Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
Router skill for ToolUniverse tasks. First checks if specialized tooluniverse skills (105+ skills covering disease/drug/target research, gene-disease associations, clinical decision support, genomics, epigenomics, proteomics, comparative genomics, chemical safety, toxicology, systems biology, and more) can solve the problem, then falls back to general strategies for using 2300+ scientific tools. Covers tool discovery, multi-hop queries, comprehensive research workflows, disambiguation, evidence grading, and report generation. Use when users need to research any scientific topic, find biological data, or explore drug/target/disease relationships. ALSO USE for any biology, medicine, chemistry, pharmacology, or life science question — even simple factoid questions like "how many X in protein Y", "what drug interacts with Z", "what gene causes disease W", or "translate this sequence". These questions benefit from database lookups (UniProt, PubMed, ChEMBL, ClinVar, GWAS Catalog, etc.) rather than answering from me
Comprehensive literature deep research across any academic domain using 120+ ToolUniverse tools. Conducts subject disambiguation, systematic literature search with citation network expansion, evidence grading (T1-T4), and structured theme extraction. Produces detailed reports with mandatory completeness checklists, integrated models, and testable hypotheses. Use when users need thorough literature reviews, target/drug/disease profiles, topic deep-dives, claim verification, or systematic evidence synthesis. Supports biomedical (genes, proteins, drugs, diseases), computer science, social science, and general academic topics. For single factoid questions, uses a fast verification mode with inline answer.
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.
Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.
Retrieves biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. Creates detailed reports with sequence metadata, cross-database references, and download options. Use when users need nucleotide sequences, protein sequences, genome data, or mention GenBank, RefSeq, EMBL accessions.
Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.
Provide actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, finds resistance mechanisms, matches clinical trials. Use when oncologist asks about treatment options for specific mutations (EGFR, KRAS, BRAF, etc.), therapy resistance, or clinical trial eligibility.
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Creates comprehensive compound profiles with identifiers, properties, bioactivity, and drug information. Use when users need chemical data, drug information, or mention PubChem CID, ChEMBL ID, SMILES, InChI, or compound names.
Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
Find and retrieve proteomics datasets from public repositories including MassIVE and ProteomeXchange (which aggregates PRIDE, PeptideAtlas, jPOST, and iProX). Search by species, keyword, or accession. Get detailed dataset metadata including instruments, publications, species, modifications, and file counts. Use when asked to find proteomics datasets, search for mass spectrometry data, look up ProteomeXchange or MassIVE accessions, or discover publicly available proteomics experiments for a given organism or topic.
Retrieve and analyze biological sequences -- gene/protein sequences from NCBI, Ensembl, and UniProt. Search nucleotide databases, fetch by accession, find orthologs, get gene summaries. Use when users ask about DNA/RNA/protein sequences, gene lookups, ortholog searches, or sequence retrieval.
Analyze post-translational modifications (PTMs) of proteins — modification sites, types, proteoforms, functional effects at PTM sites, and PTM-dependent protein interactions. Integrates iPTMnet, ProtVar, UniProt, and STRING databases. Use when asked about protein phosphorylation, ubiquitination, acetylation, glycosylation, methylation, SUMOylation, or other PTMs; proteoform diversity; PTM-regulated interactions; or functional impact of PTM sites.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment, treatment algorithm, pharmacogenomic guidance, clinical trial matches, and monitoring plan.
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, bonding theory (covalency, orbital mixing), symmetry/point groups, thermodynamics, kinetics, spectroscopy interpretation, noble gas compounds, lanthanide/actinide chemistry. Use for questions about crystal systems, unit cells, density calculations, metal complexes, solid-state chemistry, or physical chemistry calculations.
Neuroscience research and reasoning workflows using ToolUniverse tools. Covers computational neuroscience (rate models, integrate-and-fire neurons, synaptic plasticity, network dynamics), neuroanatomy (cortical regions, basal ganglia, cerebellum, brainstem, model organism connectomes), neurophysiology (ion channels, action potentials, synaptic transmission), neural circuits (E/I balance, oscillations, central pattern generators), synaptic dynamics (STDP, short-term plasticity, neuromodulation), neurodegenerative diseases (Alzheimer's, Parkinson's, ALS, Huntington's), and clinical neurology (cranial nerves, stroke localization, neuromuscular disorders). Use when researchers ask about brain regions, neural computation, firing rates, synaptic plasticity, connectomics, neurodegeneration, or clinical neurological questions.
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
