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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,578 skills3 installs3,530 views
- Long Read Sequencing Agent--> --- name: 'long-read-sequencing-agent' description: 'AI-powered analysis of long-read sequencing data (PacBio, ONT) for structural variant detection, isoform discovery, epigenetic modifications, and de novo assembly.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Long-Read Sequencing Agent** provides comprehensive AI-driven analysis of long-read sequencing data from PacBio (HiFi) and Ox...Votes: 0GitHub stars: 6
- Abundance Estimation--> --- name: bio-metagenomics-abundance description: Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output. tool_type: cli primary_tool: bracken measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Amr Detection--> --- name: bio-metagenomics-amr-detection description: Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data. tool_type: cli primary_tool: AMRFinderPlus measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Id...Votes: 0GitHub stars: 6
- Functional Profiling--> --- name: bio-metagenomics-functional-profiling description: Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data. tool_type: cli primary_tool: humann measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Profile the functional potential of metagenomic samples using HUMAn...Votes: 0GitHub stars: 6
- Kraken Classification--> --- name: bio-metagenomics-kraken description: Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken. tool_type: cli primary_tool: kraken2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Metagenome Visualization--> --- name: bio-metagenomics-visualization description: Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output. tool_type: mixed primary_tool: phyloseq measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - ru...Votes: 0GitHub stars: 6
- Metaphlan Profiling--> --- name: bio-metagenomics-metaphlan description: Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resources are limited, or for comparison with HMP/other MetaPhlAn studies. tool_type: cli primary_tool: metaphlan measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - ru...Votes: 0GitHub stars: 6
- Strain Tracking--> --- name: bio-metagenomics-strain-tracking description: Track bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics. tool_type: cli primary_tool: MASH measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command -...Votes: 0GitHub stars: 6
- BioMaster--> --- name: biomaster-workflows description: Pipeline maestro keywords: - workflows - RNAseq - ChIPseq - automation - YAML measurable_outcome: Execute a configured pipeline end-to-end (including QC report + summary) within 24 hours of receiving inputs, logging every tool/parameter. license: MIT metadata: author: BioMaster Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Orchestrate BioMaster’s multi-agent pipelines (RNA-seq, ChIP-...Votes: 0GitHub stars: 6
- Nicheformer Spatial Agent--> --- name: 'nicheformer-spatial-agent' description: 'Foundation model-powered spatial transcriptomics analysis leveraging 53M+ spatially resolved cells for cellular architecture modeling and tissue niche discovery.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Nicheformer Spatial Agent** leverages the Nicheformer foundation model, trained on over 53 million spatially resolved cells, to ...Votes: 0GitHub stars: 6
- PopEVE Variant Predictor Agent--> --- name: 'popeve-variant-predictor-agent' description: 'AI-powered genetic variant pathogenicity prediction using PopEVE deep learning model for population-aware disease variant identification and rare disease diagnosis.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **PopEVE Variant Predictor Agent** leverages the PopEVE deep learning model from Harvard Medical School to predict pathoge...Votes: 0GitHub stars: 6
- PrecisionGroundedVariantSummarization Agent--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'precision-grounded-variant-summarization' description: 'Summarize genetic variants with LLM assistance grounded in evidence databases, provenance, conflicts, and hallucination controls.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---Votes: 0GitHub stars: 6
- RNA Velocity Agent--> --- name: 'rna-velocity-agent' description: 'AI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **RNA Velocity Agent** analyzes RNA velocity from single-cell RNA sequencing to predict cellular state transitions, differentia...Votes: 0GitHub stars: 6
- SIMO Multiomics Integration Agent--> --- name: 'simo-multiomics-integration-agent' description: 'AI-powered spatial integration of multi-omics datasets using probabilistic alignment for comprehensive tissue atlas construction and cellular state mapping.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **SIMO Multiomics Integration Agent** performs spatial integration of multi-omics datasets through probabilistic alignment. Unl...Votes: 0GitHub stars: 6
- Scprint2FoundationModelAgent Agent--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'scprint2-foundation-model-agent' description: 'Agent skill for applying the scPRINT-2 next-generation single-cell foundation model from the Cantini Lab to embedding, annotation, and inference tasks on scRNA-seq data.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---Votes: 0GitHub stars: 6
- CellAgent--> --- name: cellagent-annotation description: Cell tagger keywords: - single-cell - markers - annotation - confidence - tissue measurable_outcome: Label every provided cluster with a cell type + confidence + marker evidence (or "ambiguous") within 15 minutes per dataset. license: MIT metadata: author: CellAgent Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Use CellTypeAgent to interpret marker genes, annotate scRNA-seq clusters...Votes: 0GitHub stars: 6
- RNA--> --- name: 'universal-single-cell-annotator' description: 'Annotate scRNA-seq' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context.Votes: 0GitHub stars: 6
- CompBioAgent--> --- name: compbioagent-explorer description: scRNA-seq Explorer keywords: - single-cell - visualization - web-app - cellxgene - exploration measurable_outcome: Launch a local web instance for interactive scRNA-seq exploration and generate 3+ custom visualizations per session. license: MIT metadata: author: CompBioAgent Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - web_fetch --- An LLM-powered web application for single-cell RNA-seq data ex...Votes: 0GitHub stars: 6
- Batch Integration--> --- name: bio-single-cell-batch-integration description: Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets. tool_type: mixed primary_tool: Harmony measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Integrate multiple scRNA-seq ...Votes: 0GitHub stars: 6
- Cell Annotation--> --- name: bio-single-cell-cell-annotation description: Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets. tool_type: mixed primary_tool: CellTypist measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Cell Communication--> --- name: bio-single-cell-cell-communication description: Infer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell types. tool_type: mixed primary_tool: CellChat measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Clustering--> --- name: bio-single-cell-clustering description: scRNA-seq clustering analysis keywords: - clustering - single-cell - seurat - scanpy - umap measurable_outcome: Identifies stable cell clusters with silhouette score > 0.5. tool_type: mixed primary_tool: Seurat allowed-tools: - read_file - run_shell_command --- Dimensionality reduction, neighbor graph construction, and clustering.Votes: 0GitHub stars: 6
- Data Io--> --- name: bio-single-cell-data-io description: Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats. tool_type: mixed primary_tool: Seurat measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-to...Votes: 0GitHub stars: 6
- Doublet Detection--> --- name: bio-single-cell-doublet-detection description: Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data. tool_type: mixed primary_tool: Scrublet measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. al...Votes: 0GitHub stars: 6
- Lineage Tracing--> --- name: bio-single-cell-lineage-tracing description: Reconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories. tool_type: python primary_tool: Cassiopeia measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Markers Annotation--> --- name: bio-single-cell-markers-annotation description: Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters. tool_type: mixed primary_tool: Seurat measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed...Votes: 0GitHub stars: 6
- Metabolite Communication--> --- name: bio-single-cell-metabolite-communication description: Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions. tool_type: python primary_tool: MeboCost measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. all...Votes: 0GitHub stars: 6
- Multimodal Integration--> --- name: bio-single-cell-multimodal-integration description: Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data. tool_type: mixed primary_tool: Seurat measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---...Votes: 0GitHub stars: 6
- Perturb Seq--> --- name: bio-single-cell-perturb-seq description: Analyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells. tool_type: python primary_tool: Pertpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Preprocessing--> --- name: bio-single-cell-preprocessing description: Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data. tool_type: mixed primary_tool: Seurat measurable_outcome: Execute skill workflow successfully with valid output within 15 m...Votes: 0GitHub stars: 6
- Scatac Analysis--> --- name: bio-single-cell-scatac-analysis description: Single-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data. tool_type: r primary_tool: Signac measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Analyze si...Votes: 0GitHub stars: 6
- Trajectory Inference--> --- name: bio-single-cell-trajectory-inference description: Infer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime. tool_type: mixed primary_tool: Monocle3 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Clustering--> --- name: bio-single-cell-clustering description: Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data. tool_type: mixed primary_tool: Seurat measurable_outcome: Execute skill workflow successfully with valid output with...Votes: 0GitHub stars: 6
- Spatial Epigenomics Agent--> --- name: 'spatial-epigenomics-agent' description: 'AI-powered spatial epigenomics analysis combining chromatin accessibility, histone modifications, and DNA methylation with spatial coordinates for tissue architecture mapping.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Spatial Epigenomics Agent** analyzes spatial epigenomic data combining chromatin accessibility (ATAC-seq), histone...Votes: 0GitHub stars: 6
- Spatial Transcriptomics--> --- name: spatial-transcriptomics-analysis description: Automated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression. keywords: - spatial-transcriptomics - visium - xenium - scanpy - squidpy measurable_outcome: Process a Visium dataset, identify spatially variable genes, and generate spatial feature plots within 30 minutes. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility: - system: python 3.9+ allowed-too...Votes: 0GitHub stars: 6
- STAgent--> --- name: spatial-transcriptomics-agent description: Spatial analyst keywords: - spatial - h5ad - H&E - clustering - SVG measurable_outcome: For each sample, deliver ≥1 spatial domain map + SVG list + narrative interpretation within 30 minutes. license: MIT metadata: author: LiuLab version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file - web_fetch --- Run STAgent to align histology images with expression matrices, perform clustering/SVG detect...Votes: 0GitHub stars: 6
- SpatialAgent--> --- name: 'spatial-agent' description: 'An agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- SpatialAgent focuses on the biological interpretation of spatial transcriptomics data, specifically aiming to propose mechanistic hypotheses about tissue organization and cellular interac...Votes: 0GitHub stars: 6
- Image Analysis--> --- name: bio-spatial-transcriptomics-image-analysis description: Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comma...Votes: 0GitHub stars: 6
- Spatial Communication--> --- name: bio-spatial-transcriptomics-spatial-communication description: Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: ...Votes: 0GitHub stars: 6
- Spatial Data Io--> --- name: bio-spatial-transcriptomics-spatial-data-io description: Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file...Votes: 0GitHub stars: 6
- Spatial Deconvolution--> --- name: bio-spatial-transcriptomics-spatial-deconvolution description: Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots. tool_type: python primary_tool: cell2location measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: ...Votes: 0GitHub stars: 6
- Spatial Domains--> --- name: bio-spatial-transcriptomics-spatial-domains description: Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comm...Votes: 0GitHub stars: 6
- Spatial Multiomics--> --- name: bio-spatial-transcriptomics-spatial-multiomics description: Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Spatial Neighbors--> --- name: bio-spatial-transcriptomics-spatial-neighbors description: Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---...Votes: 0GitHub stars: 6
- Spatial Preprocessing--> --- name: bio-spatial-transcriptomics-spatial-preprocessing description: Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file ...Votes: 0GitHub stars: 6
- Spatial Proteomics--> --- name: bio-spatial-transcriptomics-spatial-proteomics description: Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics. tool_type: python primary_tool: scimap measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file...Votes: 0GitHub stars: 6
- Spatial Statistics--> --- name: bio-spatial-transcriptomics-spatial-statistics description: Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_...Votes: 0GitHub stars: 6
- Spatial Visualization--> --- name: bio-spatial-transcriptomics-spatial-visualization description: Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Create visualizations f...Votes: 0GitHub stars: 6
- Variant Interpretation--> --- name: 'variant-interpretation-acmg' description: 'Classifies genetic variants according to ACMG (American College of Medical Genetics) guidelines.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Variant Interpretation Skill** automates the classification of genetic variants (Pathogenic, Benign, VUS) using a rules-based engine derived from ACMG guidelines.Votes: 0GitHub stars: 6
- Clinical Interpretation--> --- name: bio-variant-calling-clinical-interpretation description: Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants. tool_type: mixed primary_tool: InterVar measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Prioritize and interpret vari...Votes: 0GitHub stars: 6