Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when the user gives a topic and wants an automated video podcast created, or asks to learn visual design patterns from a reference video/image. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
定时自动维护 llmdoc 项目文档的 Skill。支持两种模式：聚合仓库模式（遍历子模块）和 单仓库模式（直接在当前仓库操作）。每天北京时间 5:00（UTC 21:00）由 GitHub Actions schedule 触发，也支持 workflow_dispatch 手动触发。收集指定时间范围内（默认过去 24 小时） 合并到目标分支的提交记录和 PR 内容，分析代码变更是否涉及功能新增、接口变更或架构调整， 如果需要则自动更新 llmdoc/ 下的对应文档并推送到目标分支。不改变外部行为的变更 （bug 修复、重构、依赖升级等）会被跳过。更新完成后输出结构化 JSON 结果并通过飞书通知。
Classify germline variants from VCF/BCF files according to the ACMG/AMP 2015 28-criteria evidence framework and generate clinical-grade interpretation reports with per-variant evidence audit trails and ACMG SF v3.2 secondary findings screening.
Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.
Aggregates QC reports from any bioinformatics tool outputs (FastQC, fastp, STAR, Picard, samtools, etc.) into a single MultiQC HTML report plus a ClawBio markdown summary with per-sample QC metrics.
Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
Generates professional clinical PDF reports in English from WES (Whole Exome Sequencing) data with clinical interpretation summary, pharmacogenomic alerts, and follow-up recommendations.
Generates professional clinical PDF reports in Spanish from WES (Whole Exome Sequencing) data with clinical interpretation, pharmacogenomic alerts, and follow-up recommendations.
Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.