Data & Analytics

--> --- 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...

--> --- 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...

--> --- name: 'epigenomics-methylgpt-agent' description: 'AI-powered DNA methylation analysis using MethylGPT foundation models for epigenomic profiling, differential methylation detection, and cancer epigenome characterization.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Epigenomics MethylGPT Agent** leverages foundation models for comprehensive DNA methylation analysis. It integrates M...

--> --- name: 'xlsx' description: '"Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas"' measurable_ou...

--> --- name: "spreadsheet" description: "Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified." measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: single-cell-rna-qc description: Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis. measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell...

--> --- name: 'xlsx' description: '"Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas"' measurable_ou...

--> --- name: bio-experimental-design-sample-size description: Estimates required sample sizes for differential expression, ChIP-seq, methylation, and proteomics studies. Use when budgeting experiments, writing grant proposals, or determining minimum replicates needed to achieve statistical significance for expected effect sizes. tool_type: r primary_tool: ssizeRNA measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_she...

--> --- name: bio-methylation-methylkit description: DNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection. tool_type: r primary_tool: methylKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-epitranscriptomics-m6a-differential description: Identify differential m6A methylation between conditions from MeRIP-seq. Use when comparing epitranscriptomic changes between treatment groups or cell states. tool_type: r primary_tool: exomePeak2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-chipseq-peak-annotation description: Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features. tool_type: r primary_tool: ChIPseeker measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_fi...

--> --- name: bio-chipseq-motif-analysis description: De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences. tool_type: cli primary_tool: HOMER measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Identify DNA sequence mot...

--> --- name: bio-chipseq-differential-binding description: Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions. tool_type: r primary_tool: DiffBind measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comm...

--> --- name: bio-atac-seq-differential-accessibility description: Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments. tool_type: r primary_tool: DiffBind measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-atac-seq-atac-qc description: Quality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples. tool_type: mixed primary_tool: deeptools 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: 'chromfound-scatac-foundation-model' description: 'Apply ChromFound, a genome-wide foundation model for single-cell chromatin accessibility, to scATAC embedding, annotation, regulatory discovery, and validation.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---

--> --- name: bio-molecular-descriptors description: Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria. tool_type: python primary_tool: RDKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_...

--> --- name: bio-admet-prediction description: Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety. tool_type: python primary_tool: ADMETlab measurable_outcome: Execute skill workflow successfully with valid output within 15...

--> --- name: bio-data-visualization-upset-plots description: Create UpSet plots to visualize set intersections as an alternative to Venn diagrams using UpSetR or upsetplot. Use when comparing overlapping gene sets, peak sets, or sample groups with more than 3 sets. tool_type: mixed primary_tool: UpSetR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-specialized-omics-plots description: Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots. tool_type: mixed primary_tool: ggplot2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-interactive-visualization description: Create interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards. tool_type: mixed primary_tool: plotly measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-genome-browser-tracks description: Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks. tool_type: mixed primary_tool: pyGenomeTracks measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-data-visualization-circos-plots description: Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations. tool_type: mixed primary_tool: Circos measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Circular genome visualizations for displaying m...

--> --- name: data-visualization-expert description: Generate insightful, publication-quality visualizations from complex datasets. keywords: - charts - plots - analysis - pandas - matplotlib - seaborn measurable_outcome: Create 3 high-resolution (300dpi) statistical plots (volcano, heatmap, scatter) within 15 minutes. license: MIT metadata: author: AI Agentic Skills Team version: "2.0.0" compatibility: - system: linux, macos allowed-tools: - run_shell_command - write_file - read_file --- A d...