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Found 34 Skills
Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.
Optimize tool descriptions in ToolUniverse JSON configs for clarity and usability. Reviews descriptions for missing prerequisites, unexpanded abbreviations, unclear parameters, and missing usage guidance. Use when reviewing tool descriptions, improving API documentation, or when user asks to check if tools are easy to understand.
Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology. Builds multi-layer networks from ChEMBL, OpenTargets, STRING, DrugBank, Reactome, FAERS, and 60+ other ToolUniverse tools. Calculates Network Pharmacology Scores (0-100), identifies repurposing candidates, predicts mechanisms, and analyzes polypharmacology. Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology.
Add custom local tools to ToolUniverse and use them alongside the 1000+ built-in tools. Use this skill when a user wants to: create their own tool for a private or custom API, add a local tool to their workspace, integrate an internal service with ToolUniverse, or use a custom tool via the MCP server or Python API. Covers both the JSON config approach (easiest, no Python needed) and the Python class approach (full control). Also covers how to verify tools loaded correctly and how to call them. Also covers the plugin package approach for reusable, shareable, pip-installable tool sets.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
Immunology research workflows using ToolUniverse tools. Covers antibody-antigen structural analysis (SAbDab, TheraSAbDab), immune protein interactions (IntAct, BioGRID), epitope and T-cell/B-cell assay data (IEDB), immunoglobulin gene databases (IMGT), cytokine/receptor signaling (OpenTargets, GWAS), clinical safety data for immune diseases (FAERS, clinical trials), autoimmune disease genetics (Orphanet), and immune pathway analysis (KEGG, Reactome). Use when researchers ask about antibody targets, immune signaling networks, autoimmune genetics, immunotherapy safety, epitope discovery, or immune pathway enrichment.
Solve quantitative problems in biophysics, pharmacokinetics, epidemiology, toxicology, population genetics, and statistical mechanics. Provides reasoning strategies and Python templates for calculations alongside ToolUniverse data lookups. Use when users ask about drug dosing, half-life decay, radioactive tracers, R0, herd immunity, diffusion, Hardy-Weinberg, binding equilibria, or any computation-heavy biology/chemistry question.
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.
Systematic ACMG/AMP variant classification using ToolUniverse tools. Given a genetic variant (HGVS, rsID, or gene+change), applies all 28 ACMG criteria (PVS1, PS1-4, PM1-6, PP1-5, BA1, BS1-4, BP1-7) through automated database queries and computational predictions. Produces a final 5-tier classification (Pathogenic / Likely Pathogenic / VUS / Likely Benign / Benign) with evidence summary. Use when asked to classify a variant, interpret a VUS, apply ACMG criteria, assess pathogenicity, or determine clinical significance of a germline variant.
GitHub workflow for ToolUniverse - push code safely by moving temp files, activating pre-commit hooks, running tests, and cleaning staged files. Use when pushing to GitHub, fixing CI failures, or cleaning up before commits.