Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Query and annotate gene variants from ClinVar and dbSNP databases. Trigger when: - User provides a variant identifier (rsID, HGVS notation, genomic coordinat...
Query and annotate gene variants from ClinVar and dbSNP databases. Trigger when: - User provides a variant identifier (rsID, HGVS notation, genomic coordinat...
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Query and interpret gene variant clinical significance from ClinVar and dbSNP databases with ACMG guideline support.
Provide comprehensive variant annotation including: Clinical significance classification (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign) ACMG guideline-based pathogenicity assessment Population allele frequencies (gnomAD, ExAC, 1000 Genomes) Disease and phenotype associations Functional predictions (SIFT, PolyPhen, CADD)
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
from scripts.main import VariantAnnotator # Initialize annotator annotator = VariantAnnotator() # Query by rsID result = annotator.query_variant("rs80357410") # Query by HGVS notation result = annotator.query_variant("NM_007294.3:c.5096G>A") # Query by genomic coordinate result = annotator.query_variant("chr17:43094692:G>A") # Batch query results = annotator.batch_query(["rs80357410", "rs28897696", "rs11571658"])
# Single variant query python scripts/main.py --variant rs80357410 # HGVS notation python scripts/main.py --variant "NM_007294.3:c.5096G>A" # Genomic coordinate python scripts/main.py --variant "chr17:43094692:G>A" # Batch from file python scripts/main.py --file variants.txt --output results.json # With output format python scripts/main.py --variant rs80357410 --format json
{ "variant_id": "rs80357410", "gene": "BRCA1", "chromosome": "17", "position": 43094692, "ref_allele": "G", "alt_allele": "A", "hgvs_genomic": "NC_000017.11:g.43094692G>A", "hgvs_cdna": "NM_007294.3:c.5096G>A", "hgvs_protein": "NP_009225.1:p.Arg1699Gln", "clinical_significance": { "clinvar": "Pathogenic", "acmg_classification": "Pathogenic", "acmg_criteria": ["PS4", "PM1", "PM2", "PP2", "PP3", "PP5"], "acmg_score": 13.0, "review_status": "criteria provided, multiple submitters, no conflicts" }, "disease_associations": [ { "disease": "Breast-ovarian cancer, familial 1", "medgen_id": "C2676676", "significance": "Pathogenic" } ], "population_frequencies": { "gnomAD_genome_all": 0.000008, "gnomAD_exome_all": 0.000012, "1000G_all": 0.0 }, "functional_predictions": { "sift": "deleterious", "polyphen2": "probably_damaging", "cadd_score": 24.5, "mutation_taster": "disease_causing" }, "literature_count": 42, "last_evaluated": "2023-12-15", "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)." }
The annotator implements the ACMG/AMP guidelines for variant interpretation:
PVS1 (8.0): Null variant in a gene where LOF is known mechanism PS1 (4.0): Same amino acid change as known pathogenic PS2 (4.0): De novo with confirmed paternity/maternity PS3 (4.0): Well-established functional studies show damaging effect PS4 (4.0): Prevalence in affected > controls PM1 (2.0): Located in critical functional domain PM2 (2.0): Absent from controls (MAF <0.0001) PM3 (2.0): AR disorder, detected in trans with pathogenic PM4 (2.0): Protein length changing PM5 (2.0): Novel missense at same position as known pathogenic PM6 (2.0): Assumed de novo without confirmation PP1 (1.0): Cosegregation with disease PP2 (1.0): Missense in gene with low benign rate PP3 (1.0): Multiple computational evidence support PP4 (1.0): Phenotype/patient history matches gene PP5 (1.0): Reputable source reports pathogenic
BA1 (-8.0): MAF >5% in population BS1 (-4.0): MAF >expected for disorder BS2 (-4.0): Observed in healthy adult BS3 (-4.0): Functional studies show no damage BS4 (-4.0): Lack of cosegregation BP1 (-1.0): Missense in gene where truncating are pathogenic BP2 (-1.0): Observed in trans with pathogenic BP3 (-1.0): In-frame indel in repetitive region BP4 (-1.0): Multiple computational evidence benign BP5 (-1.0): Alternate cause found BP6 (-1.0): Reputable source reports benign BP7 (-1.0): Synonymous with no splicing impact
ClassificationScore RangePathogenic≥ 10Likely Pathogenic6-9Uncertain Significance0-5Likely Benign-5 to -1Benign≤ -6
⚠️ AI自主验收状态: 需人工检查 This skill requires: NCBI E-utilities API integration (ClinVar, dbSNP) HGVS notation parsing and validation VCF format handling ACMG guideline implementation Multiple prediction algorithm integration Complex data transformation and scoring
DatabaseData TypeAPI/AccessClinVarClinical significance, disease associationsNCBI E-utilitiesdbSNPSNP data, allele frequenciesNCBI E-utilitiesgnomADPopulation frequenciesgnomAD APIEnsembl VEPFunctional predictionsREST APICADDDeleteriousness scoresREST API
Requires internet connection for database queries NCBI API rate limits: 3 requests/second (API key increases to 10/sec) Some variants may not be present in ClinVar (VUS without clinical data) HGVS notation parsing may fail for complex variants Population frequencies not available for all variants Functional predictions are computational estimates only
See references/ for: ACMG guidelines publication (Richards et al. 2015) ClinVar documentation HGVS nomenclature guide dbSNP data dictionary Example variant outputs
⚠️ 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.
Risk IndicatorAssessmentLevelCode ExecutionPython scripts with toolsHighNetwork AccessExternal API callsHighFile System AccessRead/write dataMediumInstruction TamperingStandard prompt guidelinesLowData ExposureData handled securelyMedium
No hardcoded credentials or API keys No unauthorized file system access (../) Output does not expose sensitive information Prompt injection protections in place API requests use HTTPS only Input validated against allowed patterns API timeout and retry mechanisms implemented Output directory restricted to workspace Script execution in sandboxed environment Error messages sanitized (no internal paths exposed) Dependencies audited No exposure of internal service architecture
# Python dependencies pip install -r requirements.txt
Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable
Basic Functionality: Standard input → Expected output Edge Case: Invalid input → Graceful error handling Performance: Large dataset → Acceptable processing time
Current Stage: Draft Next Review Date: 2026-03-06 Known Issues: None Planned Improvements: Performance optimization Additional feature support
ParameterTypeDefaultDescription--variantstrRequired--filestrRequired--outputstrRequired--formatstr"json"--api-keystrRequiredNCBI API key for increased rate limits--delayfloat0.34
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