Data & Analytics

--> --- name: bio-proteomics-differential-abundance description: Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups. tool_type: mixed primary_tool: MSstats measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-proteomics-dia-analysis description: Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling. tool_type: cli primary_tool: diann measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: 'prs-net-deep-learning-agent' description: 'Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **PRS-Net Deep Learning Agent** implements interpretable geometric deep learning for polygenic risk score prediction. PRS...

--> --- name: bio-population-genetics-linkage-disequilibrium description: Calculate linkage disequilibrium statistics (r², D'), perform LD pruning for population structure analysis, identify haplotype blocks, and visualize LD patterns using PLINK, scikit-allel, and LDBlockShow. Use when calculating LD or pruning variants. tool_type: mixed primary_tool: plink2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_com...

--> --- name: bio-population-genetics-association-testing description: Genome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests. tool_type: cli primary_tool: plink2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - r...

--> --- name: bio-epidemiological-genomics-variant-surveillance description: Assign pathogen lineages and track variants using Nextclade and pangolin for viral surveillance. Monitor variant prevalence and identify emerging variants of concern. Use when classifying viral sequences, tracking lineage dynamics, or monitoring for variants of concern. tool_type: cli primary_tool: nextclade measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...

--> --- name: bio-epidemiological-genomics-transmission-inference description: Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or identifying superspreaders. tool_type: r primary_tool: TransPhylo measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allow...

--> --- name: bio-comparative-genomics-synteny-analysis description: Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions. tool_type: mixed primary_tool: MCScanX measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-to...

--> --- name: computational-pathology-agent description: Analyze Whole Slide Images (WSI) for digital pathology, including tissue segmentation and feature extraction. keywords: - wsi - digital-pathology - deep-learning - resnet - openslide measurable_outcome: Preprocess and extract tissue patches from a 1GB+ .svs slide within 15 minutes for downstream ML tasks. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility: - system: python 3.9+ allowed-tools: - run_shell_comm...

--> --- name: 'ctdna-dynamics-mrd-agent' description: 'AI-powered circulating tumor DNA dynamics analysis for molecular residual disease detection, treatment response monitoring, and early relapse prediction using liquid biopsy.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **ctDNA Dynamics MRD Agent** provides comprehensive analysis of circulating tumor DNA dynamics for molecular residual d...

--> --- name: 'pdx-model-analysis-agent' description: 'AI-powered analysis of patient-derived xenograft (PDX) models for drug response prediction, translational research, and personalized treatment selection.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **PDX Model Analysis Agent** provides AI-driven analysis of patient-derived xenograft models for preclinical drug testing, translational re...

--> --- name: 'mrd-edge-detection-agent' description: 'Ultra-sensitive AI-powered molecular residual disease detection using MRD-EDGE deep learning for sub-0.001% VAF ctDNA detection and early relapse prediction.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **MRD-EDGE Detection Agent** implements the MRD-EDGE (Enhanced Detection of ctDNA through Genomic Error suppression) deep learning algo...

--> --- name: 'liquid-biopsy-analytics-agent' description: 'Comprehensive analysis of liquid biopsy data (ctDNA, CTCs) for cancer detection, MRD monitoring, and response tracking.' keywords: - liquid-biopsy - ctdna - mrd - cancer-detection - treatment-response measurable_outcome: 'Detects circulating tumor DNA with 0.01% sensitivity and accurately predicts treatment response in longitudinal samples.' allowed-tools: - read_file - run_shell_command --- The **Liquid Biopsy Analytics Agent** prov...

--> --- name: bio-longitudinal-monitoring description: Tracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression. tool_type: python primary_tool: pandas measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. al...

--> --- name: 'exosome-ev-analysis-agent' description: 'AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Exosome/EV Analysis Agent** provides comprehensive AI-driven analysis of extracellular vesicles for cancer biomarker discovery, liqui...

--> --- name: bio-read-qc-contamination-screening description: Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination. tool_type: cli primary_tool: fastq_screen measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tool...

--> --- name: bio-multi-omics-mixomics-analysis description: Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups. tool_type: r primary_tool: mixOmics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell...

--> --- name: bio-microbiome-diversity-analysis description: Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure. tool_type: r primary_tool: phyloseq measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_she...

--> --- name: bio-metabolomics-xcms-preprocessing description: XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics. tool_type: r primary_tool: xcms measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Requires Bioconductor 3....

--> --- name: bio-metabolomics-targeted-analysis description: Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards. tool_type: mixed primary_tool: skyline measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-metabolomics-statistical-analysis description: Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models. tool_type: r primary_tool: mixOmics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-metabolomics-pathway-mapping description: Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways. tool_type: r primary_tool: MetaboAnalystR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-metabolomics-lipidomics description: Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways. tool_type: mixed primary_tool: lipidr measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-machine-learning-prediction-explanation description: Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions. tool_type: python primary_tool: shap measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---