[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-opentargets-database":3,"mdc--b592n-key":33,"related-org-google-deepmind-opentargets-database":1572,"related-repo-google-deepmind-opentargets-database":1738},{"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},"opentargets-database","query Open Targets for drug discovery","Query Open Targets Platform for target-disease associations, drug target discovery, tractability\u002Fsafety data, genetics\u002Fomics evidence, known drugs, for therapeutic target identification.\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","2026-07-12T07:51:44.181023",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\u002Fopentargets_database","---\nname: opentargets-database\ndescription: >\n  Query Open Targets Platform for target-disease associations, drug target\n  discovery, tractability\u002Fsafety data, genetics\u002Fomics evidence, known drugs,\n  for therapeutic target identification.\n---\n\n# Open Targets Database Skill\n\n## Overview\n\nThis skill provides access to the Open Targets Platform GraphQL API. It\naggregates multi-modal evidence from genetics (GWAS\u002FeQTL), pathways, animal\nmodels, and clinical trials to rank target-disease associations and identify\ndruggable genes.\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\u002Fopentargets_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\u002Fplatform-docs.opentargets.org\u002Flicence, 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 fair use and implement retry logic.\n-   **Output Flag**: The `--output` flag is always required as output can be\n    very large. Use `jq` or write your own code to process this JSON file.\n-   **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\n## Quick Reference\n\nAlways use the provided Python script `scripts\u002Fquery_opentargets.py` to quickly\nquery the database. It handles API communication, retries, formatting, and\nautomatically truncates overly large responses. NEVER write your own curl or\nsimilar requests.\n\n**Usage:**\n\n```bash\nuv run scripts\u002Fquery_opentargets.py --output \u002Ftmp\u002Fopentargets_results.json [OPTIONS] COMMAND [ARGS]...\n```\n\n**Common Options:**\n\n-   `--output PATH`: **Required**. Path to write the JSON output file.\n-   `--limit N`: Limit the number of items returned in arrays (default is 50).\n    Use a smaller number like 10 when doing preliminary exploration.\n-   `--page-size N`: Set the API pagination size (default is 200). Increase if\n    you need more results (e.g., a study with many credible sets).\n\n**Available Commands:**\n\n-   **`get-gwas-studies`** *`disease_id`*: Fetches all GWAS studies associated\n    with a specific disease ID (e.g. `MONDO_0008383` for Rheumatoid Arthritis).\n-   **`get-study-credible-sets`** *`study_id`*: Fetches all credible sets for a\n    given study ID (e.g. `FINNGEN_R12_RX_CROHN_2NDLINE`). Returns confidence,\n    finemapping method, variant, and p-value info.\n-   **`get-qtl-credible-sets`** *`variant_id`*: Retrieves QTL credible sets for\n    a specific variant ID (e.g. `19_44908822_C_T`).\n-   **`get-l2g`** *`variant_id [--study-id ID]`*: Returns Locus-to-Gene (L2G)\n    predictions\u002Fscores for a locus to identify the most likely causal gene. Only\n    `variant_id` is required; use `--study-id` to filter to a specific study.\n    Accepts `chr` prefix (e.g. `chr1_113834946_A_G`).\n-   **`get-target-druggability`** *`ensembl_id`*: Provides tractability data\n    (small molecule, antibody, etc.) and clinical trial safety info for a\n    gene\u002Ftarget.\n-   **`get-associated-targets`** *`disease_id`*: Find all target genes\n    associated with a specific disease ID (EFO or MONDO).\n-   **`get-disease-drugs`** *`disease_id [--min-stage STAGE]`*: Find all drugs\n    and clinical candidates associated with a disease. Use `--min-stage` to\n    filter (e.g., `PHASE_3` for Phase III or Approved).\n-   **`get-associated-diseases`** *`ensembl_id`*: Find all diseases associated\n    with a specific target Ensembl ID.\n-   **`search-disease`** *`query_string`*: Search for a disease by name to find\n    its ID and other metadata.\n-   **`get-credible-sets-near-target`** *`ensembl_id [--window N]`*: Fetches\n    credible sets for a target and filters them to those within a genomic window\n    around the target. Useful for finding variants \"nearby\" a gene.\n-   **`custom-query`** *`query [--variables '{}']`*: Run a raw GraphQL query for\n    any other Open Targets data.\n\n## L2G Query Usage\n\nThe `get-l2g` command has two modes:\n\n*   **Variant only** (`get-l2g \u003Cvariant_id>`): Returns L2G predictions from\n    **all credible sets across all studies** where that variant is the lead\n    variant. This can return a large number of results (e.g., hundreds). Use\n    this when the user wants a broad view of which gene is most likely causal at\n    a locus, or when no specific study is mentioned.\n*   **Variant + study** (`get-l2g \u003Cvariant_id> --study-id \u003Cstudy_id>`): Returns\n    L2G predictions only for credible sets from that specific study. Use this\n    when the user asks about a specific GWAS study or when you need to narrow\n    down the results.\n\n> **Incomplete results warning:** The variant-only mode can return hundreds of\n> credible sets. The default `--page-size` is 200, so if the API reports a\n> `count` higher than the number of `rows` returned, **you are seeing incomplete\n> results**. Always compare `count` to the actual number of rows. If they\n> differ, either increase `--page-size` or inform the user that only a subset\n> was retrieved.\n\n## Querying by Region\n\nTo find studies with variants \"nearby\" a gene, use\n`get-credible-sets-near-target`, which improves upon the base API by performing\na flexible search based on genomic position: `uv run\nscripts\u002Fquery_opentargets.py --output \u002Ftmp\u002Fresults.json\nget-credible-sets-near-target ENSG00000156515 --window 500000`\n\nNote that the Open Targets GraphQL schema includes a `regions` parameter for\n`credibleSets`, however it performs an exact match against pre-computed region\nstrings (e.g., `chr10:68769984-69903496`) and there is some missing data. Use\nget-credible-sets-near-target as it allows a genomic range overlap search.\n\nThis fetches credible sets associated with the target and filters them in Python\nbased on the variant's genomic position.\n\n## Advanced GraphQL Queries\n\nIf you need to query endpoints or fields not exposed by the built-in\nsubcommands, use the `custom-query` subcommand.\n\n**Before writing a custom query:** Read the reference documentation to\nunderstand the API schema, types, and see example queries. See\n[references\u002FOpenTargets_GraphQL_Guide.md](references\u002FOpenTargets_GraphQL_Guide)\nfor full schema details, endpoints, and examples.\n\n**Example: Finding drugs for a disease**\n\n```bash\nuv run scripts\u002Fquery_opentargets.py custom-query \\\n  query drugsForDisease($id: String!) {\n    disease(efoId: $id) {\n      name\n      drugAndClinicalCandidates {\n        count\n        rows {\n          maxClinicalStage\n          drug {\n            id\n            name\n          }\n        }\n      }\n    }\n  }' \\\n--variables '{\"id\": \"EFO_1001006\"}'\n--output '\u002Ftmp\u002Fopentargets_result.json'\n```\n\n## Confidence Star Ratings\n\nThe Open Targets Platform assigns a **confidence level** to each credible set\nbased on the fine-mapping method and quality checks. These correspond to star\nratings displayed in the platform UI:\n\n| Stars          | Confidence String (API value)                             |\n| -------------- | --------------------------------------------------------- |\n| ★★★★ (4 stars) | `SuSiE fine-mapped credible set with in-sample LD`        |\n| ★★★ (3 stars)  | `SuSiE fine-mapped credible set with out-of-sample LD`    |\n| ★★ (2 stars)   | `PICS fine-mapped credible set extracted from summary     |\n:                : statistics`                                               :\n| ★ (1 star)     | `PICS fine-mapped credible set based on reported top hit` |\n| None           | `Unknown confidence`                                      |\n\nWhen users ask about \"N-star confidence\", match their request to the\ncorresponding string in the `confidence` field of the API response.\n\n## Tips and Common Mistakes\n\n-   **ID Formats**:\n    -   Disease IDs are typically MONDO IDs (e.g. `MONDO_0008383` for Rheumatoid\n        Arthritis) or EFO IDs (e.g. `EFO_0009460`). Use the `search-disease`\n        command to find the correct ID.\n    -   Target IDs must be Ensembl IDs (e.g. `ENSG00000169083`), not HGNC\n        symbols. If you only have a gene symbol, you may need to map it first\n        using a custom GraphQL `search` query.\n    -   Variant IDs are formatted as `chromosome_position_ref_alt` (e.g.,\n        `1_154426264_C_T`). A `chr` prefix (e.g. `chr1_154426264_C_T`) is\n        automatically stripped by the tool.\n    -   Study IDs can be GWAS Catalog IDs (e.g. `GCST90204201`) or\n        project-specific IDs (e.g. `FINNGEN_R12_RX_CROHN_2NDLINE`).\n-   **Truncation**: The tool truncates arrays longer than `--limit` to protect\n    the context window. If you see `\"_truncated\"`, you can run the query again\n    with a higher limit if you specifically need more data, but be cautious with\n    large limit values. Always use the `--output` flag to save the result to a\n    file and avoid terminal output truncation.\n-   **Pagination and incomplete results**: The `--page-size` option\n    (default: 200) controls how many items are fetched from the API. **Always\n    check the `count` field in the response and compare it to the number of\n    `rows` actually returned.** If `count` > number of rows, you have incomplete\n    data — either increase `--page-size` to fetch more, or inform the user that\n    only a partial result set was returned. This is especially important for\n    `get-l2g` without `--study-id`, which can return hundreds of credible sets.\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,47,54,60,66,121,127,177,183,196,204,274,282,325,333,670,676,688,733,792,798,816,845,850,856,868,886,894,1193,1199,1211,1331,1344,1350,1566],{"type":39,"tag":40,"props":41,"children":43},"element","h1",{"id":42},"open-targets-database-skill",[44],{"type":45,"value":46},"text","Open Targets Database Skill",{"type":39,"tag":48,"props":49,"children":51},"h2",{"id":50},"overview",[52],{"type":45,"value":53},"Overview",{"type":39,"tag":55,"props":56,"children":57},"p",{},[58],{"type":45,"value":59},"This skill provides access to the Open Targets Platform GraphQL API. 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These correspond to star\nratings displayed in the platform UI:",{"type":39,"tag":1212,"props":1213,"children":1214},"table",{},[1215,1234],{"type":39,"tag":1216,"props":1217,"children":1218},"thead",{},[1219],{"type":39,"tag":1220,"props":1221,"children":1222},"tr",{},[1223,1229],{"type":39,"tag":1224,"props":1225,"children":1226},"th",{},[1227],{"type":45,"value":1228},"Stars",{"type":39,"tag":1224,"props":1230,"children":1231},{},[1232],{"type":45,"value":1233},"Confidence String (API value)",{"type":39,"tag":1235,"props":1236,"children":1237},"tbody",{},[1238,1256,1273,1286,1297,1314],{"type":39,"tag":1220,"props":1239,"children":1240},{},[1241,1247],{"type":39,"tag":1242,"props":1243,"children":1244},"td",{},[1245],{"type":45,"value":1246},"★★★★ (4 stars)",{"type":39,"tag":1242,"props":1248,"children":1249},{},[1250],{"type":39,"tag":79,"props":1251,"children":1253},{"className":1252},[],[1254],{"type":45,"value":1255},"SuSiE fine-mapped credible set with in-sample LD",{"type":39,"tag":1220,"props":1257,"children":1258},{},[1259,1264],{"type":39,"tag":1242,"props":1260,"children":1261},{},[1262],{"type":45,"value":1263},"★★★ (3 stars)",{"type":39,"tag":1242,"props":1265,"children":1266},{},[1267],{"type":39,"tag":79,"props":1268,"children":1270},{"className":1269},[],[1271],{"type":45,"value":1272},"SuSiE fine-mapped credible set with out-of-sample LD",{"type":39,"tag":1220,"props":1274,"children":1275},{},[1276,1281],{"type":39,"tag":1242,"props":1277,"children":1278},{},[1279],{"type":45,"value":1280},"★★ (2 stars)",{"type":39,"tag":1242,"props":1282,"children":1283},{},[1284],{"type":45,"value":1285},"`PICS fine-mapped credible set extracted from summary",{"type":39,"tag":1220,"props":1287,"children":1288},{},[1289,1294],{"type":39,"tag":1242,"props":1290,"children":1291},{},[1292],{"type":45,"value":1293},":                : statistics`                                               :",{"type":39,"tag":1242,"props":1295,"children":1296},{},[],{"type":39,"tag":1220,"props":1298,"children":1299},{},[1300,1305],{"type":39,"tag":1242,"props":1301,"children":1302},{},[1303],{"type":45,"value":1304},"★ (1 star)",{"type":39,"tag":1242,"props":1306,"children":1307},{},[1308],{"type":39,"tag":79,"props":1309,"children":1311},{"className":1310},[],[1312],{"type":45,"value":1313},"PICS fine-mapped credible set based on reported top hit",{"type":39,"tag":1220,"props":1315,"children":1316},{},[1317,1322],{"type":39,"tag":1242,"props":1318,"children":1319},{},[1320],{"type":45,"value":1321},"None",{"type":39,"tag":1242,"props":1323,"children":1324},{},[1325],{"type":39,"tag":79,"props":1326,"children":1328},{"className":1327},[],[1329],{"type":45,"value":1330},"Unknown confidence",{"type":39,"tag":55,"props":1332,"children":1333},{},[1334,1336,1342],{"type":45,"value":1335},"When users ask about \"N-star confidence\", match their request to the\ncorresponding string in the ",{"type":39,"tag":79,"props":1337,"children":1339},{"className":1338},[],[1340],{"type":45,"value":1341},"confidence",{"type":45,"value":1343}," field of the API response.",{"type":39,"tag":48,"props":1345,"children":1347},{"id":1346},"tips-and-common-mistakes",[1348],{"type":45,"value":1349},"Tips and Common Mistakes",{"type":39,"tag":128,"props":1351,"children":1352},{},[1353,1468,1501],{"type":39,"tag":71,"props":1354,"children":1355},{},[1356,1361,1363],{"type":39,"tag":75,"props":1357,"children":1358},{},[1359],{"type":45,"value":1360},"ID Formats",{"type":45,"value":1362},":\n",{"type":39,"tag":128,"props":1364,"children":1365},{},[1366,1393,1414,1449],{"type":39,"tag":71,"props":1367,"children":1368},{},[1369,1371,1376,1378,1384,1386,1391],{"type":45,"value":1370},"Disease IDs are typically MONDO IDs (e.g. ",{"type":39,"tag":79,"props":1372,"children":1374},{"className":1373},[],[1375],{"type":45,"value":367},{"type":45,"value":1377}," for Rheumatoid\nArthritis) or EFO IDs (e.g. ",{"type":39,"tag":79,"props":1379,"children":1381},{"className":1380},[],[1382],{"type":45,"value":1383},"EFO_0009460",{"type":45,"value":1385},"). Use the ",{"type":39,"tag":79,"props":1387,"children":1389},{"className":1388},[],[1390],{"type":45,"value":609},{"type":45,"value":1392},"\ncommand to find the correct ID.",{"type":39,"tag":71,"props":1394,"children":1395},{},[1396,1398,1404,1406,1412],{"type":45,"value":1397},"Target IDs must be Ensembl IDs (e.g. ",{"type":39,"tag":79,"props":1399,"children":1401},{"className":1400},[],[1402],{"type":45,"value":1403},"ENSG00000169083",{"type":45,"value":1405},"), not HGNC\nsymbols. If you only have a gene symbol, you may need to map it first\nusing a custom GraphQL ",{"type":39,"tag":79,"props":1407,"children":1409},{"className":1408},[],[1410],{"type":45,"value":1411},"search",{"type":45,"value":1413}," query.",{"type":39,"tag":71,"props":1415,"children":1416},{},[1417,1419,1425,1427,1433,1435,1440,1441,1447],{"type":45,"value":1418},"Variant IDs are formatted as ",{"type":39,"tag":79,"props":1420,"children":1422},{"className":1421},[],[1423],{"type":45,"value":1424},"chromosome_position_ref_alt",{"type":45,"value":1426}," (e.g.,\n",{"type":39,"tag":79,"props":1428,"children":1430},{"className":1429},[],[1431],{"type":45,"value":1432},"1_154426264_C_T",{"type":45,"value":1434},"). A ",{"type":39,"tag":79,"props":1436,"children":1438},{"className":1437},[],[1439],{"type":45,"value":478},{"type":45,"value":480},{"type":39,"tag":79,"props":1442,"children":1444},{"className":1443},[],[1445],{"type":45,"value":1446},"chr1_154426264_C_T",{"type":45,"value":1448},") is\nautomatically stripped by the tool.",{"type":39,"tag":71,"props":1450,"children":1451},{},[1452,1454,1460,1462,1467],{"type":45,"value":1453},"Study IDs can be GWAS Catalog IDs (e.g. ",{"type":39,"tag":79,"props":1455,"children":1457},{"className":1456},[],[1458],{"type":45,"value":1459},"GCST90204201",{"type":45,"value":1461},") or\nproject-specific IDs (e.g. ",{"type":39,"tag":79,"props":1463,"children":1465},{"className":1464},[],[1466],{"type":45,"value":399},{"type":45,"value":433},{"type":39,"tag":71,"props":1469,"children":1470},{},[1471,1476,1478,1484,1486,1492,1494,1499],{"type":39,"tag":75,"props":1472,"children":1473},{},[1474],{"type":45,"value":1475},"Truncation",{"type":45,"value":1477},": The tool truncates arrays longer than ",{"type":39,"tag":79,"props":1479,"children":1481},{"className":1480},[],[1482],{"type":45,"value":1483},"--limit",{"type":45,"value":1485}," to protect\nthe context window. If you see ",{"type":39,"tag":79,"props":1487,"children":1489},{"className":1488},[],[1490],{"type":45,"value":1491},"\"_truncated\"",{"type":45,"value":1493},", you can run the query again\nwith a higher limit if you specifically need more data, but be cautious with\nlarge limit values. Always use the ",{"type":39,"tag":79,"props":1495,"children":1497},{"className":1496},[],[1498],{"type":45,"value":156},{"type":45,"value":1500}," flag to save the result to a\nfile and avoid terminal output truncation.",{"type":39,"tag":71,"props":1502,"children":1503},{},[1504,1509,1510,1515,1517,1536,1538,1543,1545,1550,1552,1557,1559,1564],{"type":39,"tag":75,"props":1505,"children":1506},{},[1507],{"type":45,"value":1508},"Pagination and incomplete results",{"type":45,"value":150},{"type":39,"tag":79,"props":1511,"children":1513},{"className":1512},[],[1514],{"type":45,"value":752},{"type":45,"value":1516}," option\n(default: 200) controls how many items are fetched from the API. ",{"type":39,"tag":75,"props":1518,"children":1519},{},[1520,1522,1527,1529,1534],{"type":45,"value":1521},"Always\ncheck the ",{"type":39,"tag":79,"props":1523,"children":1525},{"className":1524},[],[1526],{"type":45,"value":760},{"type":45,"value":1528}," field in the response and compare it to the number of\n",{"type":39,"tag":79,"props":1530,"children":1532},{"className":1531},[],[1533],{"type":45,"value":768},{"type":45,"value":1535}," actually returned.",{"type":45,"value":1537}," If ",{"type":39,"tag":79,"props":1539,"children":1541},{"className":1540},[],[1542],{"type":45,"value":760},{"type":45,"value":1544}," > number of rows, you have incomplete\ndata — either increase ",{"type":39,"tag":79,"props":1546,"children":1548},{"className":1547},[],[1549],{"type":45,"value":752},{"type":45,"value":1551}," to fetch more, or inform the user that\nonly a partial result set was returned. This is especially important for\n",{"type":39,"tag":79,"props":1553,"children":1555},{"className":1554},[],[1556],{"type":45,"value":445},{"type":45,"value":1558}," without ",{"type":39,"tag":79,"props":1560,"children":1562},{"className":1561},[],[1563],{"type":45,"value":470},{"type":45,"value":1565},", which can return hundreds of credible sets.",{"type":39,"tag":1567,"props":1568,"children":1569},"style",{},[1570],{"type":45,"value":1571},"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":1573,"total":1737},[1574,1587,1602,1622,1634,1649,1665,1678,1690,1705,1716,1726],{"slug":1575,"name":1575,"fn":1576,"description":1577,"org":1578,"tags":1579,"stars":22,"repoUrl":23,"updatedAt":1586},"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},[1580,1581,1584,1585],{"name":20,"slug":21,"type":15},{"name":1582,"slug":1583,"type":15},"Genomics","genomics",{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:51.827211",{"slug":1588,"name":1588,"fn":1589,"description":1590,"org":1591,"tags":1592,"stars":22,"repoUrl":23,"updatedAt":1601},"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},[1593,1594,1597,1598],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":1599,"slug":1600,"type":15},"RNA-seq","rna-seq","2026-07-12T07:51:39.494803",{"slug":1603,"name":1603,"fn":1604,"description":1605,"org":1606,"tags":1607,"stars":22,"repoUrl":23,"updatedAt":1621},"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},[1608,1611,1614,1617,1620],{"name":1609,"slug":1610,"type":15},"ChEMBL","chembl",{"name":1612,"slug":1613,"type":15},"Chemistry","chemistry",{"name":1615,"slug":1616,"type":15},"Database","database",{"name":1618,"slug":1619,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":1623,"name":1623,"fn":1624,"description":1625,"org":1626,"tags":1627,"stars":22,"repoUrl":23,"updatedAt":1633},"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},[1628,1631,1632],{"name":1629,"slug":1630,"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":1635,"name":1635,"fn":1636,"description":1637,"org":1638,"tags":1639,"stars":22,"repoUrl":23,"updatedAt":1648},"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},[1640,1643,1644,1647],{"name":1641,"slug":1642,"type":15},"ClinVar","clinvar",{"name":1595,"slug":1596,"type":15},{"name":1645,"slug":1646,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":1650,"name":1650,"fn":1651,"description":1652,"org":1653,"tags":1654,"stars":22,"repoUrl":23,"updatedAt":1664},"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},[1655,1658,1661],{"name":1656,"slug":1657,"type":15},"Compliance","compliance",{"name":1659,"slug":1660,"type":15},"Operations","operations",{"name":1662,"slug":1663,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":1666,"name":1666,"fn":1667,"description":1668,"org":1669,"tags":1670,"stars":22,"repoUrl":23,"updatedAt":1677},"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},[1671,1672,1673,1676],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},{"name":1674,"slug":1675,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",{"slug":1679,"name":1679,"fn":1680,"description":1681,"org":1682,"tags":1683,"stars":22,"repoUrl":23,"updatedAt":1689},"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},[1684,1685,1688],{"name":20,"slug":21,"type":15},{"name":1686,"slug":1687,"type":15},"Ontology","ontology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":1691,"name":1691,"fn":1692,"description":1693,"org":1694,"tags":1695,"stars":22,"repoUrl":23,"updatedAt":1704},"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},[1696,1697,1700,1701],{"name":20,"slug":21,"type":15},{"name":1698,"slug":1699,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":1702,"slug":1703,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":1706,"name":1706,"fn":1707,"description":1708,"org":1709,"tags":1710,"stars":22,"repoUrl":23,"updatedAt":1715},"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},[1711,1712,1713,1714],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":1717,"name":1717,"fn":1718,"description":1719,"org":1720,"tags":1721,"stars":22,"repoUrl":23,"updatedAt":1725},"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},[1722,1723,1724],{"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":1727,"name":1727,"fn":1728,"description":1729,"org":1730,"tags":1731,"stars":22,"repoUrl":23,"updatedAt":1736},"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},[1732,1733,1734,1735],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009",38,{"items":1739,"total":1737},[1740,1747,1754,1762,1768,1775,1781],{"slug":1575,"name":1575,"fn":1576,"description":1577,"org":1741,"tags":1742,"stars":22,"repoUrl":23,"updatedAt":1586},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1743,1744,1745,1746],{"name":20,"slug":21,"type":15},{"name":1582,"slug":1583,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1588,"name":1588,"fn":1589,"description":1590,"org":1748,"tags":1749,"stars":22,"repoUrl":23,"updatedAt":1601},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1750,1751,1752,1753],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},{"name":13,"slug":14,"type":15},{"name":1599,"slug":1600,"type":15},{"slug":1603,"name":1603,"fn":1604,"description":1605,"org":1755,"tags":1756,"stars":22,"repoUrl":23,"updatedAt":1621},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1757,1758,1759,1760,1761],{"name":1609,"slug":1610,"type":15},{"name":1612,"slug":1613,"type":15},{"name":1615,"slug":1616,"type":15},{"name":1618,"slug":1619,"type":15},{"name":13,"slug":14,"type":15},{"slug":1623,"name":1623,"fn":1624,"description":1625,"org":1763,"tags":1764,"stars":22,"repoUrl":23,"updatedAt":1633},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1765,1766,1767],{"name":1629,"slug":1630,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1635,"name":1635,"fn":1636,"description":1637,"org":1769,"tags":1770,"stars":22,"repoUrl":23,"updatedAt":1648},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1771,1772,1773,1774],{"name":1641,"slug":1642,"type":15},{"name":1595,"slug":1596,"type":15},{"name":1645,"slug":1646,"type":15},{"name":13,"slug":14,"type":15},{"slug":1650,"name":1650,"fn":1651,"description":1652,"org":1776,"tags":1777,"stars":22,"repoUrl":23,"updatedAt":1664},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1778,1779,1780],{"name":1656,"slug":1657,"type":15},{"name":1659,"slug":1660,"type":15},{"name":1662,"slug":1663,"type":15},{"slug":1666,"name":1666,"fn":1667,"description":1668,"org":1782,"tags":1783,"stars":22,"repoUrl":23,"updatedAt":1677},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1784,1785,1786,1787],{"name":20,"slug":21,"type":15},{"name":1595,"slug":1596,"type":15},{"name":1674,"slug":1675,"type":15},{"name":13,"slug":14,"type":15}]