Teng-bio ToolUniverse Router
Route user questions to specialized skills. If no skill matches, use general strategies from references/general-strategies.md.
General Reasoning Protocols
When answering scientific questions:
- Look up, don't guess: Use ToolUniverse tools to verify facts before answering.
- Compute, don't estimate: Write and run Python code (via Bash) for any calculation. Never do mental math.
- Analyze, don't just retrieve: When a question requires data analysis, download the data and run the analysis — don't just describe what you would do.
- Route to specialized skills: Use the Routing Table below to find domain-specific reasoning protocols.
You have full code execution. For any analysis task (statistics, data wrangling, visualization), write Python and execute it. ToolUniverse tools find data and metadata; Python code does the analysis.
Routing Workflow
- Extract keywords from user's question
- Scan routing table below for keyword matches
- Take action:
- 1 clear match → invoke that skill NOW using the Skill tool
- Multiple matches → ask user which they prefer (AskUserQuestion)
- No match → use general strategies (load references/general-strategies.md)
- If ambiguous (e.g., "Tell me about aspirin") → ask user to clarify intent
CRITICAL: Actually INVOKE skills — don't describe them or show the routing table to the user.
LOOK UP, DON'T GUESS: If you are not confident about a factual claim, SEARCH for it. Use PubMed_search_articles or EuropePMC_search_articles to find the answer in literature. Use UniProt_search / proteins_api_search for protein facts. Use NCBI_search_gene for gene facts. Use GBIF_search_species for taxonomy. Use PubChem_get_compound_by_name for chemical facts. A tool-verified answer is always better than a guess from memory. When uncertain, your first instinct should be to SEARCH, not to reason harder.
Consistency rule: If you've seen a similar question before in this batch, do NOT reuse your previous answer. Solve each question from scratch — the similar-looking question may have different details that change the answer.
For MC questions: Before eliminating options, check if the question asks about a SPECIFIC fact (protein name, gene function, species behavior, experimental result). If so, SEARCH for it first — then use the search result to eliminate options. This is faster and more reliable than reasoning from memory.
NEVER REFUSE: If a question is hard, attempt it anyway. First try to look up the answer. If tools don't help, use reasoning strategies from the skill. A wrong answer is better than "this requires further analysis."
COMPUTE, DON'T ESTIMATE: When a problem gives numerical values and asks for a numerical answer, WRITE AND RUN Python code. Do not attempt mental arithmetic on multi-step problems.
MULTIPLE CHOICE STRATEGY: When the question has answer choices (A, B, C, D...):
- Read the question CAREFULLY — identify exactly what is being asked
- Read ALL answer choices before reasoning — don't stop at the first plausible one
- ELIMINATE clearly wrong options first (usually 2-3 can be ruled out immediately)
- For the remaining options, reason through each: WHY would this be correct? WHY would this be wrong?
- If you have two similar-sounding options, look for the KEY DIFFERENCE between them
- Your final answer MUST be a single letter — nothing else
- COMMON ERROR: reasoning correctly but reporting the wrong letter. After choosing, re-read your choice letter and verify it matches your reasoning.
- MC traps: "All/None of the above" is correct only ~25% of the time — don't default to it. Options with absolute language ("always", "never", "only") are usually wrong. The longest/most detailed option is correct more often. When two options are opposites, one of them is usually correct.
- Quantitative MC: When an MC question involves a number, COMPUTE the answer first (use Python), THEN match to the closest option. Do not let the listed choices bias your calculation.
- For scored MC questions, run
mc_analyzer.py(inskills/tooluniverse-computational-biophysics/scripts/) to enforce systematic elimination before committing to an answer.
CRITICAL FOR BATCH PROCESSING: When answering multiple MC questions in sequence, do NOT rush. Apply the FULL elimination process to EVERY question. Common batch error: answering based on first impression without elimination. For each MC question, you MUST:
- Write out at least 2 eliminated options with reasons BEFORE selecting your answer
- If you cannot eliminate any options, that's a sign you need to LOOK UP information
VERIFY BEFORE ANSWERING: Before giving your final answer, run these checks:
- If your answer is a number: does it have the right order of magnitude? (A drug dose of 500 kg is wrong. A pH of 15 is wrong. A percentage > 100% is wrong.)
- If your answer is a letter choice: re-read the question and ALL choices. Are you sure your reasoning matches the choice letter you picked? A common error is reasoning correctly but picking the wrong letter.
- If your answer involves a protein or gene: did you look up the CORRECT one? (GABRA1 ≠ GABRR1. BRCA1 ≠ BRCA2. TP53 ≠ TP63.)
- If your answer disagrees with a tool result: trust the tool over your memory. Databases are updated; your training data has a cutoff.
- If answering MC in a batch: SLOW DOWN. Apply elimination to each question individually. The time cost of careful elimination is tiny compared to getting the answer wrong.
BATCH PROCESSING PROTOCOL — when answering multiple questions in sequence:
- For EACH question independently: read the question, identify the domain, invoke the relevant skill
- NEVER carry assumptions from one question to the next
- For EVERY MC question: write "Eliminating: [letter] because [reason]" for at least 2 options BEFORE giving your answer
- For EVERY numerical question: write and execute Python code — report the computed result, not a mental estimate
- For EVERY "what protein/gene/species" question: search a database FIRST, then answer
- Between questions: mentally reset. Each question is independent.
Answer Format Rules (numerical answers):
- If the question says "find the number", give JUST the number — no units unless asked.
- Match the precision of the question: if data uses 2 decimal places, answer with 2 decimal places.
- For large numbers (>10^6): use scientific notation. If the question specifies units like "in units of 10^28", give just the coefficient (e.g., 1.86).
- For small numbers: match the format shown in the question (e.g., "1.776 × 10^-3" not "1.8e-3").
- NEVER add units, descriptions, or explanations to numerical answers unless the question explicitly asks for them.
- When a question asks for a pure number (e.g., "how many days"), give ONLY the number (e.g., "350" not "350 days").
Language: If the user writes in a non-English language, extract keywords for routing but respond in their language. All tool calls use English terms.
Routing Table
1. Data Retrieval
| Keywords | Action |
|---|---|
| "get", "retrieve", "chemical compound", "PubChem", "ChEMBL", "drug molecule", "SMILES", "InChI" | Skill(skill="tooluniverse-chemical-compound-retrieval") |
| "get", "retrieve", "expression data", "gene expression", "omics dataset", "ArrayExpress", "RNA-seq", "microarray" | Skill(skill="tooluniverse-expression-data-retrieval") |
| "get", "retrieve", "protein structure", "PDB", "AlphaFold", "crystal structure", "3D model" | Skill(skill="tooluniverse-protein-structure-retrieval") |
| "get", "retrieve", "sequence", "DNA sequence", "RNA sequence", "protein sequence", "FASTA" | Skill(skill="tooluniverse-sequence-retrieval") |
| "find data", "search datasets", "dataset", "where can I get data", "cohort study", "data repository", "public data", "download data for analysis", "what data exists for" | Skill(skill="tooluniverse-dataset-discovery") |
| "data wrangling", "download bulk data", "parse format", "API access pattern", "direct API", "raw data download", "beyond tools", "bulk download" | Skill(skill="tooluniverse-data-wrangling") |
2. Research & Profiling
| Keywords | Action |
|---|---|
| "research", "profile", "disease", "syndrome", "disorder", "comprehensive report on [disease]" | Skill(skill="tooluniverse-disease-research") |
| "research", "profile", "drug", "medication", "therapeutic agent", "tell me about [drug]" | Skill(skill="tooluniverse-drug-research") |
| "literature review", "papers about", "publications on", "research articles", "recent studies" | Skill(skill="tooluniverse-literature-deep-research") |
| "research", "profile", "target", "protein target", "gene target", "target validation" | Skill(skill="tooluniverse-target-research") |
3. Clinical Decision Support
| Keywords | Action |
|---|---|
| "drug safety", "adverse events", "side effects", "pharmacovigilance", "pharmacogenomics", "FAERS", "black box warning" | Skill(skill="tooluniverse-pharmacovigilance") |
| "adverse event signal", "safety signal detection", "disproportionality", "PRR", "ROR" | Skill(skill="tooluniverse-adverse-event-detection") |
| "drug safety profile", "drug safety assessment", "comprehensive safety" | Skill(skill="tooluniverse-drug-safety-profiling") |
| "chemical safety", "ADMET", "chemical toxicity", "environmental toxicity", "toxic effects" | Skill(skill="tooluniverse-chemical-safety") |
| "cancer treatment", "precision oncology", "tumor mutation", "targeted therapy", "EGFR", "KRAS", "BRAF" | Skill(skill="tooluniverse-precision-oncology") |
| "cancer driver", "driver gene", "driver mutation", "IntOGen", "cBioPortal" | Skill(skill="tooluniverse-cancer-driver-analysis") |
| "somatic mutation interpretation", "cancer variant", "oncogenic variant", "tumor variant" | Skill(skill="tooluniverse-cancer-variant-interpretation") |
| "ACMG classification", "variant classification", "benign/pathogenic", "ACMG criteria", "PM2", "PS1", "PP3" | Skill(skill="tooluniverse-acmg-variant-classification") |
| "cancer classification", "OncoTree", "tumor subtype", "cancer type code", "histological classification" | Skill(skill="tooluniverse-cancer-classification") |
| "TCGA", "cancer genomics cohort", "GDC analysis", "TCGA mutations", "pan-cancer" | Skill(skill="tooluniverse-cancer-genomics-tcga") |
| "immunotherapy response", "checkpoint inhibitor response", "TMB", "MSI", "PD-L1", "ICI response" | Skill(skill="tooluniverse-immunotherapy-response-prediction") |
| "rare disease diagnosis", "differential diagnosis", "phenotype matching", "HPO", "patient with [symptoms]" | Skill(skill="tooluniverse-rare-disease-diagnosis") |
| "variant interpretation", "VUS", "pathogenicity", "clinical significance", "is [variant] pathogenic" | Skill(skill="tooluniverse-variant-interpretation") |
| "clinical guidelines", "practice guidelines", "treatment guidelines", "dosing recommendations", "standard of care" | Skill(skill="tooluniverse-clinical-guidelines") |
| "patient stratification", "precision medicine", "biomarker stratification", "treatment selection" | Skill(skill="tooluniverse-precision-medicine-stratification") |
4. Discovery & Design
| Keywords | Action |
|---|---|
| "find binders", "virtual screening", "hit identification", "compounds for [target]", "IC50", "bioactivity", "binding affinity", "potency", "selectivity", "SAR", "structure-activity", "lead optimization", "hit-to-lead" | Skill(skill="tooluniverse-binder-discovery") |
| "drug repurposing", "new indication", "existing drugs for [disease]", "repurpose [drug]" | Skill(skill="tooluniverse-drug-repurposing") |
| "drug target validation", "target druggability", "validate target", "target assessment" | Skill(skill="tooluniverse-drug-target-validation") |
| "network pharmacology", "polypharmacology", "compound-target network", "multi-target" | Skill(skill="tooluniverse-network-pharmacology") |
| "design protein", "protein binder", "de novo protein", "RFdiffusion", "ProteinMPNN" | Skill(skill="tooluniverse-protein-therapeutic-design") |
| "antibody engineering", "antibody design", "humanization", "affinity maturation" | Skill(skill="tooluniverse-antibody-engineering") |
| "ADMET prediction", "ADME", "absorption", "distribution", "metabolism", "excretion", "toxicity prediction" | Skill(skill="tooluniverse-admet-prediction") |
| "small molecule discovery", "chemical biology", "compound sourcing", "hit finding", "chemical probe" | Skill(skill="tooluniverse-small-molecule-discovery") |
| "chemical sourcing", "buy compound", "vendor search", "Enamine", "MolPort", "compound availability" | Skill(skill="tooluniverse-chemical-sourcing") |
| "GPCR", "G-protein coupled receptor", "GPCRdb", "receptor ligand", "biased agonist" | Skill(skill="tooluniverse-gpcr-structural-pharmacology") |
5. Genomics & Variant Analysis
| Keywords | Action |
|---|---|
| "GWAS study", "genome-wide association", "GWAS catalog", "GWAS for [trait]" | Skill(skill="tooluniverse-gwas-study-explorer") |
| "GWAS trait to gene", "trait-associated genes", "causal genes", "genes for [trait]" | Skill(skill="tooluniverse-gwas-trait-to-gene") |
| "fine-mapping", "credible sets", "causal variants", "statistical refinement" | Skill(skill="tooluniverse-gwas-finemapping") |
| "SNP interpretation", "rsID", "rs[number]", "variant annotation" | Skill(skill="tooluniverse-gwas-snp-interpretation") |
| "polygenic risk", "PRS", "genetic risk", "risk score for [disease]" | Skill(skill="tooluniverse-polygenic-risk-score") |
| "structural variant", "SV", "CNV", "deletion", "duplication", "chromosomal rearrangement" | Skill(skill="tooluniverse-structural-variant-analysis") |
| "VCF", "variant calling", "mutation analysis", "variant annotation pipeline" | Skill(skill="tooluniverse-variant-analysis") |
| "variant functional annotation", "protein variant effect", "variant consequence", "missense effect" | Skill(skill="tooluniverse-variant-functional-annotation") |
| "regulatory variant", "non-coding variant", "eQTL variant", "regulatory region variant" | Skill(skill="tooluniverse-regulatory-variant-analysis") |
| "rare disease genomics", "Orphanet gene", "rare disease gene", "causative gene", "exome diagnosis" | Skill(skill="tooluniverse-rare-disease-genomics") |
| "1000 Genomes", "IGSR", "population frequency", "superpopulation", "AFR/EUR/EAS/SAS/AMR" | Skill(skill="tooluniverse-population-genetics-1000genomes") |
6. Systems & Network Analysis
| Keywords | Action |
|---|---|
| "protein interactions", "PPI", "interactome", "binding partners", "protein complexes" | Skill(skill="tooluniverse-protein-interactions") |
| "systems biology", "pathway analysis", "network analysis", "gene set enrichment" | Skill(skill="tooluniverse-systems-biology") |
| "metabolomics", "metabolite identification", "metabolic pathway" | Skill(skill="tooluniverse-metabolomics") |
| "epigenomics", "gene regulation", "transcription factor", "TF binding", "enhancers", "chromatin", "ChIP-seq" | Skill(skill="tooluniverse-epigenomics") |
| "gene enrichment", "pathway enrichment", "GO enrichment", "GSEA", "overrepresentation", "gene list analysis" | Skill(skill="tooluniverse-gene-enrichment") |
| "multi-omics", "omics integration", "transcriptomics + proteomics", "integrated analysis" | Skill(skill="tooluniverse-multi-omics-integration") |
| "multi-omic disease", "disease characterization", "genomic + transcriptomic + proteomic" | Skill(skill="tooluniverse-multiomic-disease-characterization") |
| "gene regulatory network", "GRN", "TF network", "regulatory circuit", "gene regulation network" | Skill(skill="tooluniverse-gene-regulatory-networks") |
| "epigenomics chromatin", "histone modification", "chromatin accessibility", "ATAC-seq", "DNase-seq" | Skill(skill="tooluniverse-epigenomics-chromatin") |
| "pathway disease", "disease pathway", "pathway genetics", "pathway convergence" | Skill(skill="tooluniverse-pathway-disease-genetics") |
| "metabolomics pathway", "metabolic pathway mapping", "pathway-level metabolomics" | Skill(skill="tooluniverse-metabolomics-pathway") |
| "interpret results", "biological context", "beyond p-values", "what does this result mean", "integrate analysis with biology", "statistical results + biology", "causal reasoning", "evidence integration" | Skill(skill="tooluniverse-data-integration-analysis") |
7. Screening & Functional Genomics
| Keywords | Action |
|---|---|
| "CRISPR screen", "genetic screen", "screen hits", "essential genes" | Skill(skill="tooluniverse-crispr-screen-analysis") |
| "drug-drug interaction", "DDI", "drug combination", "polypharmacy" | Skill(skill="tooluniverse-drug-drug-interaction") |
| "differential expression", "DESeq2", "RNA-seq analysis", "DE genes", "fold change" | Skill(skill="tooluniverse-rnaseq-deseq2") |
| "proteomics", "mass spectrometry", "protein quantification", "TMT", "iTRAQ", "label-free" | Skill(skill="tooluniverse-proteomics-analysis") |
| "immune repertoire", "TCR", "BCR", "T-cell receptor", "B-cell receptor", "clonotype" | Skill(skill="tooluniverse-immune-repertoire-analysis") |
| "spatial transcriptomics", "Visium", "MERFISH", "seqFISH", "Slide-seq", "spatial gene expression" | Skill(skill="tooluniverse-spatial-transcriptomics") |
| "spatial omics", "spatial proteomics", "spatial multi-omics" | Skill(skill="tooluniverse-spatial-omics-analysis") |
| "microscopy", "image analysis", "cell counting", "colony morphometry", "fluorescence quantification" | Skill(skill="tooluniverse-image-analysis") |
| "electron microscopy", "cryo-EM", "TEM", "SEM", "EMPIAR", "EMDB" | Skill(skill="tooluniverse-electron-microscopy") |
| "cell line", "cell line profiling", "DepMap", "CCLE", "cell line sensitivity" | Skill(skill="tooluniverse-cell-line-profiling") |
| "clinical data integration", "clinical phenotype", "EHR analysis", "clinical cohort" | Skill(skill="tooluniverse-clinical-data-integration") |
| "phylogenetics", "phylogenetic tree", "sequence alignment", "evolutionary analysis" | If .project_os/ exists, route through research-project-os first; otherwise use general sequence/tree analysis strategies rather than invoking a removed phylogenetics skill. |
| "statistical modeling", "regression analysis", "logistic regression", "survival analysis", "Cox" | Skill(skill="tooluniverse-statistical-modeling") |
| "metabolomics analysis", "LC-MS analysis", "metabolite quantification", "metabolic flux" | Skill(skill="tooluniverse-metabolomics-analysis") |
| "functional genomics screen", "CRISPR library", "shRNA screen", "barcode screen" | Skill(skill="tooluniverse-functional-genomics-screens") |
| "proteomics data", "PRIDE", "MassIVE", "ProteomeXchange", "proteomics dataset" | Skill(skill="tooluniverse-proteomics-data-retrieval") |
| "protein modification", "PTM analysis", "phosphorylation site", "ubiquitination", "glycosylation" | Skill(skill="tooluniverse-protein-modification-analysis") |
| "structural proteomics", "cross-linking mass spec", "XL-MS", "HDX-MS", "structural biology" | Skill(skill="tooluniverse-structural-proteomics") |
| "protein structure prediction", "AlphaFold prediction", "structure modeling", "homology modeling" | Skill(skill="tooluniverse-protein-structure-prediction") |
8. Clinical Trials & Study Design
| Keywords | Action |
|---|---|
| "clinical trial design", "trial protocol", "study design", "endpoint selection" | Skill(skill="tooluniverse-clinical-trial-design") |
| "clinical trial matching", "patient-to-trial", "trial eligibility", "find trials for patient" | Skill(skill="tooluniverse-clinical-trial-matching") |
| "GWAS drug discovery", "genetic target validation", "GWAS to drug" | Skill(skill="tooluniverse-gwas-drug-discovery") |
| "epidemiological analysis", "epidemiology", "risk factors", "exposure-outcome", "observational study", "confounder adjustment", "disease risk analysis", "analyze health data", "regression on clinical data", "survival analysis on cohort" | Skill(skill="tooluniverse-epidemiological-analysis") |
9. Organism & Evolution
| Keywords | Action |
|---|---|
| "model organism", "mouse phenotype", "fly ortholog", "worm", "zebrafish", "yeast", "cross-species" | Skill(skill="tooluniverse-model-organism-genetics") |
| "comparative genomics", "ortholog", "paralog", "conservation", "evolutionary" | Skill(skill="tooluniverse-comparative-genomics") |
| "population genetics", "allele frequency", "HWE", "Fst", "genetic drift" | Skill(skill="tooluniverse-population-genetics") |
| "plant", "Arabidopsis", "crop", "plant pathway", "photosynthesis" | Skill(skill="tooluniverse-plant-genomics") |
| "microbiome", "metagenomics", "gut bacteria", "16S", "MGnify" | Skill(skill="tooluniverse-metagenomics-analysis") |
| "pathogen", "infectious disease", "outbreak", "emerging infection" | Skill(skill="tooluniverse-infectious-disease") |
| "ecology", "biodiversity", "invasive species", "pollinator", "food web", "conservation", "community ecology", "trophic" | Skill(skill="tooluniverse-ecology-biodiversity") |
| "microbiome", "gut microbiota", "dysbiosis", "microbiome composition", "16S rRNA" | Skill(skill="tooluniverse-microbiome-research") |
| "adverse outcome pathway", "AOP", "key event", "molecular initiating event", "KER" | Skill(skill="tooluniverse-adverse-outcome-pathway") |
10. Specialized Biology
| Keywords | Action |
|---|---|
| "lipidomics", "lipid", "sphingolipid", "ceramide", "fatty acid", "LIPID MAPS" | Skill(skill="tooluniverse-lipidomics") |
| "miRNA", "lncRNA", "non-coding RNA", "microRNA", "ncRNA" | Skill(skill="tooluniverse-noncoding-rna") |
| "aging", "senescence", "longevity", "senolytic", "geroprotector" | Skill(skill="tooluniverse-aging-senescence") |
| "vaccine", "epitope prediction", "MHC binding", "immunogenicity", "T-cell epitope" | Skill(skill="tooluniverse-vaccine-design") |
| "stem cell", "iPSC", "organoid", "pluripotency", "differentiation" | Skill(skill="tooluniverse-stem-cell-organoid") |
| "single cell", "scRNA-seq", "cell clustering", "UMAP", "cell type" | Skill(skill="tooluniverse-single-cell") |
| "pharmacogenomics", "PGx", "CPIC", "CYP2D6", "drug-gene", "genotype-guided dosing" | Skill(skill="tooluniverse-pharmacogenomics") |
| "drug mechanism", "mechanism of action", "how does [drug] work", "MOA" | Skill(skill="tooluniverse-drug-mechanism-research") |
| "drug regulatory", "FDA approval", "generic availability", "Orange Book", "patent" | Skill(skill="tooluniverse-drug-regulatory") |
| "gene-disease", "disease genes", "gene association", "genetic basis" | Skill(skill="tooluniverse-gene-disease-association") |
| "toxicology", "AOP", "adverse outcome pathway", "toxin", "BPA" | Skill(skill="tooluniverse-toxicology") |
| "variant to mechanism", "how does variant cause disease", "trace variant" | Skill(skill="tooluniverse-variant-to-mechanism") |
| "regulatory genomics", "enhancer", "promoter", "ENCODE", "cis-regulatory" | Skill(skill="tooluniverse-regulatory-genomics") |
| "KEGG disease", "KEGG drug", "KEGG pathway disease" | Skill(skill="tooluniverse-kegg-disease-drug") |
| "HLA", "MHC", "antigen presentation", "transplant compatibility" | Skill(skill="tooluniverse-hla-immunogenomics") |
| "immunology", "immune response", "cytokine", "antibody-antigen", "autoimmune", "immune signaling" | Skill(skill="tooluniverse-immunology") |
| "neuroscience", "neuron", "brain", "synapse", "neural network", "firing rate", "computational neuroscience", "neuroanatomy", "neurodegeneration", "cranial nerve", "action potential", "connectome" | Skill(skill="tooluniverse-neuroscience") |
11. Problem-Solving & Computation
| Keywords | Action |
|---|---|
| "organic chemistry", "reaction mechanism", "predict product", "NMR interpretation", "IUPAC name", "Diels-Alder", "Grignard", "stereochemistry", "retrosynthesis" | Skill(skill="tooluniverse-organic-chemistry") |
| "inorganic chemistry", "crystal structure", "unit cell", "coordination", "point group", "symmetry", "noble gas compound", "lanthanide", "covalency", "bonding theory", "thermodynamics", "Nernst" | Skill(skill="tooluniverse-inorganic-physical-chemistry") |
| "calculate", "compute", "dosing calculation", "drip rate", "half-life decay", "dilution", "R₀", "herd immunity", "partition function", "pharmacokinetics", "stoichiometry" | Skill(skill="tooluniverse-computational-biophysics") |
| "neural model", "firing rate", "integrate-and-fire", "synaptic dynamics", "network model", "balanced network" | Skill(skill="tooluniverse-neuroscience") |
| "environmental calculation", "contaminant dilution", "bioconcentration", "mass balance", "environmental fate" | Skill(skill="tooluniverse-computational-biophysics") |
12. Infrastructure & Setup
| Keywords | Action |
|---|---|
| "setup", "install", "configure", "API keys", "upgrade", "how to use", "get started", "CLI", "tu command", "MCP vs CLI vs SDK", "what is ToolUniverse", "what can this do", "what databases", "demo", "tutorial", "quickstart", "I'm new" | Skill(skill="setup-tooluniverse") |
| "SDK", "Python SDK", "build AI scientist", "programmatic access", "import tooluniverse", "coding API", "tu build", "typed wrappers" | Skill(skill="tooluniverse-sdk") |
| "install skills", "missing skills", "skill not found", "add skills" | Skill(skill="tooluniverse-install-skills") |
Tie-Breaking Rules
Computation Over Lookup: When a question requires calculation, reasoning, or mechanism prediction, route to the problem-solving skill even if a data-retrieval skill also matches.
- "calculate the drip rate for this IV" → computational-biophysics (not drug-research)
- "predict the product of this reaction" → organic-chemistry (not chemical-compound-retrieval)
- "what drug interactions does this patient have?" → drug-drug-interaction (clinical reasoning)
Domain Over Setup: When "how do I", "help me", "explain", or "what is" co-occurs with a domain entity (drug, gene, protein, disease, variant, pathway name), route to the domain skill, NOT setup.
- "how do I find interactions for TP53?" → protein-interactions
- "help me research metformin" → drug-research
- "what is EGFR?" → target-research
- Only route to setup when NO domain entity present ("how do I use this?")
Specificity Rule: More specific beats general.
- "cancer treatment" → precision-oncology (not disease-research)
Data Type Rule: "get/retrieve/fetch" → retrieval skills.
- "get compound structure" → chemical-compound-retrieval (not drug-research)
Still ambiguous: Ask user with AskUserQuestion.
When to Use General Strategies
Only when no specialized skill matches:
- Meta-questions about ToolUniverse itself (no domain entity)
- Custom workflows combining multiple skills
- User explicitly says "don't use specialized skills"
WARNING: "how do I find interactions for TP53?" is NOT a meta-question — route to protein-interactions.
When using general strategies, load references/general-strategies.md and execute them (run actual queries, don't just describe).
Problem-Solving Mode
Skills are not just tool catalogs — they encode domain expertise and reasoning frameworks. When a question requires reasoning, computation, or clinical judgment (not just data lookup), route to the appropriate problem-solving skill.
When to use Problem-Solving Mode
- Question requires step-by-step calculation (dosing, dilution, decay, stoichiometry) →
tooluniverse-computational-biophysics - Question requires reaction mechanism reasoning (predict products, NMR interpretation, stereochemistry) →
tooluniverse-organic-chemistry - Question requires clinical decision-making (differential diagnosis, drug interactions, treatment selection) → route to the relevant clinical skill
- Question requires data lookup → use Quick Lookup Mode below
Key principle
Think first, then look up. Many scientific problems require reasoning frameworks + computation, not just database queries. Skills should help you SOLVE problems, not just find data.
Bundled Scripts (cross-skill reference)
These scripts are available across skills for quick local computation — invoke them directly when routing to the corresponding skill:
| Script | Skill | Use When | ToolUniverse Tool Alternative (preferred) |
|---|---|---|---|
skills/tooluniverse-computational-biophysics/scripts/iv_drip_rate.py |
computational-biophysics | IV drip rate / dosing calculations | -- |
skills/tooluniverse-computational-biophysics/scripts/herd_immunity.py |
computational-biophysics | R₀, herd immunity threshold | Epidemiology_r0_herd |
skills/tooluniverse-computational-biophysics/scripts/epidemiology.py |
computational-biophysics | Epidemiology calculations | Epidemiology_r0_herd, Epidemiology_vaccine_coverage, Epidemiology_nnt, Epidemiology_diagnostic, Epidemiology_bayesian |
skills/tooluniverse-computational-biophysics/scripts/radioactive_decay.py |
computational-biophysics | Radioactive decay / half-life | -- |
skills/tooluniverse-computational-biophysics/scripts/fluid_calculations.py |
computational-biophysics | Fluid dynamics / flow calculations | -- |
skills/tooluniverse-computational-biophysics/scripts/burn_fluids.py |
computational-biophysics | Burn injury fluid resuscitation | -- |
skills/tooluniverse-computational-biophysics/scripts/enzyme_kinetics.py |
computational-biophysics | Km/Vmax, Hill coefficient, Ki from data | EnzymeKinetics_calculate |
skills/tooluniverse-computational-biophysics/scripts/env_risk_assessment.py |
computational-biophysics | Soil contamination hazard quotient | -- |
skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py |
drug-drug-interaction | CYP substrates, drug interactions, pharmacology constants | -- |
skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py |
rare-disease-diagnosis | HPO pattern matching, differential diagnosis | -- |
skills/tooluniverse-sequence-analysis/scripts/translate_dna.py |
sequence-analysis | DNA → protein translation | DNA_translate_reading_frames |
skills/tooluniverse-sequence-analysis/scripts/amino_acids.py |
sequence-analysis | Amino acid properties lookup | -- |
skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py |
sequence-analysis | GC content, reverse complement, motif scan | Sequence_count_residues, Sequence_gc_content, Sequence_reverse_complement, Sequence_stats |
skills/tooluniverse-sequence-analysis/scripts/biology_facts.py |
sequence-analysis | Genetic code, codon tables, biology constants | -- |
skills/tooluniverse-organic-chemistry/scripts/degrees_of_unsaturation.py |
organic-chemistry | Degrees of unsaturation from formula | DegreesOfUnsaturation_calculate |
skills/tooluniverse-organic-chemistry/scripts/molecular_formula.py |
organic-chemistry | Molecular weight, formula parsing | MolecularFormula_analyze |
skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py |
organic-chemistry | Functional groups, reaction types reference | -- |
skills/tooluniverse-organic-chemistry/scripts/molecular_complexity.py |
organic-chemistry | Böttcher/Bertz molecular complexity | -- |
skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py |
organic-chemistry | Crystal structure density validation | CrystalStructure_validate |
skills/tooluniverse-organic-chemistry/scripts/stereochem_tracker.py |
organic-chemistry | Track R/S through reaction sequences | -- |
skills/tooluniverse-organic-chemistry/scripts/smiles_verifier.py |
organic-chemistry | Verify SMILES: MW, heavy atoms, valence electrons | SMILES_verify |
skills/tooluniverse-population-genetics/scripts/popgen_calculator.py |
population-genetics | HWE, Fst, allele frequency calculations | PopGen_hwe_test, PopGen_fst, PopGen_inbreeding, PopGen_haplotype_count |
skills/tooluniverse-metabolomics/scripts/metabolism_ref.py |
metabolomics | Pathway lookup, 13C tracer, ATP yield | -- |
skills/tooluniverse-variant-analysis/scripts/parse_vcf.py |
variant-analysis | Parse VCF files locally | -- |
Quick Lookup Mode
For factoid questions (short answer expected), don't generate a full research report. Instead:
- Route to the appropriate skill
- Make 1-3 targeted tool calls
- Return the specific answer
Examples:
- "How many cysteine residues in [protein]?" → UniProt sequence lookup → count residues
- "What drug interacts with [gene]?" → ChEMBL/OpenTargets lookup
- "Translate this DNA sequence" → Compute directly using codon table
Key principle: If you're uncertain about a scientific fact, look it up in a database rather than answering from memory.
Routing Examples
Clear match: "comprehensive research report on breast cancer" → Skill(skill="tooluniverse-disease-research", args="breast cancer")
Factoid lookup: "How many cysteine residues in GABAAρ1 TM3-TM4 linker?" → Skill(skill="tooluniverse-sequence-analysis") → UniProt lookup → count
Ambiguous: "Tell me about aspirin" → AskUserQuestion: drug profile, safety, chemical data, or repurposing?
No match: "How can I find all tools related to proteomics?" → General strategies: run find_tools queries
Domain + setup keyword: "help me understand BRCA1 variants" → Skill(skill="tooluniverse-variant-interpretation", args="BRCA1")