[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ2QNNFZP4JX21RECQ43AM":3,"skill-same:01M6CZ2QNNFZP4JX21RECQ43AM":89},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":21,"linkOnly":22,"markdownContent":23,"markdownBody":24,"frontMatter":25,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":32,"install":33,"resources":46},"01M6CZ2QNNFZP4JX21RECQ43AM","genome-variant-annotation",null,"Annotate variants with functional effects using SnpEff or Ensembl VEP, adding gene impact, population frequencies, and clinical significance.","Scientific-Tooling\u002Fstructured-intelligence","Scientific-Tooling","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F267453265?v=4","01M6CZ1SE6GGD4F67EAQ0PVX38","Scientific Tooling","01M6CZ1SJKCFVGH10Z3A152K02",4,"MIT","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002FSKILL.md",[],[19,20],"genome variant annotation","annotate","2026-11-10 10:12:30",false,"---\nname: genome-variant-annotation\ndescription: Annotate variants with functional effects using SnpEff or Ensembl VEP, adding gene impact, population frequencies, and clinical significance.\n---\n\n# Skill: genome-variant-annotation\n\n## Use When\n\n- User wants to annotate variants with gene and transcript impact predictions.\n- User needs to predict functional consequences (missense, nonsense, splice-site, etc.).\n- User wants to add population allele frequencies (gnomAD, 1000 Genomes).\n- User wants to add clinical annotations (ClinVar significance).\n- User needs to compare annotation engines (SnpEff vs VEP).\n\n## Inputs\n\n- Required:\n  - Filtered VCF file (`.vcf.gz`).\n- Optional:\n  - Annotator choice: `snpeff` or `vep` (default: `snpeff`).\n  - Genome build (default: `GRCh38`).\n  - Cache directory for annotation databases.\n  - Additional VEP plugins or fields (e.g., CADD, LOFTEE).\n  - Output format (default: VCF).\n\n## Workflow\n\n1. If SnpEff: run `snpEff ann` with the appropriate database (e.g., `GRCh38.105`), producing an annotated VCF and summary stats HTML.\n2. If VEP: run `vep` with `--cache`, `--merged` or `--refseq`, and add plugins (CADD, gnomAD, ClinVar, LOFTEE).\n3. Add fields to each variant: gene symbol, consequence, impact tier (HIGH \u002F MODERATE \u002F LOW \u002F MODIFIER), HGVS notation.\n4. Optionally add population allele frequencies from gnomAD.\n5. Generate summary statistics: variants by impact category, genes with HIGH-impact variants.\n\n## Output Contract\n\n- Annotated VCF file (`.vcf.gz`).\n- Annotation summary: HTML report (SnpEff) or stats file (VEP).\n- Variant impact distribution (HIGH, MODERATE, LOW, MODIFIER counts).\n\n## Limits\n\n- SnpEff and VEP annotation databases must be pre-downloaded to the cache directory.\n- VEP plugins (CADD, LOFTEE, gnomAD) require separate data file downloads.\n- Annotation databases must match the reference genome build (GRCh37 vs GRCh38).\n- VEP is generally slower but more configurable than SnpEff.\n- Large VCFs (millions of variants) may require significant memory and runtime.\n- Common failure cases:\n  - SnpEff database name not matching the reference build (e.g., using `hg19` when VCF is on `GRCh38`).\n  - VEP cache directory missing or not matching the installed VEP version.\n  - VEP plugin data files (CADD, LOFTEE) not downloaded or path misconfigured.\n","# Skill: genome-variant-annotation\n\n## Use When\n\n- User wants to annotate variants with gene and transcript impact predictions.\n- User needs to predict functional consequences (missense, nonsense, splice-site, etc.).\n- User wants to add population allele frequencies (gnomAD, 1000 Genomes).\n- User wants to add clinical annotations (ClinVar significance).\n- User needs to compare annotation engines (SnpEff vs VEP).\n\n## Inputs\n\n- Required:\n  - Filtered VCF file (`.vcf.gz`).\n- Optional:\n  - Annotator choice: `snpeff` or `vep` (default: `snpeff`).\n  - Genome build (default: `GRCh38`).\n  - Cache directory for annotation databases.\n  - Additional VEP plugins or fields (e.g., CADD, LOFTEE).\n  - Output format (default: VCF).\n\n## Workflow\n\n1. If SnpEff: run `snpEff ann` with the appropriate database (e.g., `GRCh38.105`), producing an annotated VCF and summary stats HTML.\n2. If VEP: run `vep` with `--cache`, `--merged` or `--refseq`, and add plugins (CADD, gnomAD, ClinVar, LOFTEE).\n3. Add fields to each variant: gene symbol, consequence, impact tier (HIGH \u002F MODERATE \u002F LOW \u002F MODIFIER), HGVS notation.\n4. Optionally add population allele frequencies from gnomAD.\n5. Generate summary statistics: variants by impact category, genes with HIGH-impact variants.\n\n## Output Contract\n\n- Annotated VCF file (`.vcf.gz`).\n- Annotation summary: HTML report (SnpEff) or stats file (VEP).\n- Variant impact distribution (HIGH, MODERATE, LOW, MODIFIER counts).\n\n## Limits\n\n- SnpEff and VEP annotation databases must be pre-downloaded to the cache directory.\n- VEP plugins (CADD, LOFTEE, gnomAD) require separate data file downloads.\n- Annotation databases must match the reference genome build (GRCh37 vs GRCh38).\n- VEP is generally slower but more configurable than SnpEff.\n- Large VCFs (millions of variants) may require significant memory and runtime.\n- Common failure cases:\n  - SnpEff database name not matching the reference build (e.g., using `hg19` when VCF is on `GRCh38`).\n  - VEP cache directory missing or not matching the installed VEP version.\n  - VEP plugin data files (CADD, LOFTEE) not downloaded or path misconfigured.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fgenome-variant-annotation",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 00:01:40",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill genome-variant-annotation",{"tool":39,"command":40},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill genome-variant-annotation",{"tool":42,"command":43},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill genome-variant-annotation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002FSKILL.md",true,[47,53,57,61,67,71,75,79,85],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"agents\u002Fclaude.yaml","REFERENCE","参考文档",296,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fagents\u002Fclaude.yaml",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"agents\u002Fopenai.yaml",297,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fagents\u002Fopenai.yaml",{"path":58,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":59,"sourceHtmlUrl":60},"agents\u002FREADME.md",322,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fagents\u002FREADME.md",{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"assets\u002F.gitkeep","ASSET","资产文件",0,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fassets\u002F.gitkeep",{"path":68,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":69,"sourceHtmlUrl":70},"config.yaml",326,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fconfig.yaml",{"path":72,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002FREADME.md",68,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Freferences\u002FREADME.md",{"path":76,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fsnpeff-vep.md",5251,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Freferences\u002Fsnpeff-vep.md",{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"scripts\u002Fbatch_run.sh","SCRIPT","脚本",4564,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fscripts\u002Fbatch_run.sh",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Frun.sh",6869,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fgenome-variant-annotation\u002Fscripts\u002Frun.sh",[90,118,131,144,158,170],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":101,"publishedAt":21},"01M6CZ6S2C54G03K71G77H22KE","structured-intelligence-skill-creator","Create or update repository skills that conform to local templates, provider metadata requirements, registry rules, and validation workflows.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstructured-intelligence-skill-creator\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":99,"taxonomyTypeDesc":100},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"structured intelligence skill creator","skill creator","intelligence","structured","create","requirements","workflows","provider","update","registry","local","validation","metadata","repository","templates","Rules",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":21},"01M6CZ6M3JFN400406T1NPWNER","stat-survival-analysis","Estimate and compare survival functions using Kaplan-Meier curves and log-rank test, with optional Cox proportional hazards regression.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-survival-analysis\u002FSKILL.md",[],[125,126,127,128,129,130],"analysis","stat survival analysis","regression","log","functions","test",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":21},"01M6CZ6G5JPDZASM4DRD9QCTEE","stat-pca","Perform PCA on a numeric data matrix to reduce dimensionality, visualize sample structure, and identify top contributing features.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-pca\u002FSKILL.md",[],[138,139,140,141,142,143],"stat pca","sample","visualize","contributing","data","structure",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":21},"01M6CZ6D9A3N6849BT9VYFSBK5","stat-pairwise-correlation","Compute pairwise correlations and significance tests across all numeric variables, and produce a correlation heatmap.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-pairwise-correlation\u002FSKILL.md",[],[151,152,153,154,155,156,157],"stat pairwise correlation","correlation","produce","heatmap","compute","across","tests",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":21},"01M6CZ69DMF1PDKW83RWARQX6D","stat-nonlinear-embedding","Embed high-dimensional data into 2D using t-SNE or UMAP for exploratory visualization of sample or cell relationships.","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-nonlinear-embedding\u002FSKILL.md",[],[165,166,142,167,139,168,169],"stat nonlinear embedding","visualization","relationships","cell","exploratory visualization",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":21},"01M6CZ65YZGJV40F2FZNH04PBS","stat-logistic-regression","Fit a logistic regression model for binary outcomes, report odds ratios and model performance (AUC-ROC, confusion matrix).","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-logistic-regression\u002FSKILL.md",[],[127,177,178,179,180,181],"stat logistic regression","report","model","outcomes","performance"]