# Tooluniverse

> Router for ToolUniverse tasks. Routes to 105+ specialized skills (disease/drug/target research, genomics, proteomics, clinical decision support, etc.) or uses 2300+ scientific tools. Covers tool discovery, multi-hop queries, workflows, and report generation. Use for scientific research, biological data, drug/target/disease relationships, or any biology/medicine/chemistry question. Verifies facts against databases (UniProt, PubMed, ChEMBL, ClinVar, GWAS Catalog).

- Skill: `teng-bio/tooluniverse` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add teng-bio/tooluniverse`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teng-bio/tooluniverse/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: Teng-bio (https://skillmd.com/u/teng-bio)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/teng-bio/tooluniverse

---


# ToolUniverse Router

Route user questions to specialized skills. If no skill matches, use general strategies from [references/general-strategies.md](references/general-strategies.md).

## General Reasoning Protocols

When answering scientific questions:

1. **Look up, don't guess**: Use ToolUniverse tools to verify facts before answering.
2. **Compute, don't estimate**: Write and run Python code (via Bash) for any calculation. Never do mental math.
3. **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.
4. **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

1. **Extract keywords** from user's question
2. **Scan routing table** below for keyword matches
3. **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](references/general-strategies.md))
4. **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...):
1. Read the question CAREFULLY — identify exactly what is being asked
2. Read ALL answer choices before reasoning — don't stop at the first plausible one
3. ELIMINATE clearly wrong options first (usually 2-3 can be ruled out immediately)
4. For the remaining options, reason through each: WHY would this be correct? WHY would this be wrong?
5. If you have two similar-sounding options, look for the KEY DIFFERENCE between them
6. Your final answer MUST be a single letter — nothing else
7. COMMON ERROR: reasoning correctly but reporting the wrong letter. After choosing, re-read your choice letter and verify it matches your reasoning.
8. **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.
9. **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.
10. For scored MC questions, run `mc_analyzer.py` (in `skills/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:
1. For EACH question independently: read the question, identify the domain, invoke the relevant skill
2. NEVER carry assumptions from one question to the next
3. For EVERY MC question: write "Eliminating: [letter] because [reason]" for at least 2 options BEFORE giving your answer
4. For EVERY numerical question: write and execute Python code — report the computed result, not a mental estimate
5. For EVERY "what protein/gene/species" question: search a database FIRST, then answer
6. 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

1. **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)

2. **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?")

2. **Specificity Rule**: More specific beats general.
   - "cancer treatment" → precision-oncology (not disease-research)

3. **Data Type Rule**: "get/retrieve/fetch" → retrieval skills.
   - "get compound structure" → chemical-compound-retrieval (not drug-research)

4. **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](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:
1. Route to the appropriate skill
2. Make 1-3 targeted tool calls
3. 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")`