{ "schemaVersion": "1.0", "item": { "slug": "variant-annotation", "name": "Variant Annotation", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/AIPOCH-AI/variant-annotation", "canonicalUrl": "https://clawhub.ai/AIPOCH-AI/variant-annotation", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/variant-annotation", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=variant-annotation", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "requirements.txt", "SKILL.md", "scripts/main.py", "references/acmg-guidelines.md", "references/clinvar-guide.md", "references/example-variants.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-07T17:22:31.273Z", "expiresAt": "2026-05-14T17:22:31.273Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/variant-annotation" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/variant-annotation", "agentPageUrl": "https://openagent3.xyz/skills/variant-annotation/agent", "manifestUrl": "https://openagent3.xyz/skills/variant-annotation/agent.json", "briefUrl": "https://openagent3.xyz/skills/variant-annotation/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Variant Annotation", "body": "Query and interpret gene variant clinical significance from ClinVar and dbSNP databases with ACMG guideline support." }, { "title": "Purpose", "body": "Provide comprehensive variant annotation including:\n\nClinical significance classification (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign)\nACMG guideline-based pathogenicity assessment\nPopulation allele frequencies (gnomAD, ExAC, 1000 Genomes)\nDisease and phenotype associations\nFunctional predictions (SIFT, PolyPhen, CADD)" }, { "title": "Supported Input Formats", "body": "FormatExampleDescriptionrsIDrs80357410dbSNP reference SNP IDHGVS cDNANM_007294.3:c.5096G>ACoding DNA changeHGVS ProteinNP_009225.1:p.Arg1699GlnProtein changeHGVS GenomicNC_000017.11:g.43094692G>AGenomic coordinateVCF-stylechr17:43094692:G>AChromosome:position:ref>altGene:AABRCA1:R1699QGene with amino acid change" }, { "title": "Python API", "body": "from scripts.main import VariantAnnotator\n\n# Initialize annotator\nannotator = VariantAnnotator()\n\n# Query by rsID\nresult = annotator.query_variant(\"rs80357410\")\n\n# Query by HGVS notation\nresult = annotator.query_variant(\"NM_007294.3:c.5096G>A\")\n\n# Query by genomic coordinate\nresult = annotator.query_variant(\"chr17:43094692:G>A\")\n\n# Batch query\nresults = annotator.batch_query([\"rs80357410\", \"rs28897696\", \"rs11571658\"])" }, { "title": "Command Line", "body": "# Single variant query\npython scripts/main.py --variant rs80357410\n\n# HGVS notation\npython scripts/main.py --variant \"NM_007294.3:c.5096G>A\"\n\n# Genomic coordinate\npython scripts/main.py --variant \"chr17:43094692:G>A\"\n\n# Batch from file\npython scripts/main.py --file variants.txt --output results.json\n\n# With output format\npython scripts/main.py --variant rs80357410 --format json" }, { "title": "Output Format", "body": "{\n \"variant_id\": \"rs80357410\",\n \"gene\": \"BRCA1\",\n \"chromosome\": \"17\",\n \"position\": 43094692,\n \"ref_allele\": \"G\",\n \"alt_allele\": \"A\",\n \"hgvs_genomic\": \"NC_000017.11:g.43094692G>A\",\n \"hgvs_cdna\": \"NM_007294.3:c.5096G>A\",\n \"hgvs_protein\": \"NP_009225.1:p.Arg1699Gln\",\n \n \"clinical_significance\": {\n \"clinvar\": \"Pathogenic\",\n \"acmg_classification\": \"Pathogenic\",\n \"acmg_criteria\": [\"PS4\", \"PM1\", \"PM2\", \"PP2\", \"PP3\", \"PP5\"],\n \"acmg_score\": 13.0,\n \"review_status\": \"criteria provided, multiple submitters, no conflicts\"\n },\n \n \"disease_associations\": [\n {\n \"disease\": \"Breast-ovarian cancer, familial 1\",\n \"medgen_id\": \"C2676676\",\n \"significance\": \"Pathogenic\"\n }\n ],\n \n \"population_frequencies\": {\n \"gnomAD_genome_all\": 0.000008,\n \"gnomAD_exome_all\": 0.000012,\n \"1000G_all\": 0.0\n },\n \n \"functional_predictions\": {\n \"sift\": \"deleterious\",\n \"polyphen2\": \"probably_damaging\",\n \"cadd_score\": 24.5,\n \"mutation_taster\": \"disease_causing\"\n },\n \n \"literature_count\": 42,\n \"last_evaluated\": \"2023-12-15\",\n \n \"interpretation_summary\": \"This variant (BRCA1 p.Arg1699Gln) is classified as Pathogenic based on ACMG guidelines. It shows strong evidence of pathogenicity including population data (extremely rare), computational predictions (deleterious), and strong clinical significance (established association with hereditary breast-ovarian cancer).\"\n}" }, { "title": "ACMG Classification Criteria", "body": "The annotator implements the ACMG/AMP guidelines for variant interpretation:" }, { "title": "Pathogenic Evidence (Score)", "body": "PVS1 (8.0): Null variant in a gene where LOF is known mechanism\nPS1 (4.0): Same amino acid change as known pathogenic\nPS2 (4.0): De novo with confirmed paternity/maternity\nPS3 (4.0): Well-established functional studies show damaging effect\nPS4 (4.0): Prevalence in affected > controls\nPM1 (2.0): Located in critical functional domain\nPM2 (2.0): Absent from controls (MAF <0.0001)\nPM3 (2.0): AR disorder, detected in trans with pathogenic\nPM4 (2.0): Protein length changing\nPM5 (2.0): Novel missense at same position as known pathogenic\nPM6 (2.0): Assumed de novo without confirmation\nPP1 (1.0): Cosegregation with disease\nPP2 (1.0): Missense in gene with low benign rate\nPP3 (1.0): Multiple computational evidence support\nPP4 (1.0): Phenotype/patient history matches gene\nPP5 (1.0): Reputable source reports pathogenic" }, { "title": "Benign Evidence", "body": "BA1 (-8.0): MAF >5% in population\nBS1 (-4.0): MAF >expected for disorder\nBS2 (-4.0): Observed in healthy adult\nBS3 (-4.0): Functional studies show no damage\nBS4 (-4.0): Lack of cosegregation\nBP1 (-1.0): Missense in gene where truncating are pathogenic\nBP2 (-1.0): Observed in trans with pathogenic\nBP3 (-1.0): In-frame indel in repetitive region\nBP4 (-1.0): Multiple computational evidence benign\nBP5 (-1.0): Alternate cause found\nBP6 (-1.0): Reputable source reports benign\nBP7 (-1.0): Synonymous with no splicing impact" }, { "title": "Classification Thresholds", "body": "ClassificationScore RangePathogenic≥ 10Likely Pathogenic6-9Uncertain Significance0-5Likely Benign-5 to -1Benign≤ -6" }, { "title": "Technical Difficulty: HIGH", "body": "⚠️ AI自主验收状态: 需人工检查\n\nThis skill requires:\n\nNCBI E-utilities API integration (ClinVar, dbSNP)\nHGVS notation parsing and validation\nVCF format handling\nACMG guideline implementation\nMultiple prediction algorithm integration\nComplex data transformation and scoring" }, { "title": "Data Sources", "body": "DatabaseData TypeAPI/AccessClinVarClinical significance, disease associationsNCBI E-utilitiesdbSNPSNP data, allele frequenciesNCBI E-utilitiesgnomADPopulation frequenciesgnomAD APIEnsembl VEPFunctional predictionsREST APICADDDeleteriousness scoresREST API" }, { "title": "Limitations", "body": "Requires internet connection for database queries\nNCBI API rate limits: 3 requests/second (API key increases to 10/sec)\nSome variants may not be present in ClinVar (VUS without clinical data)\nHGVS notation parsing may fail for complex variants\nPopulation frequencies not available for all variants\nFunctional predictions are computational estimates only" }, { "title": "References", "body": "See references/ for:\n\nACMG guidelines publication (Richards et al. 2015)\nClinVar documentation\nHGVS nomenclature guide\ndbSNP data dictionary\nExample variant outputs" }, { "title": "Safety & Disclaimer", "body": "⚠️ IMPORTANT: This tool is for research and educational purposes only. Variant interpretations are computational predictions and should not be used as the sole basis for clinical decisions. Always consult certified genetic counselors and clinical laboratories for diagnostic purposes. ACMG classifications in this tool are algorithmic estimates and may differ from expert panel reviews." }, { "title": "Risk Assessment", "body": "Risk IndicatorAssessmentLevelCode ExecutionPython scripts with toolsHighNetwork AccessExternal API callsHighFile System AccessRead/write dataMediumInstruction TamperingStandard prompt guidelinesLowData ExposureData handled securelyMedium" }, { "title": "Security Checklist", "body": "No hardcoded credentials or API keys\n No unauthorized file system access (../)\n Output does not expose sensitive information\n Prompt injection protections in place\n API requests use HTTPS only\n Input validated against allowed patterns\n API timeout and retry mechanisms implemented\n Output directory restricted to workspace\n Script execution in sandboxed environment\n Error messages sanitized (no internal paths exposed)\n Dependencies audited\n No exposure of internal service architecture" }, { "title": "Prerequisites", "body": "# Python dependencies\npip install -r requirements.txt" }, { "title": "Success Metrics", "body": "Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable" }, { "title": "Test Cases", "body": "Basic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time" }, { "title": "Lifecycle Status", "body": "Current Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\n\nPerformance optimization\nAdditional feature support" }, { "title": "Parameters", "body": "ParameterTypeDefaultDescription--variantstrRequired--filestrRequired--outputstrRequired--formatstr\"json\"--api-keystrRequiredNCBI API key for increased rate limits--delayfloat0.34" } ], "body": "Variant Annotation\n\nQuery and interpret gene variant clinical significance from ClinVar and dbSNP databases with ACMG guideline support.\n\nPurpose\n\nProvide comprehensive variant annotation including:\n\nClinical significance classification (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign)\nACMG guideline-based pathogenicity assessment\nPopulation allele frequencies (gnomAD, ExAC, 1000 Genomes)\nDisease and phenotype associations\nFunctional predictions (SIFT, PolyPhen, CADD)\nSupported Input Formats\nFormat\tExample\tDescription\nrsID\trs80357410\tdbSNP reference SNP ID\nHGVS cDNA\tNM_007294.3:c.5096G>A\tCoding DNA change\nHGVS Protein\tNP_009225.1:p.Arg1699Gln\tProtein change\nHGVS Genomic\tNC_000017.11:g.43094692G>A\tGenomic coordinate\nVCF-style\tchr17:43094692:G>A\tChromosome:position:ref>alt\nGene:AA\tBRCA1:R1699Q\tGene with amino acid change\nUsage\nPython API\nfrom scripts.main import VariantAnnotator\n\n# Initialize annotator\nannotator = VariantAnnotator()\n\n# Query by rsID\nresult = annotator.query_variant(\"rs80357410\")\n\n# Query by HGVS notation\nresult = annotator.query_variant(\"NM_007294.3:c.5096G>A\")\n\n# Query by genomic coordinate\nresult = annotator.query_variant(\"chr17:43094692:G>A\")\n\n# Batch query\nresults = annotator.batch_query([\"rs80357410\", \"rs28897696\", \"rs11571658\"])\n\nCommand Line\n# Single variant query\npython scripts/main.py --variant rs80357410\n\n# HGVS notation\npython scripts/main.py --variant \"NM_007294.3:c.5096G>A\"\n\n# Genomic coordinate\npython scripts/main.py --variant \"chr17:43094692:G>A\"\n\n# Batch from file\npython scripts/main.py --file variants.txt --output results.json\n\n# With output format\npython scripts/main.py --variant rs80357410 --format json\n\nOutput Format\n{\n \"variant_id\": \"rs80357410\",\n \"gene\": \"BRCA1\",\n \"chromosome\": \"17\",\n \"position\": 43094692,\n \"ref_allele\": \"G\",\n \"alt_allele\": \"A\",\n \"hgvs_genomic\": \"NC_000017.11:g.43094692G>A\",\n \"hgvs_cdna\": \"NM_007294.3:c.5096G>A\",\n \"hgvs_protein\": \"NP_009225.1:p.Arg1699Gln\",\n \n \"clinical_significance\": {\n \"clinvar\": \"Pathogenic\",\n \"acmg_classification\": \"Pathogenic\",\n \"acmg_criteria\": [\"PS4\", \"PM1\", \"PM2\", \"PP2\", \"PP3\", \"PP5\"],\n \"acmg_score\": 13.0,\n \"review_status\": \"criteria provided, multiple submitters, no conflicts\"\n },\n \n \"disease_associations\": [\n {\n \"disease\": \"Breast-ovarian cancer, familial 1\",\n \"medgen_id\": \"C2676676\",\n \"significance\": \"Pathogenic\"\n }\n ],\n \n \"population_frequencies\": {\n \"gnomAD_genome_all\": 0.000008,\n \"gnomAD_exome_all\": 0.000012,\n \"1000G_all\": 0.0\n },\n \n \"functional_predictions\": {\n \"sift\": \"deleterious\",\n \"polyphen2\": \"probably_damaging\",\n \"cadd_score\": 24.5,\n \"mutation_taster\": \"disease_causing\"\n },\n \n \"literature_count\": 42,\n \"last_evaluated\": \"2023-12-15\",\n \n \"interpretation_summary\": \"This variant (BRCA1 p.Arg1699Gln) is classified as Pathogenic based on ACMG guidelines. It shows strong evidence of pathogenicity including population data (extremely rare), computational predictions (deleterious), and strong clinical significance (established association with hereditary breast-ovarian cancer).\"\n}\n\nACMG Classification Criteria\n\nThe annotator implements the ACMG/AMP guidelines for variant interpretation:\n\nPathogenic Evidence (Score)\nPVS1 (8.0): Null variant in a gene where LOF is known mechanism\nPS1 (4.0): Same amino acid change as known pathogenic\nPS2 (4.0): De novo with confirmed paternity/maternity\nPS3 (4.0): Well-established functional studies show damaging effect\nPS4 (4.0): Prevalence in affected > controls\nPM1 (2.0): Located in critical functional domain\nPM2 (2.0): Absent from controls (MAF <0.0001)\nPM3 (2.0): AR disorder, detected in trans with pathogenic\nPM4 (2.0): Protein length changing\nPM5 (2.0): Novel missense at same position as known pathogenic\nPM6 (2.0): Assumed de novo without confirmation\nPP1 (1.0): Cosegregation with disease\nPP2 (1.0): Missense in gene with low benign rate\nPP3 (1.0): Multiple computational evidence support\nPP4 (1.0): Phenotype/patient history matches gene\nPP5 (1.0): Reputable source reports pathogenic\nBenign Evidence\nBA1 (-8.0): MAF >5% in population\nBS1 (-4.0): MAF >expected for disorder\nBS2 (-4.0): Observed in healthy adult\nBS3 (-4.0): Functional studies show no damage\nBS4 (-4.0): Lack of cosegregation\nBP1 (-1.0): Missense in gene where truncating are pathogenic\nBP2 (-1.0): Observed in trans with pathogenic\nBP3 (-1.0): In-frame indel in repetitive region\nBP4 (-1.0): Multiple computational evidence benign\nBP5 (-1.0): Alternate cause found\nBP6 (-1.0): Reputable source reports benign\nBP7 (-1.0): Synonymous with no splicing impact\nClassification Thresholds\nClassification\tScore Range\nPathogenic\t≥ 10\nLikely Pathogenic\t6-9\nUncertain Significance\t0-5\nLikely Benign\t-5 to -1\nBenign\t≤ -6\nTechnical Difficulty: HIGH\n\n⚠️ AI自主验收状态: 需人工检查\n\nThis skill requires:\n\nNCBI E-utilities API integration (ClinVar, dbSNP)\nHGVS notation parsing and validation\nVCF format handling\nACMG guideline implementation\nMultiple prediction algorithm integration\nComplex data transformation and scoring\nData Sources\nDatabase\tData Type\tAPI/Access\nClinVar\tClinical significance, disease associations\tNCBI E-utilities\ndbSNP\tSNP data, allele frequencies\tNCBI E-utilities\ngnomAD\tPopulation frequencies\tgnomAD API\nEnsembl VEP\tFunctional predictions\tREST API\nCADD\tDeleteriousness scores\tREST API\nLimitations\nRequires internet connection for database queries\nNCBI API rate limits: 3 requests/second (API key increases to 10/sec)\nSome variants may not be present in ClinVar (VUS without clinical data)\nHGVS notation parsing may fail for complex variants\nPopulation frequencies not available for all variants\nFunctional predictions are computational estimates only\nReferences\n\nSee references/ for:\n\nACMG guidelines publication (Richards et al. 2015)\nClinVar documentation\nHGVS nomenclature guide\ndbSNP data dictionary\nExample variant outputs\nSafety & Disclaimer\n\n⚠️ IMPORTANT: This tool is for research and educational purposes only. Variant interpretations are computational predictions and should not be used as the sole basis for clinical decisions. Always consult certified genetic counselors and clinical laboratories for diagnostic purposes. ACMG classifications in this tool are algorithmic estimates and may differ from expert panel reviews.\n\nRisk Assessment\nRisk Indicator\tAssessment\tLevel\nCode Execution\tPython scripts with tools\tHigh\nNetwork Access\tExternal API calls\tHigh\nFile System Access\tRead/write data\tMedium\nInstruction Tampering\tStandard prompt guidelines\tLow\nData Exposure\tData handled securely\tMedium\nSecurity Checklist\n No hardcoded credentials or API keys\n No unauthorized file system access (../)\n Output does not expose sensitive information\n Prompt injection protections in place\n API requests use HTTPS only\n Input validated against allowed patterns\n API timeout and retry mechanisms implemented\n Output directory restricted to workspace\n Script execution in sandboxed environment\n Error messages sanitized (no internal paths exposed)\n Dependencies audited\n No exposure of internal service architecture\nPrerequisites\n# Python dependencies\npip install -r requirements.txt\n\nEvaluation Criteria\nSuccess Metrics\n Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable\nTest Cases\nBasic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time\nLifecycle Status\nCurrent Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\nPerformance optimization\nAdditional feature support\nParameters\nParameter\tType\tDefault\tDescription\n--variant\tstr\tRequired\t\n--file\tstr\tRequired\t\n--output\tstr\tRequired\t\n--format\tstr\t\"json\"\t\n--api-key\tstr\tRequired\tNCBI API key for increased rate limits\n--delay\tfloat\t0.34" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/AIPOCH-AI/variant-annotation", "publisherUrl": "https://clawhub.ai/AIPOCH-AI/variant-annotation", "owner": "AIPOCH-AI", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/variant-annotation", "downloadUrl": "https://openagent3.xyz/downloads/variant-annotation", "agentUrl": "https://openagent3.xyz/skills/variant-annotation/agent", "manifestUrl": "https://openagent3.xyz/skills/variant-annotation/agent.json", "briefUrl": "https://openagent3.xyz/skills/variant-annotation/agent.md" } }