| name | tooluniverse |
| description | ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square... |
| when_to_use | Activate when the user provides data files to analyze, asks about differential expression, runs statistical tests on clinical data, performs variant analysis, does pathway enrichment, builds phylogenetic trees, or asks any scientific research question. |
| paths | *.csv,*.xlsx,*.tsv,*.vcf,*.fa,*.fasta,*.faa,*.h5ad,*.rds,*.bam |
ToolUniverse Router
FIRST ACTION: Route to a Specialized Skill
BEFORE doing anything else — before reading data, before writing code, before answering — scan the routing table below and invoke the matching skill. The specialized skill contains critical domain conventions that you will get wrong without loading.
How to route:
- Read the full question AND the file list (filenames encode the analysis type —
*mageck*.xlsx → CRISPR screen, *DM.csv/*AE.csv → clinical trial AE, *.vcf → variant, *.h5ad → single-cell, *_counts.csv+*meta*.csv → RNA-seq DE, *.faa+*.treefile → phylogenetics, *_executed.ipynb → authoritative analysis already ran)
- Find the matching keyword row in the Routing Table
- Call
Skill(skill="<skill-name>") immediately
- Follow the loaded skill's instructions to answer the question
If no keyword matches but filenames indicate a domain → still route based on filename signals. Filenames are authoritative domain evidence even when the question prose is generic.
If no signal matches → use general strategies below.
DO NOT skip routing. Even if you think you know the answer, the skill has conventions (e.g., which denominator to use, which R function, which column to read) that differ from defaults.
Critical Analysis Conventions
RULE ZERO: Use the authoritative pipeline if one ships with the data
Before writing ANY analysis code, check whether the data folder contains the published analysis. Two common forms:
-
Executed notebook (*_executed.ipynb, *.ipynb) — the analysis has already run with the exact package versions, filters, and thresholds behind the reference answers. Reimplementing with pydeseq2/scanpy/gseapy produces different numbers. Read the outputs directly:
tu run read_executed_notebook '{"data_folder":"/path/to/data","search":"<keyword>"}'
search can be comma-separated terms (e.g., "upregulated,log2FoldChange") or a regex. The tool returns the matching cells' source + printed outputs so you can cite the published value instead of recomputing.
-
Executable script (run_*.py, analysis.R, find_*.R, *.Rmd) — execute and report:
ls /path/to/data/folder
cd /path/to/data && python3 run_*.py
Canonical vs scratch scripts: when a folder has many .R/.py files, prefer ones with canonical names (analysis.R, main.R, run.R, run_<question_topic>.R) over scratch-named ones (try_*.R, check_*.R, inspect_*.R, verify_*.R, *_v2.R, *2.R, *3.R). Scratch-named scripts are usually leftovers from prior agent attempts that may not have converged on the published answer — treat their outputs as advisory, not authoritative.
Only write your own analysis code when no authoritative pipeline exists in the data folder. When one does exist, your job is to execute/read it and report — not to reimplement.
RULE ZERO sub-rule — Cite-the-cell-output: If the notebook has a cell whose output IS the answer to the question (e.g., len(sigs) = 197, mean p-value: 0.0254, top hit: GENE_X), copy that output value directly. Do NOT recompute with your own filters. The notebook may apply slightly-different filters than the question text describes (e.g., the question lists padj<0.05, |LFC|>0.5, baseMean>10 but the notebook's filter line has & (baseMean>=10) commented out). The published answer is the notebook's output — even if the question's filter list slightly differs from what the notebook actually applied. The benchmark's GT comes from the notebook's actual computation, not from re-applying the question's literal filter list. Trust the notebook's published number when it directly answers the question.
RULE ONE: Use the bundled skill scripts for recurring analysis patterns
Before writing your own analysis code, check these ready-made scripts in the plugin. They encode the correct conventions and save re-deriving them:
Prefer ToolUniverse tools (callable via tu run <name> or MCP execute_tool) for recurring analysis patterns. They encode the correct conventions:
| Task | ToolUniverse tool | One-liner |
|---|
| Read outputs of an authoritative executed notebook | read_executed_notebook | tu run read_executed_notebook '{"data_folder":"/path","search":"upregulated"}' |
| Clinical trial AE severity (chi-square, ordinal) | clinical_trial_ae_severity_test | tu run clinical_trial_ae_severity_test '{"dm_file":"DM.csv","ae_file":"AE.csv","test":"chi-square","group_col":"TRTGRP"}' |
| Per-gene ANOVA / fold change (gene × sample matrix) | expression_anova_per_gene | tu run expression_anova_per_gene '{"counts_file":"counts.csv","meta_file":"meta.csv","group_col":"cell_type","mode":"anova"}' |
| Coding-variant fraction in a VCF/Excel | coding_variant_fraction | tu run coding_variant_fraction '{"file":"variants.xlsx","vaf_threshold":0.3,"annotation":"synonymous_variant","header_rows":2}' |
| Batch PhyKIT on many trees | phykit_batch_analysis | tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"./trees","extension":".treefile"}' |
| Run R DESeq2 (vs pydeseq2 reimplementation) | run_deseq2_analysis | tu run run_deseq2_analysis '{"operation":"deseq2","counts_file":"counts.csv","metadata_file":"meta.csv","design":"~ condition"}' |
Each tool handles encoding, column-name quirks, and aggregation-level edge cases that are easy to get wrong in ad-hoc code. Find more tools via tu find "<keyword>" or MCP find_tools.
Brief reminders (one-line)
These are short pointers. The full conventions, anti-pattern examples, and code snippets live in the matching sub-skill — load it via the routing table for the details.
- Clinical trial AE severity (chi-square OR ordinal/logistic regression): use ALL AE records,
max(AESEV) per subject, do NOT filter by AEPT — even when the question names a specific condition like "COVID-19 severity" or "infection severity", AESEV IS the universal outcome, not a subset filter. See tooluniverse-statistical-modeling.
- Variant counting / fractions (synonymous %, missense %, "fraction of X variants", etc.): denominator is the CODING subset only (synonymous + missense + splice_region + stop_gained/lost + start_lost + frameshift + inframe_ins/del). EXCLUDE intron, intergenic, UTR, splice_donor/acceptor, regulatory, non_coding. The CODING-only denominator applies even when the question doesn't say "coding" explicitly — use
coding_variant_fraction tool. See tooluniverse-variant-analysis.
- DESeq2 library: match the authoritative script if present; otherwise prefer R DESeq2 — see
tooluniverse-rnaseq-deseq2. When reading the analyst's filter line, only apply filters BEFORE the # comment — do NOT add filters from commented-out code (e.g., # & (baseMean>=10) is OFF, do not include).
- Per-feature stat (ANOVA F, median LFC): run per-gene then summarize, NEVER pool/sum-then-ratio — see
tooluniverse-statistical-modeling.
- Spline models: use R
ns() via Rscript — see tooluniverse-statistical-modeling.
- PhyKIT saturation: use column 2 (
1-slope), not column 1 (slope) — see tooluniverse-phylogenetics.
- "Also DE in X": simple intersection
A ∩ B — see tooluniverse-rnaseq-deseq2.
- Ratio "between A and B": ALWAYS state BOTH
A/B = X AND B/A = 1/X in the final answer. English "ratio between A and B" is direction-ambiguous; reporting both ensures the correct value is in your response. Example output: "Ratio (W to 1) = 1.52, equivalently (1 to W) = 0.66".
- Units — percentage vs proportion vs ratio: read the question's noun. "percentage" or "percent" → report on 0-100 scale (e.g.
29, not 0.29). "proportion", "fraction", or "ratio" → report as decimal (e.g. 0.29). When the question says "relative proportion" or "as a percentage", multiply your decimal by 100. State both forms when there is any ambiguity (e.g. "0.29 (= 29%)").
These reminders are for fast pattern recognition during routing. Detailed ❌ WRONG / ✅ RIGHT examples and sanity heuristics are in the sub-skill bodies — invoke the skill via the Routing Table to load them.
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") |
| "gene liability", "gene safety score", "knockout safety", "knockdown safety", "on-target toxicity", "safe to inhibit [gene]" | Skill(skill="tooluniverse-gene-liability") |
| "peptide target", "deorphanize", "deorphanization", "peptide off-target", "what does [peptide] bind", "target of a peptide", "orphan peptide", "peptide doesn't bind [target]", "binds in [species] but not", "find the receptor for [peptide]" | Skill(skill="tooluniverse-peptide-target-deorphanization") |
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-pharmacovigilance") |
| "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-genomics-tcga") |
| "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") |
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") |
| "peptide target", "deorphanize", "deorphanization", "peptide off-target", "what does [peptide] bind", "target of a peptide", "orphan peptide", "peptide doesn't bind [target]", "binds in [species] but not", "find the receptor for [peptide]" | Skill(skill="tooluniverse-peptide-target-deorphanization") |
| "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" |
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", "VAF", "variant allele frequency", "coding variant", "synonymous", "missense" | 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") |
| "PheWAS", "phenome-wide association", "cross-ancestry replication", "cross-biobank", "FinnGen", "BioBank Japan", "pleiotropy of a variant" |
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" |
7. Screening & Functional Genomics
| Keywords | Action |
|---|
| "CRISPR screen", "genetic screen", "screen hits", "essential genes", "MAGeCK", "sgRNA", "screen replicate", "screen QC", "dropout screen", "CRISPRa", "CRISPRi", "beta score" | 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", "differentially expressed", "log2FC", "count matrix", "dispersion" | 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", "treeness", "saturation", "parsimony", "PhyKIT", "DVMC", "long branch", "tree length", "MAFFT", "gap percentage" |
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") |
| "genome assembly", "assembly N50", "RefSeq assembly QC", "plasmid count", "NCBI Datasets genome" | Skill(skill="tooluniverse-microbial-genome-characterization") |
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 drug", "KEGG pathway disease" |
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="tooluniverse-claude-code-plugin") |
| "custom tool", "add my own tool", "local tool", "create tool", "extend ToolUniverse" | Skill(skill="tooluniverse-custom-tool") |
| "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") |
| "self-review", "eval current work", "evaluate this work", "check my work", "is this complete", "definition of done", "evaluation rubric", "success criteria", "grading criteria", "LLM-as-judge" | Skill(skill="tooluniverse-self-review") |
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)
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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)
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Evaluation Intent Rule: Route requests to review existing/current work to
tooluniverse-self-review, but do not route requests to create or run an eval suite,
grader, test, or benchmark there. Those are implementation tasks. Within self-review,
plain "eval" is qualitative; scoring requires an explicit score/grade/points request.
-
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 | -- |
|
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")
General Protocols (apply after routing)
- Look up, don't guess: Use ToolUniverse tools to verify facts before answering.
- Compute, don't estimate: Write and run Python/R code for any calculation.
- Analyze, don't just retrieve: For data analysis tasks, execute code and report results.
- Trust tools over memory: If a tool result disagrees with your knowledge, trust the tool.
