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Uses FAVOR to convert rsID→variant_id first, then queries gnomAD.","InternScience\u002FDrClaw","InternScience","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F154449623?v=4","01M6DHY1ZF02QASNAVMNPT7NP6","Intern Science","01M6DHY2523FY56Q5572NKAAD0",172,"MIT","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Fagent_hub\u002Ftemplates\u002Fgenomics\u002Fskills\u002Fvariant-population-frequency\u002FSKILL.md",[],[19,20,21,22,23,24,25,26],"variant population frequency","first","convert","queries","query","across","populations","id","2026-11-10 10:12:30",false,"---\nname: variant-population-frequency\ndescription: \"Query gnomAD for variant allele frequency across populations. Uses FAVOR to convert rsID→variant_id first, then queries gnomAD. \"\nlicense: MIT license\nmetadata:\n    skill-author: PJLab\n---\n\n# gnomAD Population Frequency\n\n## Usage\n\n### Tool Description\n\n```tex\nStep 1: Query FAVOR to convert rsID → chr-pos-ref-alt format.\nAPI: GET https:\u002F\u002Fapi.genohub.org\u002Fv1\u002Frsids\u002F{rs_id}\nStep 2: Query gnomAD GraphQL API with the variant_id.\nAPI: POST https:\u002F\u002Fgnomad.broadinstitute.org\u002Fapi (GraphQL)\nArgs:\n    rs_id (str): dbSNP rsID (e.g. \"rs7412\")\nReturn:\n    Overall AF, population-specific AF (exome + genome), homozygote counts.\n```\n\n### Query Example\n\n```python\nimport requests\n\nrs_id = \"rs7412\"\n\n# ── Step 1: FAVOR 获取 variant_id (chr-pos-ref-alt) ──\n# 注意：FAVOR 可能返回多个变异（多等位基因位点），需遍历所有结果\nfavor_url = f\"https:\u002F\u002Fapi.genohub.org\u002Fv1\u002Frsids\u002F{rs_id}\"\nfavor_resp = requests.get(favor_url, timeout=30).json()\nif not isinstance(favor_resp, list):\n    favor_resp = [favor_resp]\n\nvariant_ids = [item.get(\"variant_vcf\", \"\") for item in favor_resp if item.get(\"variant_vcf\")]\nprint(f\"[FAVOR] 该rsID对应 {len(variant_ids)} 个变异: {variant_ids}\")\n\n# ── Step 2: gnomAD 查询人群频率（遍历所有变异） ──\nquery = \"\"\"\nquery VariantQuery($variantId: String!) {\n  variant(variantId: $variantId, dataset: gnomad_r4) {\n    variant_id\n    rsid\n    exome {\n      ac\n      an\n      af\n      ac_hom\n      populations { id ac an ac_hom }\n    }\n    genome {\n      ac\n      an\n      af\n      ac_hom\n      populations { id ac an ac_hom }\n    }\n  }\n}\n\"\"\"\n\nfor variant_vcf in variant_ids:\n    print(f\"\\n── 查询 {variant_vcf} ──\")\n    resp = requests.post(\n        \"https:\u002F\u002Fgnomad.broadinstitute.org\u002Fapi\",\n        json={\"query\": query, \"variables\": {\"variantId\": variant_vcf}},\n        timeout=30\n    ).json()\n\n    v = resp.get(\"data\", {}).get(\"variant\", {})\n    if not v:\n        print(f\"[gnomAD] {variant_vcf}: 未找到数据\")\n        continue\n    print(f\"[gnomAD] variant: {v.get('variant_id')}, rsid: {v.get('rsid')}\")\n\n    for source in [\"exome\", \"genome\"]:\n        d = v.get(source, {})\n        if d:\n            print(f\"[gnomAD] {source}: AF={d.get('af')}, AC={d.get('ac')}, AN={d.get('an')}, Hom={d.get('ac_hom')}\")\n            for pop in (d.get(\"populations\") or []):\n                if pop.get(\"an\", 0) > 0:\n                    af = pop[\"ac\"] \u002F pop[\"an\"]\n                    print(f\"  {pop['id']}: AF={af:.6f}, AC={pop['ac']}, AN={pop['an']}\")\n```\n","# gnomAD Population Frequency\n\n## Usage\n\n### Tool Description\n\n```tex\nStep 1: Query FAVOR to convert rsID → chr-pos-ref-alt format.\nAPI: GET https:\u002F\u002Fapi.genohub.org\u002Fv1\u002Frsids\u002F{rs_id}\nStep 2: Query gnomAD GraphQL API with the variant_id.\nAPI: POST https:\u002F\u002Fgnomad.broadinstitute.org\u002Fapi (GraphQL)\nArgs:\n    rs_id (str): dbSNP rsID (e.g. \"rs7412\")\nReturn:\n    Overall AF, population-specific AF (exome + genome), homozygote counts.\n```\n\n### Query Example\n\n```python\nimport requests\n\nrs_id = \"rs7412\"\n\n# ── Step 1: FAVOR 获取 variant_id (chr-pos-ref-alt) ──\n# 注意：FAVOR 可能返回多个变异（多等位基因位点），需遍历所有结果\nfavor_url = f\"https:\u002F\u002Fapi.genohub.org\u002Fv1\u002Frsids\u002F{rs_id}\"\nfavor_resp = requests.get(favor_url, timeout=30).json()\nif not isinstance(favor_resp, list):\n    favor_resp = [favor_resp]\n\nvariant_ids = [item.get(\"variant_vcf\", \"\") for item in favor_resp if item.get(\"variant_vcf\")]\nprint(f\"[FAVOR] 该rsID对应 {len(variant_ids)} 个变异: {variant_ids}\")\n\n# ── Step 2: gnomAD 查询人群频率（遍历所有变异） ──\nquery = \"\"\"\nquery VariantQuery($variantId: String!) {\n  variant(variantId: $variantId, dataset: gnomad_r4) {\n    variant_id\n    rsid\n    exome {\n      ac\n      an\n      af\n      ac_hom\n      populations { id ac an ac_hom }\n    }\n    genome {\n      ac\n      an\n      af\n      ac_hom\n      populations { id ac an ac_hom }\n    }\n  }\n}\n\"\"\"\n\nfor variant_vcf in variant_ids:\n    print(f\"\\n── 查询 {variant_vcf} ──\")\n    resp = requests.post(\n        \"https:\u002F\u002Fgnomad.broadinstitute.org\u002Fapi\",\n        json={\"query\": query, \"variables\": {\"variantId\": variant_vcf}},\n        timeout=30\n    ).json()\n\n    v = resp.get(\"data\", {}).get(\"variant\", {})\n    if not v:\n        print(f\"[gnomAD] {variant_vcf}: 未找到数据\")\n        continue\n    print(f\"[gnomAD] variant: {v.get('variant_id')}, rsid: {v.get('rsid')}\")\n\n    for source in [\"exome\", \"genome\"]:\n        d = v.get(source, {})\n        if d:\n            print(f\"[gnomAD] {source}: AF={d.get('af')}, AC={d.get('ac')}, AN={d.get('an')}, Hom={d.get('ac_hom')}\")\n            for pop in (d.get(\"populations\") or []):\n                if pop.get(\"an\", 0) > 0:\n                    af = pop[\"ac\"] \u002F pop[\"an\"]\n                    print(f\"  {pop['id']}: AF={af:.6f}, AC={pop['ac']}, AN={pop['an']}\")\n```\n",{"name":5,"license":32,"metadata":33,"description":35},"MIT license",{"skill-author":34},"PJLab","Query gnomAD for variant allele frequency across populations. Uses FAVOR to convert rsID→variant_id first, then queries gnomAD. 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Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-critical-thinking\u002FSKILL.md",[91,96],{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121],"critical","scientific critical thinking","thinking","design","writing","review","risk","analysis","peer review","Bash","quality","read","write","edit","use","evidence","peer","frameworks","teaching","bias",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":27},"01M6DMQ77Z121463E049G2DG8K","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fthinking\u002Fscientific-brainstorming\u002FSKILL.md",[],[129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,116],"brainstorming","scientific brainstorming","research ideation","stage","connections","generation","exploration","do","planning","creative","ideation","research","sessions","data","hypothesis","open",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":157,"publishedAt":27},"01M6DMPV4AMCCTWARGPSFHDWJB","scientific-literature-search","Search scientific literature and research papers using FlowSearch to find relevant academic articles and publications.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fscientific-literature-search\u002FSKILL.md",[151,154],{"categoryNo":152,"categoryName":153,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":155,"categoryName":156,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000023","Skill与智能体开发",[158,159,140,160,161,162],"search","scientific literature search","find","Academic","搜索",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":175,"publishedAt":27},"01M6DMPQJP8HVFV54V05D6K9HA","pubmed-article-search","Search PubMed database for scientific articles and publications to retrieve biomedical literature.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fpubmed-article-search\u002FSKILL.md",[169,172],{"categoryNo":170,"categoryName":171,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":173,"categoryName":174,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000036","内容创作者",[176,177,158,162,178,179,180],"pubmed article search","article","database","pubmed database","biomedical",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":188,"publishedAt":27},"01M6DMPK4H3JQ185M97BRB9KQJ","biomedical-web-search","Search biomedical literature and web content using Tavily search engine for research and clinical information.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fsearch\u002Fbiomedical-web-search\u002FSKILL.md",[187],{"categoryNo":152,"categoryName":153,"taxonomyType":94,"taxonomyTypeDesc":95},[189,180,190,158,191,192,140,193,194,162,195],"biomedical web search","web search","web","tavily","content","engine","clinical",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":203,"publishedAt":27},"01M6DMNNJH9V1S5PQHZRX0N934","uniprot-protein-retrieval","Retrieve protein sequences and functional information from UniProt database by protein name, enabling protein analysis and bioinformatics workflows.","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fprotein\u002Funiprot-protein-retrieval\u002FSKILL.md",[202],{"categoryNo":155,"categoryName":156,"taxonomyType":94,"taxonomyTypeDesc":95},[204,178,109,205,206,207,208],"uniprot protein retrieval","uniprot database","bioinformatics","name","workflows"]