Data & Analytics

--> --- name: bio-crispr-screens-jacks-analysis description: JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments. tool_type: python primary_tool: JACKS measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- JACKS jointly models ...

--> --- name: bio-crispr-screens-hit-calling description: Statistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes. tool_type: mixed primary_tool: bagel2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-crispr-screens-batch-correction description: Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation. tool_type: python primary_tool: scipy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-copy-number-gatk-cnv description: Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines. tool_type: cli primary_tool: gatk measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-copy-number-cnvkit-analysis description: Detect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data. tool_type: cli primary_tool: cnvkit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-copy-number-cnv-visualization description: Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations. tool_type: mixed primary_tool: matplotlib measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-variant-calling-joint-calling description: Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples. tool_type: cli primary_tool: GATK measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Call variants jointly across multi...

--> --- name: bio-variant-calling-filtering-best-practices description: Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices. tool_type: mixed primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- 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...

--> --- 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_...

--> --- 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: ...

--> --- 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...

--> --- 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: ...

--> --- 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...

--> --- 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...

--> --- 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 ---

--> --- 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...

--> --- 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 ---

--> --- 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 ---

--> --- 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...

--> --- 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 ---

--> <!-- 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 ---

--> --- 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...

--> --- 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 ...