,"value":360}," - Submit full\nexperimental datasets (e.g., logFC, p-values) for rank-based enrichment\nanalysis using the async background API.",{"type":39,"tag":48,"props":362,"children":363},{},[364],{"type":45,"value":365},"To begin, read the reference file most appropriate to the current task to\ndiscover the correct CLI command.",{"type":39,"tag":367,"props":368,"children":369},"style",{},[370],{"type":45,"value":371},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":373,"total":537},[374,387,402,422,434,449,465,478,490,505,516,526],{"slug":375,"name":375,"fn":376,"description":377,"org":378,"tags":379,"stars":22,"repoUrl":23,"updatedAt":386},"alphafold-database-fetch-and-analyze","retrieve and analyze AlphaFold protein structures","Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[380,381,384,385],{"name":20,"slug":21,"type":15},{"name":382,"slug":383,"type":15},"Genomics","genomics",{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:51.827211",{"slug":388,"name":388,"fn":389,"description":390,"org":391,"tags":392,"stars":22,"repoUrl":23,"updatedAt":401},"alphagenome-single-variant-analysis","analyze genetic variant effects with AlphaGenome","Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue\u002Fcell-type ontologies (UBERON\u002FCL) or analyzing variants in chr:pos:ref>alt format.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[393,394,397,398],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":399,"slug":400,"type":15},"RNA-seq","rna-seq","2026-07-12T07:51:39.494803",{"slug":403,"name":403,"fn":404,"description":405,"org":406,"tags":407,"stars":22,"repoUrl":23,"updatedAt":421},"chembl-database","query ChEMBL database for bioactive molecules","Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50\u002FKi values, drug mechanisms, or structure searches.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[408,411,414,417,420],{"name":409,"slug":410,"type":15},"ChEMBL","chembl",{"name":412,"slug":413,"type":15},"Chemistry","chemistry",{"name":415,"slug":416,"type":15},"Database","database",{"name":418,"slug":419,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":423,"name":423,"fn":424,"description":425,"org":426,"tags":427,"stars":22,"repoUrl":23,"updatedAt":433},"clinical-trials-database","query clinical trial data","Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility\u002Finclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[428,431,432],{"name":429,"slug":430,"type":15},"Clinical Trials","clinical-trials",{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:06.846705",{"slug":435,"name":435,"fn":436,"description":437,"org":438,"tags":439,"stars":22,"repoUrl":23,"updatedAt":448},"clinvar-database","retrieve clinical significance from ClinVar database","Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding \"hard positive\" benchmark controls for human genomic variants.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[440,443,444,447],{"name":441,"slug":442,"type":15},"ClinVar","clinvar",{"name":395,"slug":396,"type":15},{"name":445,"slug":446,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":450,"name":450,"fn":451,"description":452,"org":453,"tags":454,"stars":22,"repoUrl":23,"updatedAt":464},"credentials","manage and verify API credentials safely","Instructions for handling API keys and credentials safely, verifying their presence, and prompting the user to add them if missing using a safe protocol.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[455,458,461],{"name":456,"slug":457,"type":15},"Compliance","compliance",{"name":459,"slug":460,"type":15},"Operations","operations",{"name":462,"slug":463,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":466,"name":466,"fn":467,"description":468,"org":469,"tags":470,"stars":22,"repoUrl":23,"updatedAt":477},"dbsnp-database","search genetic variants in dbSNP database","Use when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database. Resolves between rsIDs, genomic coordinates in VCF format, and HGVS strings. For an rsID, returns variant type, gene associations, clinical significance, allele frequencies, and genomic coordinates (GRCh38).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[471,472,473,476],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},{"name":474,"slug":475,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",{"slug":479,"name":479,"fn":480,"description":481,"org":482,"tags":483,"stars":22,"repoUrl":23,"updatedAt":489},"embl-ebi-ols","search biomedical ontologies in EMBL-EBI OLS","Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[484,485,488],{"name":20,"slug":21,"type":15},{"name":486,"slug":487,"type":15},"Ontology","ontology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":491,"name":491,"fn":492,"description":493,"org":494,"tags":495,"stars":22,"repoUrl":23,"updatedAt":504},"encode-ccres-database","query ENCODE regulatory and experimental data","Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). 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},[496,497,500,501],{"name":20,"slug":21,"type":15},{"name":498,"slug":499,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":502,"slug":503,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":506,"name":506,"fn":507,"description":508,"org":509,"tags":510,"stars":22,"repoUrl":23,"updatedAt":515},"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},[511,512,513,514],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":517,"name":517,"fn":518,"description":519,"org":520,"tags":521,"stars":22,"repoUrl":23,"updatedAt":525},"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},[522,523,524],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:09.354992",{"slug":527,"name":527,"fn":528,"description":529,"org":530,"tags":531,"stars":22,"repoUrl":23,"updatedAt":536},"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},[532,533,534,535],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009",38,{"items":539,"total":537},[540,547,554,562,568,575,581],{"slug":375,"name":375,"fn":376,"description":377,"org":541,"tags":542,"stars":22,"repoUrl":23,"updatedAt":386},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[543,544,545,546],{"name":20,"slug":21,"type":15},{"name":382,"slug":383,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":388,"name":388,"fn":389,"description":390,"org":548,"tags":549,"stars":22,"repoUrl":23,"updatedAt":401},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[550,551,552,553],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},{"name":13,"slug":14,"type":15},{"name":399,"slug":400,"type":15},{"slug":403,"name":403,"fn":404,"description":405,"org":555,"tags":556,"stars":22,"repoUrl":23,"updatedAt":421},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[557,558,559,560,561],{"name":409,"slug":410,"type":15},{"name":412,"slug":413,"type":15},{"name":415,"slug":416,"type":15},{"name":418,"slug":419,"type":15},{"name":13,"slug":14,"type":15},{"slug":423,"name":423,"fn":424,"description":425,"org":563,"tags":564,"stars":22,"repoUrl":23,"updatedAt":433},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[565,566,567],{"name":429,"slug":430,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":435,"name":435,"fn":436,"description":437,"org":569,"tags":570,"stars":22,"repoUrl":23,"updatedAt":448},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[571,572,573,574],{"name":441,"slug":442,"type":15},{"name":395,"slug":396,"type":15},{"name":445,"slug":446,"type":15},{"name":13,"slug":14,"type":15},{"slug":450,"name":450,"fn":451,"description":452,"org":576,"tags":577,"stars":22,"repoUrl":23,"updatedAt":464},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[578,579,580],{"name":456,"slug":457,"type":15},{"name":459,"slug":460,"type":15},{"name":462,"slug":463,"type":15},{"slug":466,"name":466,"fn":467,"description":468,"org":582,"tags":583,"stars":22,"repoUrl":23,"updatedAt":477},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[584,585,586,587],{"name":20,"slug":21,"type":15},{"name":395,"slug":396,"type":15},{"name":474,"slug":475,"type":15},{"name":13,"slug":14,"type":15}]