[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-encode-ccres-database":3,"mdc--8eafll-key":36,"related-org-google-deepmind-encode-ccres-database":1472,"related-repo-google-deepmind-encode-ccres-database":1632},{"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},"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},"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},"GraphQL","graphql",{"name":20,"slug":21,"type":15},"Bioinformatics","bioinformatics",{"name":23,"slug":24,"type":15},"REST API","rest-api",2333,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills","2026-07-12T07:52:10.597139",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\u002Fencode_ccres_database","---\nname: encode-ccres-database\ndescription: >\n    Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN\n    GraphQL API, or make custom queries to the ENCODE Portal REST API for\n    experiments and files (ChIP-seq peaks, etc.). Use when you want to query\n    regulatory annotations or raw experimental data across human cell types.\n---\n\n# ENCODE Database Skill\n\nThis skill allows you to query the ENCODE Registry of cCREs (candidate\ncis-Regulatory Elements) via the SCREEN GraphQL API. It helps identify\nfunctional non-coding DNA elements (like Promoters, Enhancers, and insulators)\nby analyzing biochemical signatures (DNase, H3K4me3, H3K27ac, CTCF).\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\u002Fencode_ccres_database_LICENSE.txt does\n    not already exist in the workspace root directory then (1) prominently\n    notify the user to check the terms at\n    https:\u002F\u002Fwww.encodeproject.org\u002Fhelp\u002Frest-api\u002F, then (2) create the file\n    recording the notification text and timestamp.\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-   **Parsing Output**: Do NOT use `cat` to read the entire JSON output file\n    into context, as it can be extremely large. You MUST use `jq` to efficiently\n    parse and extract relevant fields.\n-   **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\n### Quick Start\n\n```bash\n# Search cCREs by coordinates\nuv run scripts\u002Fscreen_api.py search --chromosome chr11 \\\n  --start 5205263 --end 5207263 \\\n  --output \u002Ftmp\u002Fsearch.json\n\n# Get details for a specific cCRE\nuv run scripts\u002Fscreen_api.py details EH38E2941922 \\\n  --output \u002Ftmp\u002Fdetails.json\n```\n\nAll subcommands write JSON to disk. Always save output in a temporary location\nlike `\u002Ftmp\u002F`.\n\n### Identifying High-Confidence (\"Type A\") Biosamples\n\nBiosamples in ENCODE are often categorized by their data completeness. **\"Type\nA\"** (or high-confidence) biosamples are those that have experimental data for\nall four core epigenetic markers: **DNase, H3K4me3, H3K27ac, and CTCF**.\n\nThe `biosamples` and `details` commands automatically enrich their output with\nan `is_type_a` boolean flag for each biosample.\n\n**Example: Finding high-confidence cell types**\n\n```bash\nuv run scripts\u002Fscreen_api.py biosamples --output \u002Ftmp\u002Fbiosamples.json\n# Use jq to filter for Type A biosamples\njq '.data.ccREBiosampleQuery.biosamples[] | select(.is_type_a == true) | .displayname' \u002Ftmp\u002Fbiosamples.json\n```\n\n### Parsing Output (CRITICAL)\n\n**Do NOT use `cat` to read the entire JSON output file into context, as it**\n**can be extremely large.** Instead, you MUST use `jq` to efficiently parse and\nextract the relevant fields from the JSON file saved by the script. If `jq` is\nnot available on the system, write your own Python filtering code (e.g.,\n`python3 -c \"import json...\"`) to extract the necessary data.\n\nFor a complete reference of the JSON structure returned by eachmcommand (so you\nknow which fields to query with `jq`), read\n`references\u002Fjson_output_structure.md`.\n\n### Available Commands\n\n-   `search`: Search cCREs by coordinates, accessions, or epigenetic signals.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py search \\\n        --chromosome chr11 --start 5205263 --end 5207263 \\\n        --output \u002Ftmp\u002Fsearch.json\n    ```\n\n-   `nearby-genes`: Find nearby genes for given cCRE accessions.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py nearby-genes \\\n        EH38E1516972 --output \u002Ftmp\u002Fnearby.json\n    ```\n\n-   `details`: Get detailed information and biosample-specific max Z-scores for\n    a specific cCRE.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py details EH38E2941922 \\\n        --output \u002Ftmp\u002Fdetails.json\n    ```\n\n-   `biosamples`: Get biosample metadata for an assembly.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py biosamples \\\n        --output \u002Ftmp\u002Fbiosamples.json\n    ```\n\n-   `orthologs`: Get orthologous cCREs in another assembly.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py orthologs EH38E2941922 \\\n        --output \u002Ftmp\u002Forthologs.json\n    ```\n\n-   `linked-genes`: Find linked genes via methods like HiC or eQTLs.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py linked-genes \\\n        EH38E1516972 --output \u002Ftmp\u002Flinked.json\n    ```\n\n-   `gene-expression`: Get gene expression (TPM) across all biosamples for a\n    named gene. Internally resolves the gene symbol to an Ensembl gene ID, then\n    queries per-biosample RNA-seq quantifications.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py gene-expression GAPDH \\\n        --output \u002Ftmp\u002Fgene_expr.json\n    ```\n\n-   `entex`: Get ENTEx data for a cCRE or genomic region.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py entex \\\n        --accession EH38E1310345 \\\n        --output \u002Ftmp\u002Fentex.json\n    ```\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py entex \\\n        --region chr1:1000068:1000409 \\\n        --output \u002Ftmp\u002Fentex.json\n    ```\n\n-   `gwas`: Query genome-wide association studies, SNPs, or enrichment data.\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py gwas studies \\\n        --output \u002Ftmp\u002Fgwas.json\n    ```\n\n    ```bash\n    uv run scripts\u002Fscreen_api.py gwas snps --study \\\n        Ahola-Olli_AV-27989323-Eotaxin_levels \\\n        --output \u002Ftmp\u002Fgwas_snps.json\n    ```\n\nYou can supply the `--assembly mm10` or `--assembly grch38` flag to explicitly\nrequest a specific assembly for most commands. By default, the script targets\n`grch38` but will automatically fall back to `mm10` if no results are found or\nif the query fails.\n\n## ENCODE Portal REST API (Direct Access)\n\nFor accessing raw experiments, ChIP-seq peaks, or other datasets that are not\nrepresented as cCREs in SCREEN, use the `scripts\u002Fencode_portal_api.py` script.\nIt allows custom queries to the ENCODE Portal REST API.\n\n### Usage\n\n```bash\nuv run scripts\u002Fencode_portal_api.py search \"type=Experiment&target.label=ZNF549\" --output \u002Ftmp\u002Fznf549_experiments.json\n```\n\n### Data Analysis Tips\n\nWhen analyzing `.bed` or `.bigBed` files downloaded from ENCODE, standard\nbioinformatics tools are highly recommended for finding overlaps (e.g., between\ngene promoters and peaks):\n\n-   **`bedtools`**: For fast mathematical operations on genomic intervals.\n-   **`bigBedToBed`**: For converting binary BigBed files to readable BED\n    format.\n-   **`pybedtools`**: A Python wrapper for `bedtools`.\n\nWrite custom logic if these tools are not pre-installed.\n\n## Custom Queries (SCREEN GraphQL)\n\nIf you need to make a complex GraphQL query that the script does not support,\nread `references\u002Fgraphql_schema.md` for a reference of available queries,\narguments, and return fields in the SCREEN GraphQL API.\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,56,63,118,124,174,181,350,363,369,387,416,424,496,502,546,565,571,1252,1289,1295,1308,1314,1365,1371,1391,1442,1447,1453,1466],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"encode-database-skill",[47],{"type":48,"value":49},"text","ENCODE Database Skill",{"type":42,"tag":51,"props":52,"children":53},"p",{},[54],{"type":48,"value":55},"This skill allows you to query the ENCODE Registry of cCREs (candidate\ncis-Regulatory Elements) via the SCREEN GraphQL API. It helps identify\nfunctional non-coding DNA elements (like Promoters, Enhancers, and insulators)\nby analyzing biochemical signatures (DNase, H3K4me3, H3K27ac, CTCF).",{"type":42,"tag":57,"props":58,"children":60},"h2",{"id":59},"prerequisites",[61],{"type":48,"value":62},"Prerequisites",{"type":42,"tag":64,"props":65,"children":66},"ol",{},[67,98],{"type":42,"tag":68,"props":69,"children":70},"li",{},[71,82,84,89,91,96],{"type":42,"tag":72,"props":73,"children":74},"strong",{},[75],{"type":42,"tag":76,"props":77,"children":79},"code",{"className":78},[],[80],{"type":48,"value":81},"uv",{"type":48,"value":83},": Read the ",{"type":42,"tag":76,"props":85,"children":87},{"className":86},[],[88],{"type":48,"value":81},{"type":48,"value":90}," skill and follow its Setup instructions to ensure\n",{"type":42,"tag":76,"props":92,"children":94},{"className":93},[],[95],{"type":48,"value":81},{"type":48,"value":97}," is installed and on PATH.",{"type":42,"tag":68,"props":99,"children":100},{},[101,106,108,116],{"type":42,"tag":72,"props":102,"children":103},{},[104],{"type":48,"value":105},"User Notification",{"type":48,"value":107},": If .licenses\u002Fencode_ccres_database_LICENSE.txt does\nnot already exist in the workspace root directory then (1) prominently\nnotify the user to check the terms at\n",{"type":42,"tag":109,"props":110,"children":114},"a",{"href":111,"rel":112},"https:\u002F\u002Fwww.encodeproject.org\u002Fhelp\u002Frest-api\u002F",[113],"nofollow",[115],{"type":48,"value":111},{"type":48,"value":117},", then (2) create the file\nrecording the notification text and timestamp.",{"type":42,"tag":57,"props":119,"children":121},{"id":120},"core-rules",[122],{"type":48,"value":123},"Core Rules",{"type":42,"tag":125,"props":126,"children":127},"ul",{},[128,138,164],{"type":42,"tag":68,"props":129,"children":130},{},[131,136],{"type":42,"tag":72,"props":132,"children":133},{},[134],{"type":48,"value":135},"Use the Wrapper",{"type":48,"value":137},": ALWAYS execute the provided helper scripts to query the\ndatabase rather than accessing the database directly. The scripts\nautomatically enforce the required rate limit gracefully.",{"type":42,"tag":68,"props":139,"children":140},{},[141,146,148,154,156,162],{"type":42,"tag":72,"props":142,"children":143},{},[144],{"type":48,"value":145},"Parsing Output",{"type":48,"value":147},": Do NOT use ",{"type":42,"tag":76,"props":149,"children":151},{"className":150},[],[152],{"type":48,"value":153},"cat",{"type":48,"value":155}," to read the entire JSON output file\ninto context, as it can be extremely large. You MUST use ",{"type":42,"tag":76,"props":157,"children":159},{"className":158},[],[160],{"type":48,"value":161},"jq",{"type":48,"value":163}," to efficiently\nparse and extract relevant fields.",{"type":42,"tag":68,"props":165,"children":166},{},[167,172],{"type":42,"tag":72,"props":168,"children":169},{},[170],{"type":48,"value":171},"Notification",{"type":48,"value":173},": If this skill is used, ensure this is mentioned in the\noutput.",{"type":42,"tag":175,"props":176,"children":178},"h3",{"id":177},"quick-start",[179],{"type":48,"value":180},"Quick Start",{"type":42,"tag":182,"props":183,"children":188},"pre",{"className":184,"code":185,"language":186,"meta":187,"style":187},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Search cCREs by coordinates\nuv run scripts\u002Fscreen_api.py search --chromosome chr11 \\\n  --start 5205263 --end 5207263 \\\n  --output \u002Ftmp\u002Fsearch.json\n\n# Get details for a specific cCRE\nuv run scripts\u002Fscreen_api.py details EH38E2941922 \\\n  --output \u002Ftmp\u002Fdetails.json\n","bash","",[189],{"type":42,"tag":76,"props":190,"children":191},{"__ignoreMap":187},[192,204,245,274,288,298,307,337],{"type":42,"tag":193,"props":194,"children":197},"span",{"class":195,"line":196},"line",1,[198],{"type":42,"tag":193,"props":199,"children":201},{"style":200},"--shiki-light:#90A4AE;--shiki-light-font-style:italic;--shiki-default:#546E7A;--shiki-default-font-style:italic;--shiki-dark:#676E95;--shiki-dark-font-style:italic",[202],{"type":48,"value":203},"# Search cCREs by coordinates\n",{"type":42,"tag":193,"props":205,"children":207},{"class":195,"line":206},2,[208,213,219,224,229,234,239],{"type":42,"tag":193,"props":209,"children":211},{"style":210},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[212],{"type":48,"value":81},{"type":42,"tag":193,"props":214,"children":216},{"style":215},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[217],{"type":48,"value":218}," run",{"type":42,"tag":193,"props":220,"children":221},{"style":215},[222],{"type":48,"value":223}," scripts\u002Fscreen_api.py",{"type":42,"tag":193,"props":225,"children":226},{"style":215},[227],{"type":48,"value":228}," search",{"type":42,"tag":193,"props":230,"children":231},{"style":215},[232],{"type":48,"value":233}," --chromosome",{"type":42,"tag":193,"props":235,"children":236},{"style":215},[237],{"type":48,"value":238}," chr11",{"type":42,"tag":193,"props":240,"children":242},{"style":241},"--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8",[243],{"type":48,"value":244}," \\\n",{"type":42,"tag":193,"props":246,"children":248},{"class":195,"line":247},3,[249,254,260,265,270],{"type":42,"tag":193,"props":250,"children":251},{"style":215},[252],{"type":48,"value":253},"  --start",{"type":42,"tag":193,"props":255,"children":257},{"style":256},"--shiki-light:#F76D47;--shiki-default:#F78C6C;--shiki-dark:#F78C6C",[258],{"type":48,"value":259}," 5205263",{"type":42,"tag":193,"props":261,"children":262},{"style":215},[263],{"type":48,"value":264}," --end",{"type":42,"tag":193,"props":266,"children":267},{"style":256},[268],{"type":48,"value":269}," 5207263",{"type":42,"tag":193,"props":271,"children":272},{"style":241},[273],{"type":48,"value":244},{"type":42,"tag":193,"props":275,"children":277},{"class":195,"line":276},4,[278,283],{"type":42,"tag":193,"props":279,"children":280},{"style":215},[281],{"type":48,"value":282},"  --output",{"type":42,"tag":193,"props":284,"children":285},{"style":215},[286],{"type":48,"value":287}," \u002Ftmp\u002Fsearch.json\n",{"type":42,"tag":193,"props":289,"children":291},{"class":195,"line":290},5,[292],{"type":42,"tag":193,"props":293,"children":295},{"emptyLinePlaceholder":294},true,[296],{"type":48,"value":297},"\n",{"type":42,"tag":193,"props":299,"children":301},{"class":195,"line":300},6,[302],{"type":42,"tag":193,"props":303,"children":304},{"style":200},[305],{"type":48,"value":306},"# Get details for a specific cCRE\n",{"type":42,"tag":193,"props":308,"children":310},{"class":195,"line":309},7,[311,315,319,323,328,333],{"type":42,"tag":193,"props":312,"children":313},{"style":210},[314],{"type":48,"value":81},{"type":42,"tag":193,"props":316,"children":317},{"style":215},[318],{"type":48,"value":218},{"type":42,"tag":193,"props":320,"children":321},{"style":215},[322],{"type":48,"value":223},{"type":42,"tag":193,"props":324,"children":325},{"style":215},[326],{"type":48,"value":327}," details",{"type":42,"tag":193,"props":329,"children":330},{"style":215},[331],{"type":48,"value":332}," EH38E2941922",{"type":42,"tag":193,"props":334,"children":335},{"style":241},[336],{"type":48,"value":244},{"type":42,"tag":193,"props":338,"children":340},{"class":195,"line":339},8,[341,345],{"type":42,"tag":193,"props":342,"children":343},{"style":215},[344],{"type":48,"value":282},{"type":42,"tag":193,"props":346,"children":347},{"style":215},[348],{"type":48,"value":349}," \u002Ftmp\u002Fdetails.json\n",{"type":42,"tag":51,"props":351,"children":352},{},[353,355,361],{"type":48,"value":354},"All subcommands write JSON to disk. Always save output in a temporary location\nlike ",{"type":42,"tag":76,"props":356,"children":358},{"className":357},[],[359],{"type":48,"value":360},"\u002Ftmp\u002F",{"type":48,"value":362},".",{"type":42,"tag":175,"props":364,"children":366},{"id":365},"identifying-high-confidence-type-a-biosamples",[367],{"type":48,"value":368},"Identifying High-Confidence (\"Type A\") Biosamples",{"type":42,"tag":51,"props":370,"children":371},{},[372,374,379,381,386],{"type":48,"value":373},"Biosamples in ENCODE are often categorized by their data completeness. 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var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1473,"total":1631},[1474,1489,1504,1524,1536,1551,1567,1580,1592,1599,1610,1620],{"slug":1475,"name":1475,"fn":1476,"description":1477,"org":1478,"tags":1479,"stars":25,"repoUrl":26,"updatedAt":1488},"alphafold-database-fetch-and-analyze","retrieve and analyze AlphaFold protein structures","Retrieve and analyze AlphaFold predicted structures for a protein. 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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},[1495,1496,1499,1500],{"name":20,"slug":21,"type":15},{"name":1497,"slug":1498,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":1501,"slug":1502,"type":15},"RNA-seq","rna-seq","2026-07-12T07:51:39.494803",{"slug":1505,"name":1505,"fn":1506,"description":1507,"org":1508,"tags":1509,"stars":25,"repoUrl":26,"updatedAt":1523},"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},[1510,1513,1516,1519,1522],{"name":1511,"slug":1512,"type":15},"ChEMBL","chembl",{"name":1514,"slug":1515,"type":15},"Chemistry","chemistry",{"name":1517,"slug":1518,"type":15},"Database","database",{"name":1520,"slug":1521,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":1525,"name":1525,"fn":1526,"description":1527,"org":1528,"tags":1529,"stars":25,"repoUrl":26,"updatedAt":1535},"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},[1530,1533,1534],{"name":1531,"slug":1532,"type":15},"Clinical Trials","clinical-trials",{"name":1485,"slug":1486,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:06.846705",{"slug":1537,"name":1537,"fn":1538,"description":1539,"org":1540,"tags":1541,"stars":25,"repoUrl":26,"updatedAt":1550},"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},[1542,1545,1546,1549],{"name":1543,"slug":1544,"type":15},"ClinVar","clinvar",{"name":1497,"slug":1498,"type":15},{"name":1547,"slug":1548,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":1552,"name":1552,"fn":1553,"description":1554,"org":1555,"tags":1556,"stars":25,"repoUrl":26,"updatedAt":1566},"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},[1557,1560,1563],{"name":1558,"slug":1559,"type":15},"Compliance","compliance",{"name":1561,"slug":1562,"type":15},"Operations","operations",{"name":1564,"slug":1565,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":1568,"name":1568,"fn":1569,"description":1570,"org":1571,"tags":1572,"stars":25,"repoUrl":26,"updatedAt":1579},"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},[1573,1574,1575,1578],{"name":20,"slug":21,"type":15},{"name":1497,"slug":1498,"type":15},{"name":1576,"slug":1577,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",{"slug":1581,"name":1581,"fn":1582,"description":1583,"org":1584,"tags":1585,"stars":25,"repoUrl":26,"updatedAt":1591},"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},[1586,1587,1590],{"name":20,"slug":21,"type":15},{"name":1588,"slug":1589,"type":15},"Ontology","ontology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":4,"name":4,"fn":5,"description":6,"org":1593,"tags":1594,"stars":25,"repoUrl":26,"updatedAt":27},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1595,1596,1597,1598],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"slug":1600,"name":1600,"fn":1601,"description":1602,"org":1603,"tags":1604,"stars":25,"repoUrl":26,"updatedAt":1609},"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},[1605,1606,1607,1608],{"name":20,"slug":21,"type":15},{"name":1497,"slug":1498,"type":15},{"name":1485,"slug":1486,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":1611,"name":1611,"fn":1612,"description":1613,"org":1614,"tags":1615,"stars":25,"repoUrl":26,"updatedAt":1619},"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. 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